DE102023200068A1 - CONTROL METHOD FOR A DISPLAY DEVICE, DATA PROCESSING DEVICE, COMPUTER PROGRAM AND COMPUTER-READABLE MEDIUM - Google Patents

Abstract

A control method for a communication interface is provided. The control method includes performing a statistical analysis of data sets, each of which includes information relating to a refraction of a human eye, to identify one data set from the data sets as an outlier, generating a representation based on the plurality of data sets, wherein the data set identified as an outlier is marked in the generated representation, and controlling the communication interface such that the communication interface provides the generated representation.

Description

The present disclosure relates to a control method for a communication interface, a data processing device configured to at least partially execute the control method, a computer program comprising instructions that cause the data processing device or a computer to at least partially execute the control method when executing the program, and/or a computer-readable medium comprising instructions that cause the data processing device or a computer to at least partially execute the control method when executing the instructions.

The following discussion of the prior art should not be interpreted as an admission that this prior art is known to a person skilled in the relevant technical field, nor that the prior art discussed is generally known.

The general term “refractive (eye) surgery” refers to eye operations to correct a visual defect or ametropia of a human eye, which change the overall refractive power of the eye and thus replace conventional optical corrections, such as glasses or contact lenses, or at least significantly reduce their required strength.

An axial refractive error occurs when the focal length of the optical system of the eye does not match the length of the eyeball. If the eyeball is too long in relation to the refractive power of the eye, this is called nearsightedness or myopia. If the eyeball is too short, this leads to farsightedness or hyperopia. Astigmatism, corneal curvature or astigmatism occurs when the optical system of the eye has different focal points in different meridians. The extent of ametropia is given in diopters.

The aim of refractive surgery is, as already mentioned above, to adjust the overall refractive power of the eye's optical system so that the environment is sharply imaged on the retina. This can be done by changing the refractive power of the cornea (e.g. laser procedures such as LASIK (laser in situ keratomileusis), PRK (photorefractive keratectomy), LASEK (laser epithelial keratomileusis), EpiLASIK (epithelial laser in situ keratomileusis), or femtosecond lenticule extraction) or by implanting an additional lens or replacing the body's own lens (or a combination of the aforementioned options). The change in the cornea's refractive power occurs by changing its curvature, either by removing tissue (e.g. by a laser procedure) or by making defined incisions that cause a change in shape, essentially due to the intraocular pressure. For short-sighted eyes, flattening, i.e. reducing the refractive power, is necessary, and for long-sighted eyes, splitting, i.e. increasing the refractive power, is necessary. Lens implants (so-called intraocular lenses) are essentially implanted glasses that are selected depending on the correction required. There are implants that are used in addition to the body's own lens (usually in the anterior chamber of the eye), and those that replace the body's healthy lens. However, the correction of the refractive vision defect as part of a cataract operation (i.e. replacing the clouded lens with an implant) is not considered refractive surgery.

After successful application of all these procedures, the focal point of parallel incident light of the non-accommodated optical system of the eye should, if possible, regularly lie on the retina.

In order to achieve a result by means of refractive eye surgery that is as close as possible to the desired result, i.e. in order to keep the deviation from the actual state of the eye after the operation to a target state after the operation as small as possible, so-called nomograms are conventionally used, which graphically prepare data from eye operations that have already been carried out, i.e. empirical values, and allow an evaluation or surgical planning by the surgeon.

According to Mrochen et al. (Mrochen, M., Hafezi, F., Iseli, HP et al. Improvement of refractive results through nomograms. Ophthalmologe 103, 331-339 (2006). https://doi.org/10.1007/s00347-005-1290-7), the predictability of refractive results can be significantly improved by nomograms. These allow a statistical evaluation of surgical treatment data. A nomogram can be defined as a two-dimensional diagram from which a mathematical function or a relationship can be approximately read. A graphic nomogram would usually contain scales on which known values are plotted on the one hand (e.g. desired spherical correction) and on the other hand the result (e.g. achieved spherical correction) can be read. The accuracy with which the function values can be read depends on the accuracy with which individual data points can be read in the diagram and a numerical compensation function can be determined. Nomograms are usually used to compensate for average deviations from a target value. Suitable statistical models can also be used to reduce the scatter of the result. In refractive surgery in particular, models can be created that take possible non-linear relationships into account. For example, not only the linear and quadratic dependencies on the target value of the sphere correction can be determined, but also possible linear or quadratic influences of the additional cylinder correction on the result of the sphere.

In this context, the EP 2 150 223 A1 a method for optimizing a prescription for laser ablation corneal treatment. The method includes receiving a measured correction prescription for a current patient. Next, a database of treatment results from a plurality of previously treated patients is accessed. The database includes a desired correction and an actual correction. A difference between the desired correction and the actual correction represents an over- or under-correction resulting from the procedure. From the difference data, a distribution of data points is calculated depending on the level of correction. From the distribution of data points, a statistically based offset is calculated that applies to the correction prescription to match the actual corrections to the desired corrections. From the data point distribution, a confidence interval of the data with a predetermined confidence level is calculated. The statistically based offset is then adjusted based on the confidence interval to obtain an optimized prescription. The adjusted offset is then output for use in performing a refractive procedure.

For the sake of completeness, it should be mentioned that in addition to the use of graphically prepared data in the form of nomograms, so-called treatment tables can also be used. EP 2 552 370 A1 A method for evaluating a treatment table for use in an ophthalmological refractive surgery for a patient is described.

Against the background of this prior art, an object of the present disclosure can be seen in specifying an improved device and/or an improved method, each of which is suitable for enriching the prior art.

The problem is solved by the features of the independent claim. The subordinate claims and the dependent claims each contain optional further developments of the disclosure.

The object is then achieved by a control method for a communication interface, optionally a display device. The control method comprises carrying out a statistical analysis of data sets, each of which comprises information relating to a refraction of a human eye, in order to identify one data set from the data sets as an outlier. The control method comprises generating a representation based on the plurality of data sets, wherein the data set identified as an outlier is marked in the generated representation. The control method comprises controlling the communication interface so that the communication interface provides the generated representation.

In this description, definitions of terms used in this description are given, but each description represents only one possible specific definition of many possible definitions of the respective term and thus is not intended to limit the scope of the disclosure to that specific definition.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. If a term has multiple definitions, the definitions in this disclosure may prevail unless otherwise specified.

Wherever the terms "for example," "such as," "including," and the like are used, the term "and without limitation" shall follow unless expressly stated otherwise. Similarly, "an example," "exemplary," and the like shall be construed as non-limiting.

The term "substantially" permits variations that do not adversely affect the intended purpose. Descriptive terms should be understood as being modified by the term "substantially", even if the words "substantially" are not expressly mentioned.

The term "approximately", in the context of a numerical value, refers to the actual value given and to the approximation of that value that can reasonably be derived by a person skilled in the art, including approximations based on the experimental and/or measurement conditions for that value.

The terms “comprising” and “including” and “having” and “involving” (and similarly “comprises,” “includes,” “has,” “with,” and “involves”) and the like are used interchangeably and have the same meaning. In particular, each of the terms is defined in accordance with the usual definition of the term “comprising” in patent law and is therefore construed as an open term meaning “at least the following,” i.e., the term “comprising” is to be construed in such a way as not to exclude additional features. For example, “an apparatus comprising components a, b, and c” means that the apparatus comprises at least components a, b, and c. Similarly, the phrase “a method comprising steps a, b, and c” means that the method comprises at least steps a, b, and c.

The control method may be a computer-implemented method, i.e. one, several or all steps of the method can be carried out at least partially by a computer or a data processing device, optionally a control device.

A data set can be understood as a group of data fields that are related in terms of content and belong to an object, in this case information relating to a refraction of a human eye. The data sets can be in a fixed or predetermined structure. In other words, a data set can be understood as summarized electronic data. The data sets can be stored in a database and/or in a file. The data set can be distinguished from data in general, since the data in a data set is present as data group(s) that belong to a specific object, in this case the human eye, and that have essentially identical structures within a data set.

The communication interface can be designed to provide the generated representation or information reproduction visually, aurally and/or haptically. The communication interface can be a human-machine interface (HMI).

“Marked” can be understood as a form of representation of a data point corresponding to the data set identified as an outlier in the (optionally graphical and/or tabular) representation, which differs from a form of representation of those data points that correspond to or belong to the data sets that were not identified as outliers. The marking can be done visually, auditorily and/or haptically.

Information concerning a refraction of the human eye can be understood as any information that characterizes a refractive power of the human eye. This can include measurement results of objective and/or subjective refraction measurements.

The representation can be an electronic file that is created as part of the control method and which, after creation, is present in a predetermined data format or in a predetermined data structure. This representation, present as a data structure, can be output to a control unit that controls the communication interface as part of controlling the communication interface. The representation, present as a data structure, can be designed in such a way that it causes the communication interface to reproduce or provide, optionally display, a representation stored in the data structure. The representation is therefore a data structure that has a technical use, namely controlling a communication interface. The data structure or data format is therefore functional data, i.e. the structure or format has a technical function in a technical system comprising the communication interface. Here, it enables the operation of the communication interface that processes the data to be controlled. In addition to this functional character, the representation also has a cognitive character due to the way in which the information contained in the representation is provided by the communication interface, which also contributes to the technical character of the disclosure, as explained in detail below.

In statistics, an outlier is when a measured value or finding does not fit into an expected series of measurements or does not generally meet expectations. Identifying or determining Such an outlier is identified by means of a statistical analysis of the data sets. In other words, predefined rules are used to determine whether a data set is to be classified as an outlier (or not). The inherently subjective concept of "outlier" is therefore objectified. In other words, automated outlier detection is carried out.

Outlier detection or the identification of the outlier can be implemented using an algorithm. An algorithm can be understood as a clear instruction for solving a problem or a class of problems, in this case a clear instruction for identifying an outlier in the data sets or for determining whether a data set of (several) data sets is an outlier. The algorithm can consist of a finite number of defined or predetermined individual steps. The algorithm can thus be implemented in a computer program for execution. When solving the problem, a specific input, in this case the (entirety of) data sets, is converted into a specific output, in this case which data set is to be classified as an outlier.

The method is not limited to identifying a single outlier. Rather, several or all data sets can be identified as outliers and classified as such according to the statistical analysis. Furthermore, according to the disclosure, it can also happen that none of the data sets can be classified as outliers according to the statistical analysis.

The control method described above offers a number of technical advantages.

By marking or highlighting the outlier in the data provided via the communication interface, i.e. by the cognitive content of the (optionally graphical and/or tabular) representation, a constant or guided human-machine interaction can be realized in the area of surgical planning for refractive eye surgery by reproducing, optionally displaying, the information contained in the data structure for a user.

In other words, not only the fact that the representation is made, but also how it is made, contributes to the technical character of the process according to the disclosure.

The representation can be a graphical representation, optionally including a nomogram. Such nomograms are used by surgeons to plan refractive eye surgery (see above). The planning involves specifying various settings for a surgical device used to perform the refractive eye surgery, such as a femtosecond laser. The result achieved by the eye surgery varies depending on the settings of the surgical device used.

The nomogram regularly contains a regression curve that is generated based on the individual data points contained in the nomogram diagram.

If nomograms for computer-assisted surgical planning are generated automatically, the software developer is faced with the challenge of defining general rules based on which data sets of a large number of data sets a nomogram requested by the user or surgical planner should be generated.

This means that, without knowing the actual data sets, it must be decided in advance under which conditions a data set should and should not be taken into account when creating the desired nomogram.

Traditionally, this challenge is avoided by not automatically selecting data sets, but rather by the surgery planner manually selecting or deselecting data sets and then generating the nomogram based on the desired data sets. However, this is disadvantageous or has its limitations in many respects. For example, a desired sphere correction can be displayed in a nomogram above a sphere correction actually achieved. In a two-dimensional visualization, the data points can all lie within a certain interval along a best fit line, so that no outliers can be identified by a surgery planner in this view. However, it is conceivable that there are 99 women and one man among the data sets, so that statistically the man would be considered an outlier. However, this is not recognizable to the surgery planner due to the limited information content of the nomogram. To avoid this problem, data sets can be excluded from the nomogram creation from the outset. However, this can lead to potentially relevant data sets not being taken into account, since, for example, in the case outlined, no difference at all can be seen between male and female patients. In this case, the full database is not used. The fact that the full database is not used is not apparent to the operation planner from the individual nomogram generated.

In other words, the software developer is faced with the technical task of visualizing a three-dimensional content in a two-dimensional diagram, namely all data points with their X-value and Y-value and, as a third dimension, whether the respective data set underlying the data point was or should be taken into account in the (final) nomogram formation according to the statistical analysis. This technical task is complicated by the fact that the proposed solution must be independent of the type of X-value and Y-value displayed, e.g. sphere, gender, age, etc., i.e. it must be clear from the display which data sets are or were taken into account, regardless of what is visualized.

From a technical point of view, this is solved on the one hand by no longer considering data points as is conventional, but rather by considering data sets, so that the automated determination of outliers is separated from the actual graphical representation, but on the other hand those data sets identified as outliers are marked in the graphical representation, e.g. in color, so that the information resulting from the upstream data processing step, which is actually not apparent to the operation planner, becomes visible to the operation planner.

Possible further developments of the tax procedure described above are explained in detail below.

The communication interface can include or consist of a display and/or input device. The display can optionally be graphical and/or tabular.

The data sets can each comprise, as the information relating to the refraction of the human eye, a result of an objective refraction measurement and an associated result of a subjective refraction measurement (of the human eye). Carrying out the statistical analysis of the data sets can comprise using the results of the objective refraction measurement and the associated result of the subjective refraction measurement.

In optometry and ophthalmology, the term axial refraction refers to the refractive power of the optical correction with which a certain eye, without accommodation, produces a sharp image of an object at an infinite distance. If this value is zero, it is called normal vision or emmetropia, otherwise it is called ametropia. A distinction must be made between the result of an objective refraction measurement or an objective refraction, which is measured using an apparatus and determined by the refractive properties of the eyeball, and the result of a subjective refraction measurement or a subjective refraction, which requires the person being examined to provide information about the perceived image sharpness and takes into account the influence of other variables on perception.

Performing the statistical analysis may include determining a difference between the result of the objective refraction measurement and the corresponding result of the subjective refraction measurement for all data sets. Those data sets whose difference is outside a confidence interval with a predetermined confidence level may be identified as outliers.

In statistics, the confidence interval, abbreviated CI, (also called confidence interval, confidence range or expected range) can be understood as an interval that is intended to indicate the precision of the position estimate of a parameter. The confidence interval indicates the range that includes the parameter of a distribution of a random variable with a certain probability (the coverage probability). The confidence level defines this probability.

The method may include setting the predetermined confidence level based on a first user input.

It is therefore conceivable that the control of the communication interface, optionally the display device, includes the provision of a graphical user interface which makes it possible to involve the user, optionally the operation planner, in the process of automated generation of the nomograms through his user input.

The data sets can each comprise, as the information relating to the refraction of the human eye, a correction value of the eye achieved by means of a refractive eye operation and an associated quantity that characterizes a setting of a device performing the refractive eye operation. Carrying out the statistical analysis of the data sets can comprise using the quantity that characterizes the setting of the device performing the refractive eye operation and the respective associated correction value.

The disclosure is not limited to a variable that characterizes the setting of the device performing the refractive eye surgery, but the variable can, additionally or alternatively, be a patient age and/or a therapy history of the patient on whom the refractive eye surgery is to be performed.

The quantity that characterizes a setting of a device performing refractive eye surgery can be a laser setting. This can, for example, characterize a refractive correction in diopters that can be set on the laser device (e.g. sphere and/or cylinder, and optionally higher orders to correct astigmatism). Depending on the device (e.g. femtosecond laser or excimer laser) and procedure (e.g. PRK, LASIK, or SMILE), different scan patterns with different device parameters can optionally be used and can be set to achieve this correction. When it comes to nomograms for the SMILE procedure, for example, the radius of curvature of the posterior lenticule cut can also be decisive.

Performing the statistical analysis may include assigning a plurality of the data sets to a group of data sets based on the respective quantity characterizing the setting of the device performing the refractive eye surgery. Identifying outliers from the group of data sets may be done based on their respective correction values using a leave-one-out kernel density estimation.

Kernel density estimation (also known as the Parzen window method; English kernel density estimation, KDE) is a statistical method for estimating the probability distribution of a random variable. The kernel is the continuous Lebesgue density k of an almost arbitrarily chosen probability measure K. Possible kernels are the Gaussian kernel or the Cauchy kernel. Details on kernel density estimation can be found at https://de.wikipedia.org/wiki/Kerndichtesch%C3%A4tzer.

Identifying outliers from the group of data sets based on their respective correction values using the leave-one-out kernel density estimation for each data set from the group of data sets can include determining a probability distribution taking into account the correction values of all data sets from the group of data sets except for the achieved correction value of the data set for which it is determined whether it is an outlier, by means of a kernel density estimation. It can also include determining a probability value for the correction value of the data set for which it is determined whether it is an outlier, based on the determined probability distribution. It can also include identifying the data set for which it is determined whether it is an outlier as an outlier if its determined probability value is below a predetermined probability threshold.

The method includes setting the predetermined probability threshold based on a second user input. What is described above for the graphical user interface applies here mutatis mutandis.

Those records of the records may be assigned to the group of records that have the same size that characterizes the setting of the device performing the refractive eye surgery.

In other words, the data sets can be grouped in such a way that those data sets are summarized and evaluated together in which the same setting of the eye surgery device performing the correction, e.g. a laser. It can be assumed, for example, that these data sets should achieve the same or similar correction result. In this way, the outliers can be filtered out of the respective groups.

The method can include receiving a third user input relating to at least one data set identified as an outlier, with which the identification of this data set as an outlier is cancelled. In concrete terms, this can mean that the user, optionally the operations planner, can decide whether the respective data set should actually be classified as an outlier. To do this, the user can, for example, click on the data point corresponding to the data set in the diagram or nomogram. The same can also be done the other way round, i.e. additionally or alternatively, the method can include receiving a fourth user input relating to at least one data set identified as a non-outlier, with which the identification of this data set as a non-outlier is cancelled or this data set is manually marked as an outlier. What was described above for the graphical user interface applies here mutatis mutandis.

The method may include determining a first best fit curve based solely on the data sets of the data sets that were not identified as outliers and inserting the determined best fit curve into the representation.

The best fit curve can optionally be created initially based on the data sets automatically identified as non-outliers in the first step and then optionally updated if the user manually marks a data point as a non-outlier using the user input described above.

The method may include controlling a device configured to perform a refractive eye operation based on the generated representation. In other words, the method may include controlling a device configured to perform a refractive eye operation based on the data set corrected for the at least one outlier.

In other words, the refractive eye surgery can be controlled and thus at least partially carried out automatically, which was planned based on the representation generated by the method described above or the data set adjusted for outliers.

Furthermore, a device for data processing is provided, comprising means for carrying out the control method described above.

The data processing device can be a computer, e.g. a personal computer (PC), a smartphone, a tablet, a server and/or a cloud component.

The device for data processing can have a processor and a memory connected to the processor, in which the method in the form of a computer program and the data sets are stored. The processor can be designed to access the computer program and the data sets and to carry out the method described above. The term "have" here is not to be interpreted as if the memory and the processor actually have to be physically installed in a device. This is only one possible implementation, although many other possible implementations, such as the use of a cloud to store the computer program and/or the data sets, are also conceivable in addition or as an alternative.

What is described above with reference to the control method also applies analogously to the data processing device and vice versa.

Furthermore, a computer program is provided, comprising instructions which, when the program is executed by a computer, cause the computer to at least partially carry out or implement the control method described above.

A program code of the computer program can be in any code, in particular in a code that is suitable for controlling computers, such as personal computers, tablets, smartphones and/or servers.

What is described above with reference to the control method and the data processing device also applies analogously to the computer program and vice versa.

Furthermore, a computer-readable medium, in particular a computer-readable storage medium, is provided. The computer-readable medium comprises instructions which, when executed by a computer, cause the computer to at least partially carry out the control method described above.

That is, a computer-readable medium may be provided that comprises a computer program as defined above. The computer-readable medium may be any digital data storage device, such as a USB stick, a hard disk, a CD-ROM, an SD card or an SSD card.

The computer program does not necessarily have to be stored on such a computer-readable storage medium in order to be made available to the computer, but can also be obtained via the Internet or from another external source. It can therefore also be a data signal that includes commands that, when executed by a computer, cause the computer to at least partially carry out the control method described above.

The above description with reference to the control method, the data processing device and the computer program also applies analogously to the computer-readable medium and vice versa.

• 1 zeigt schematisch ein Ablaufdiagramm eines offenbarungsgemäßen Steuerverfahrens für eine Anzeigevorrichtung, und
• 2 zeigt schematisch eine Anzeigevorrichtung, die mittels des Steuerverfahrens gesteuert wird,
• 3 zeigt schematisch eine Vorrichtung bzw. ein System zur Datenverarbeitung aufweisend die Anzeigevorrichtung aus 1, die bzw. das ausgestaltet ist, um das Steuerverfahren auszuführen,
• 4 zeigt zu Erklärungszwecken ein Diagramm mit einer Wahrscheinlichkeitsdichteverteilung von Differenzwerten aus subjektiver und objektiver präoperativer Refraktion,
• 5 zeigt zu Erklärungszwecken ein beispielhaftes Diagramm, in dem Korrekturwerte (vertikale Achse in 5) über Lasereinstellungen (horizontale Achse in 5) aufgetragen sind,
• 6 zeigt zu Erklärungszwecken ein beispielhaftes Balkendiagramm, wobei auf der horizontalen Achse ein erzielter Korrekturwert und auf der vertikalen Achse eine Anzahl von Datensätzen, in denen der jeweilige Korrekturwert erzielt wurde, aufgetragen ist,
• 7 zeigt beispielhaft eine Leave-One-Out-Kern-Dichte-Schätzung für die gleiche Teilmenge bzw. Gruppe wie in 6.

Below, an exemplary embodiment is described with reference to 1 to 7 described.

• 1 shows schematically a flow chart of a control method for a display device according to the disclosure, and
• 2 shows schematically a display device controlled by means of the control method,
• 3 shows schematically a device or a system for data processing comprising the display device of 1 , which is designed to carry out the tax procedure,
• 4 shows for explanatory purposes a diagram with a probability density distribution of difference values from subjective and objective preoperative refraction,
• 5 shows an example diagram for explanatory purposes in which correction values (vertical axis in 5 ) via laser settings (horizontal axis in 5 ) are applied,
• 6 shows an example bar chart for explanatory purposes, with the horizontal axis showing an achieved correction value and the vertical axis showing a number of data sets in which the respective correction value was achieved,
• 7 shows an example of a leave-one-out kernel density estimate for the same subset or group as in 6 .

At least one detailed embodiment is described below, wherein the described embodiment is merely exemplary of the disclosure that may be embodied in various forms. The figures are not necessarily to scale, and some features may be exaggerated to show details of particular components. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a basis for the claims, and as a representative basis for teaching one skilled in the art how to variously employ the present disclosure.

In the following, the embodiment according to the disclosure is initially described generally with reference to the 1 to 3 described.

This in 1 The control method shown essentially comprises six steps S1 - S6. The 3 The device for data processing 1 shown has a unique position in 2 shown, having a display, a control device 3 connected to the display device 2 and having a processor (not shown), and a memory 8 (eg a RAM) connected to the control device 3. The display device 2 is only one example of a communication interface, which can additionally or alternatively have a loudspeaker for the auditory output of information and/or a vibration unit for the output of haptic information. The same applies mutatis mutandis to the visual output, ie the display, of information described below.

In a first step S1 of the control method, the control device 3 loads a plurality of data records stored in a database in the memory 8 together with a computer program into the control device 3, wherein the computer program comprises instructions which enable the control device 3 to carry out the control method described in further detail below.

In a second step S2 of the control method, the control device 3 controls the display device 2 acting as a human-machine interface so that it displays a graphical user interface (GUI) 5. The data processing device 1 has input devices (not shown) (e.g. a computer mouse, a keyboard and/or a touch display as part of the display device) so that a user, in this case an operation planner for a refractive eye operation, can enter requested information about the data processing device 1 via the user interface 5. In the present case, in the first step S1 of the control method, two user inputs are initially requested, namely a confidence level, which can be entered in a first field 51 of the user interface 5, and a probability limit, which can be entered in a second field 52 of the user interface 5.

In a second step S2 of the control method, the control device 3 carries out a statistical analysis of the data sets, each of which comprises information relating to a refraction of a human eye, in order to identify one or more outliers 42 in the data sets.

In order to identify the outliers, two options, which can be combined with one another, are fundamentally possible. The first of the two options is based on a comparison of an objective and a subjective refraction. The second option is based on an evaluation of a vision correction achieved using essentially the same laser settings. Both options are explained in detail below. Of course, these are only two of a multitude of possible concrete designs or implementations of the principle according to the disclosure and the disclosure is therefore not limited to these two options. In general, however, it can be stated that the principle according to the disclosure allows various types of outliers in refractive surgery data to be automatically identified where a relationship between one or more parameters deviates significantly from the rest of the data.

According to the first possibility, the data sets containing information relating to the refraction of the human eye each comprise a result of an objective refraction measurement and a corresponding result of a subjective refraction measurement. This can be a subjective or objective refraction measurement carried out preoperatively and/or a subjective or objective refraction measurement carried out postoperatively. The refraction measurement can refer to both a sphere and a cylinder (value) of the eye to be treated or treated. The sphere value provides information about the refractive power of the eye in the true sense, i.e. the position of the focal point in relation to the retina of the eye. The cylinder value, optionally together with the axis value, provides information about the strength of astigmatism. Carrying out the statistical analysis of the data sets involves using the results of the objective refraction measurement and the corresponding result of the subjective refraction measurement. More specifically, performing the statistical analysis comprises determining a difference between the result of the objective refraction measurement and the corresponding result of the subjective refraction measurement for all data sets, and identifying as outliers those data sets whose difference is outside a confidence interval with the confidence level received in the first step S1 of the method and set by the operation planner.

This approach is in 4 illustrated for explanatory purposes with a confidence level of 95%. 4 The diagram shown is a probability density function, where for all data sets the difference (e.g. in diopters) between the subjective preoperative sphere and the objective preoperative sphere was calculated and the obtained differences are plotted on the horizontal axis and the corresponding probability density is plotted on the vertical axis.

The first option assumes that the difference between the subjective and objective refraction measurement should be limited to a certain range, the so-called confidence interval, the size of which is determined by the confidence level (here 95%). The confidence level allows the determination of a lower and an upper threshold (e.g. for the lower threshold (100% - confidence level)/2; and for the upper threshold: 100% - lower threshold). In the present example with a confidence level of 95%, the lower threshold is the 2.5th percentile of the distribution, while the upper threshold is the 97.5th percentile of the distribution.

In the 4 The example shown shows that a difference that lies in a range of [-0.81, 0.63] is within the confidence interval. Data sets whose difference lies within this range are not to be considered as outliers. Data sets whose difference lies outside this range, however, are to be considered or identified as outliers.

$$\begin{array}{l}\text{Erzielter Korrekturwert Zylinder}=\text{pr}\ddot{\text{a}}\text{operativer Zylinderwert}-\text{postoperativer}\\ \text{Zylinderwert}\end{array}$$

According to the second possibility, the data sets containing the information relating to the refraction of the human eye each comprise a correction value of the eye achieved by means of a refractive eye operation and an associated value that characterizes a setting of a device that performs the refractive eye operation. The correction value achieved can be a difference between the preoperative and postoperative measured values (e.g. in diopters) described above with reference to the first possibility, ie it can be a difference between a result of a postoperatively performed or postoperative subjective and/or objective refraction measurement and a preoperative subjective and/or objective refraction measurement. To be more precise, the correction value achieved can be calculated for each of the data sets as follows: $$\begin{array}{l}\text{Achieved correction value Sph}\ddot{\text{a}}\text{re}=\text{pr}\ddot{\text{a}}\text{operative Sph}\ddot{\text{a}}\text{renworth}-\text{postoperative}\\ \text{Sph}\ddot{\text{a}}\text{renworth}\end{array}$$

$$\begin{array}{l}\text{Achieved correction value cylinder}=\text{pr}\ddot{\text{a}}\text{operative cylinder value}-\text{postoperative}\\ \text{Cylinder value}\end{array}$$

The quantity can be, for example, a laser setting (e.g. in diopters) of a laser used for refractive eye surgery. Carrying out the statistical analysis of the data sets involves using the quantity that characterizes the setting of the device performing the refractive eye surgery and the corresponding correction value.

Laser setting can be understood as those parameters of a refractive laser system that must necessarily be set in order to achieve a well-defined or predefined tissue ablation in the cornea (e.g. wavelength, spot spacing, numerical aperture, temporal-spatial variation of the energy fluence, etc.), which leads to a change in the refractive power of the cornea and thus of the eye. In the case of a pulsed laser, pulse frequency and pulse energy can be parameters or laser settings that must be set.

It is assumed that when using the same laser setting, the vision correction achieved in different patients should be very similar. 5 The scatter plot shown shows the relationship between the laser setting and the achieved vision correction or the achieved correction value. As can be seen from 5 As can be seen, most samples show similar achieved corrections for each laser setting, while the samples that deviate significantly from the mean group can be identified as outliers, as explained in detail below. It is conceivable that the laser vision correction for sphere and cylinder are analyzed separately.

More precisely, several of the data sets are first assigned to a group of data sets in which the respective value that characterizes the setting of the device performing the refractive eye surgery, i.e. in this case the laser setting, is the same. A result resulting from this is shown as a bar chart for a group of data sets with the same laser setting, for example in 6 where the horizontal axis represents an achieved correction value and the vertical axis represents a number of data sets in which the respective correction value was achieved.

In other words, to detect the outliers in this approach, the data is first divided into subsets, with each subset being for a sphere or cylinder laser setting. The achieved correction is expected to be similar for each subset because the same laser setting was used. The distribution of the achieved correction in a subset can be represented as a histogram, as in 6 shown.

Subsequently, outliers from the respective group of data sets are identified based on their respective correction values using a leave-one-out kernel density estimation.

For this purpose, a probability (density) distribution is first calculated using a kernel density estimation, taking into account the correction values of all data sets from the group of data sets except for the correction value achieved for the data set for which it is determined whether it is an outlier. Then, based on the determined probability distribution, a probability value is determined for the correction value of the data set for which it is determined whether it is an outlier. Finally, the data set for which it is determined whether it is an outlier is identified as an outlier if its determined probability value is below the probability threshold determined using the second user input (see above).

In other words, the distribution of the achieved correction in a subset or group of data sets can be modelled using the kernel density estimation, for example, according to the following formula, where K is a Gaussian kernel: $${\rho }_K\left(x\right)={\displaystyle \sum _{i=1}^{N}K\left(\frac{x-x_i}{H}\right)}$$

It is proposed to use leave-one-out kernel density estimation to identify outliers in each subset. To evaluate the probability density of a data sample, the distribution function ρ _K (x) is estimated based on the remaining data. The leave-one-out strategy avoids the influence of the selected data sample on the estimated distribution function. This process is repeated until all data samples in the subset have been evaluated. A normal data sample, i.e. not an outlier, should have a higher probability density than the threshold ρ ₀ of the second user input: $${\rho }_K\left(x_0\right)>{\mathrm{<figure-callout\; id="0"\; label="p"\; filenames="DE102023200068A1\_0007.png"\; state="\{\{state\}\}">p</figure-callout>}}_0$$

In 7 is an example of a leave-one-out kernel density estimate for the same subset or group as in 6 shown. The left figure in 7 shows the estimated probability density for a data set whose achieved correction value is -8. Assuming that the ρ ₀ = 0.1, this data set is identified as an outlier because ρ _K (-8) = 0.1. In the right figure in 7 a leave-one-out kernel density estimation is performed for a data set with an achieved correction of -5.75; this sample is considered normal, i.e. not an outlier, taking into account ρ _K (-5.75) > 0.1.

In a third step S3 of the control method, the control device 3 generates a graphical representation 4 based on the multiple data sets, wherein the data set identified as an outlier 42 is marked in the generated graphical representation or nomogram 4. Each data set is shown in the graphical representation 4 as a data point 41 in a diagram. The user can determine through a user input which data should be plotted against each other in the nomogram 4. This can, for example, be an achieved correction value above a desired correction value. Regardless of which of the two options is used to identify the outlier 42, a data point 42 will stand out from the other data points 41 by marking it, e.g. in color, which belongs to a data set that was identified as an outlier 42. The data of this data set identified as an outlier 42 are not taken into account in a calculation of a regression curve, e.g. a regression line, carried out in the third step S3. This calculated compensation curve 6 is also inserted into the nomogram 4 (see 2 ).

In a fourth step S4 of the control method, the control device 3 controls the display device 2 such that the display device 2 displays the generated graphic representation 4, i.e. visually reproduces it for a user.

In a fifth step S5 of the control method, in response to the displayed nomogram or graphical representation 4, a third user input is received relating to the at least one data set identified as an outlier 42 in the nomogram 4, with which the identification of this data set as an outlier is cancelled. To do this, the user or operation planner can, for example, click on the data point 42 of the at least one data set identified as an outlier 42. The outlier 42 is then taken into account when calculating the compensation curve 7, so that a different compensation curve 7 results, as is shown by way of example in 2 In other words, the graphical representation or nomogram 4 is updated according to the third user input.

In a sixth step S6 of the method, a device designed to perform a refractive eye operation can be controlled based on the nomogram 4 created according to the steps S1 - S5 described above. This can be done, for example, by the control device 3.

List of reference symbols

1

Device for data processing

2

Communication interface, optional display and/or input device

3

Control device

4

graphical representation, optional nomogram

41

Data points

42

Runaway

5

Graphical user interface

51

Field for entering a confidence level

52

Field for entering a probability threshold

6

Regression curve without taking the outlier into account

7

Adjustment curve taking the outlier into account

8th

(Data) storage

S1 - S6

Process steps

QUOTES INCLUDED IN THE DESCRIPTION

This list of documents listed by the applicant was generated automatically and is included solely for the better information of the reader. The list is not part of the German patent or utility model application. The DPMA accepts no liability for any errors or omissions.

Cited patent literature

• EP 2150223 A1 [0009]
• EP 2552370 A1 [0010]

Claims (15)

Control method for a communication interface (2), characterized in that the control method comprises: - performing a statistical analysis of data sets, each comprising information relating to a refraction of a human eye, to identify one data set from the data sets as an outlier (42), - generating a representation (4) based on the plurality of data sets, wherein the data set identified as an outlier (42) is marked in the generated representation (4), and - controlling the communication interface (2) so that the communication interface (2) provides the generated representation (4). Tax procedure according to Claim 1 , characterized in that: - the communication interface (2) comprises or consists of a display and/or input device, - wherein the representation (4) is optionally graphical and/or tabular by means of the display device. Tax procedure according to Claim 1 or 2 , characterized in that: - the data sets comprise, as the information relating to the refraction of the human eye, in each case a result of an objective refraction measurement and an associated result of a subjective refraction measurement, and - carrying out the statistical analysis of the data sets comprises using the results of the objective refraction measurement and the respectively associated result of the subjective refraction measurement. Tax procedure according to Claim 3 , characterized in that carrying out the statistical analysis comprises: - determining a difference between the result of the objective refraction measurement and the corresponding result of the subjective refraction measurement for all data sets, and - identifying those data sets as outliers (42) whose difference is outside a confidence interval with a predetermined confidence level. Tax procedure according to Claim 4 , characterized in that the method comprises setting the predetermined confidence level based on a first user input. Tax procedure according to one of the Claims 1 until 5 , characterized in that: - the data sets, as the information relating to the refraction of the human eye, each comprise a correction value of the eye achieved by means of a refractive eye operation and an associated quantity which characterizes a setting of a device performing the refractive eye operation, and - carrying out the statistical analysis of the data sets comprises using the quantity which characterizes the setting of the device performing the refractive eye operation and the respectively associated correction value. Tax procedure according to Claim 6 , characterized in that performing the statistical analysis comprises: - assigning a plurality of the data sets to a group of data sets based on the respective quantity characterizing the setting of the device performing the refractive eye surgery, and - identifying the outlier (42) from the group of data sets based on their respective correction values using a leave-one-out kernel density estimate. Tax procedure according to Claim 7 , characterized in that identifying the outlier (42) from the group of data sets based on their respective correction values using the leave-one-out kernel density estimation for each data set from the group of data sets comprises: - determining a probability distribution taking into account the correction values of all data sets from the group of data sets except for the achieved correction value of the data set for which it is determined whether it is the outlier (42), by means of a kernel density estimation, - determining a probability value for the correction value of the data set for which it is determined whether it is the outlier (42), based on the determined probability distribution, and - identifying the data set for which it is determined whether it is the outlier (42) as an outlier (42) if its determined probability value is below a predetermined probability threshold. Tax procedure according to Claim 8 , characterized in that the method comprises setting the predetermined probability threshold based on a second user input. Tax procedure according to one of the Claims 6 until 9 , characterized in that those data sets of the data sets are assigned to the group of data sets which have the same size that characterizes the setting of the device performing the refractive eye surgery. Tax procedure according to one of the Claims 1 until 10 , characterized in that the method comprises receiving a third user input concerning the data set identified as an outlier (42), with which the identification as an outlier (42) of this data set is cancelled. Tax procedure according to one of the Claims 1 until 11 , characterized in that the method comprises: - determining a first adjustment curve (6) based exclusively on the data sets of the data sets that were not identified as outliers (42), and - inserting the determined adjustment curve (6) into the graphical representation (4). Tax procedure according to one of the Claims 1 until 12 , characterized in that the method comprises controlling a device configured to perform a refractive eye operation based on the generated representation. Device for data processing (1), comprising means for carrying out the control method according to one of the Claims 1 until 13 . Computer program and/or computer-readable medium comprising instructions which, when the program or instructions are executed by a computer, cause the computer to carry out the control method according to one of the Claims 1 until 13 to execute.

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