Classifications

• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B3/00—Apparatus for testing the eyes; Instruments for examining the eyes
  • A61B3/0016—Operational features thereof
  • A61B3/0025—Operational features thereof characterised by electronic signal processing, e.g. eye models
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
  • G06N20/00—Machine learning
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
  • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
  • G16H20/40—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
  • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
  • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
  • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
  • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61F—FILTERS IMPLANTABLE INTO BLOOD VESSELS; PROSTHESES; DEVICES PROVIDING PATENCY TO, OR PREVENTING COLLAPSING OF, TUBULAR STRUCTURES OF THE BODY, e.g. STENTS; ORTHOPAEDIC, NURSING OR CONTRACEPTIVE DEVICES; FOMENTATION; TREATMENT OR PROTECTION OF EYES OR EARS; BANDAGES, DRESSINGS OR ABSORBENT PADS; FIRST-AID KITS
  • A61F2/00—Filters implantable into blood vessels; Prostheses, i.e. artificial substitutes or replacements for parts of the body; Appliances for connecting them with the body; Devices providing patency to, or preventing collapsing of, tubular structures of the body, e.g. stents
  • A61F2/02—Prostheses implantable into the body
  • A61F2/14—Eye parts, e.g. lenses, corneal implants; Implanting instruments specially adapted therefor; Artificial eyes
  • A61F2/16—Intraocular lenses

Definitions

• the invention relates to determining the refractive power of an intraocular lens and in particular to a computer-implemented method for determining the refractive power of an intraocular lens to be used using a learning model with a special loss function, a corresponding system and a corresponding computer program product for carrying out the method.
• IOL intraocular lens
• the biological lens is detached and removed from the capsular bag in a minimally invasive procedure.
• the clouded lens is then replaced with an artificial lens implant.
• This artificial lens implant or intraocular lens is inserted into the then empty capsular bag. Knowledge of the correct position of the intraocular lens and the required refractive power are mutually dependent.
• an underlying task for the concept presented here is to specify a method and a system for improved IOL refractive power predictions for an intraocular lens.
• a computer-implemented method for determining the refractive power of an intraocular lens to be inserted is presented.
• the method can in particular include providing a physical model for determining the refractive power of an intraocular lens and training a machine learning system with clinical ophthalmological training data and associated desired results to form a learning model for determining the refractive power.
• a loss function used for training can have two components: a first component of the loss function can take into account the corresponding clinical ophthalmological training data and associated and desired results, and a second component of the loss function can take into account limitations of the physical model in that a loss function component value of this second component becomes larger the farther a predicted value of refractive power during training deviates from results of the physical model with the same clinical ophthalmologic training data as input values.
• the method can also include providing ophthalmological data of a patient and predicting the refractive power of the intraocular lens to be inserted using the trained machine learning system, with the ophthalmological data provided being able to be used as input data for the machine learning system.
• a system for determining the refractive power of an intraocular lens to be used is presented.
• the system can have a provisioning module in which a physical model for a refractive power determination for an intraocular lens is stored, and a training module that is adapted to train a machine learning system with clinical ophthalmological training data and associated desired results to form a learning model for the refractive power determination .
• parameter values of the learning model can be stored in the learning system.
• a loss function used for training can have two components in particular: a first component of the loss function can take into account corresponding clinical ophthalmological training data and associated and desired results, and a second component of the loss function can thereby take into account limitations of the physical model that a loss function component value of this second component becomes larger the farther a predicted value of the refractive power during training deviates from results of the physical model with the same clinical ophthalmological training data as input values.
• the system can also have a memory for a patient's ophthalmological data and a prediction unit that is adapted to predict the refractive power of the intraocular lens to be used by means of the trained machine learning system, the ophthalmological data provided being used as input data for the machine learning system.
• embodiments may relate to a computer program product, accessible from a computer-usable or computer-readable medium, having program code for use by, or in connection with, a computer or other instruction processing system.
• a computer-usable or computer-readable medium can be any Be a device that is suitable for storing, communicating, forwarding or transporting the program code.
• the computer-implemented method for determining the refractive power for an intraocular lens to be used has several advantages and technical effects that can also apply accordingly to the associated system:
• a machine learning system for determining the refractive power for an intraocular lens to be used which is based exclusively on available clinical ophthalmological data based, would on the one hand require a comparatively long training time, and on the other hand known properties of physical models could not be taken into account so elegantly.
• clinical training data were used exclusively, a large number of data points - i.e. training data - would be required in order to guarantee a large number of anatomical variability.
• the entire parameter space can be systematically sampled using physical models.
• the method presented here uses the best of both worlds: on the one hand the world of physical-mathematical models and on the other hand also the world of clinical ophthalmological data.
• the machine learning model can be additionally pre-trained before being trained with clinical ophthalmologic data.
• automatically generated training data can be generated using a physical model. This physical model does not necessarily have to be the same one that influences the loss function. In this way, influences from different physical models can be taken into account during training.
• the method presented here has a decisive influence on the robustness of the training, both for the untrained case and for the case of a pre-trained system.
• the physical constraint in the loss function ensures that the system cannot learn any physically inconsistent predictions while it is being trained on the real data.
• the influence of outliers in the data set is intercepted and the trained entire network can provide a more stable prediction.
• control over the loss function can mean that no "catastrophic forgetting" can set in, i.e. that the previously learned knowledge cannot simply be overwritten by training on the ophthalmological data.
• the physical constraint in the loss function can force the network to continue to take physical constraints and boundaries into account.
• the physical constraint in the loss function itself can cover the entire parameter space. It can provide the correct physical solution for every conceivable data point and can thus enable the entire parameter range to be represented systematically. This is a decisive advantage for a training process compared to the traditional method, since under normal circumstances only a small part of the parameter space can be represented by the existing real data. This can also always be faulty. All of this can be offset by the physical constraint. It therefore represents a decisive extension and improvement of the training process.
• the correct weighting of the components relative to one another can represent a further crucial aspect of the concept presented.
• the weighting that is carried out means that the machine learning model can, on the one hand, take the physical framework conditions into account and, on the other hand, has enough freedom to adapt to the ideal data situation. This balanced interaction can provide a crucial advantage in the training process and improve the final prediction of IOL refractive power for new ophthalmic data.
• theoretical ophthalmological data can consist of literature data. Interpolations between the literature data - or data from other sources - can also generate intermediate values.
• the additional reference data obtained in this way could supplement or replace the mathematical-physical model in addition to or instead of the physical model in the loss function.
• the proposed concept could also be expanded to the effect that not only one physical model would be used to influence the loss function. Rather, the influencing of the loss function could also take into account at least one further physical model. In such a case, the loss function would be supplemented by another term that would be included with an additional weighting factor. The other function - in particular the supply of the input data - would take place in accordance with and in parallel with the first physical model.
• a speed advantage can be achieved during training, which can result from training not only using the clinical ophthalmological data during training, but also by measuring outliers in the clinical ophthalmological data being corrected directly by the physical model.
• significant computing power could be saved, and the available computing capacities can thus be better used.
• the physical model can cover the entire expected anatomical variability, resulting in more robust systems for determining or predicting the IOL refractive power. And (ii) by combining the physical model with the clinical data, less clinical data is needed for a robust model. Consequently, robust clinic-specific, doctor-specific or lens-specific models can be created.
• the first and the second component of the loss function can be weighted in a configurable manner.
• This enables fine tuning of the learning model of the machine learning system to be trained.
• it is configurable which of the two components of the loss function should be given more weight: (i) the clinical ophthalmological training data or (ii) the limitations of the physical model.
• the influencing parameters can be adjusted individually and also depending on the type of physical model selected.
• weightings of different strengths, depending on the type of physical model selected, or other or additional restrictions (“constraints”) could be specified.
• weighting function of the following type can be used:
• Delta first components, i.e. results of a deviation function (e.g. MSE, mean square error, i.e. the "mean of the square deviations") of the deviation values during training; and
• Phy second component, i.e. the constraint imposed by the physical model.
• the values for the weighting can be reset from training to training (or re-training).
• An explicit user interface can be provided for this in order to enable training under optimal conditions. In this way, different physical framework conditions - i.e. physical models - could also be tried out in an elegant way.
• the ophthalmological data can have OCT image data—i.e., complete “raw” image data or explicit ophthalmological values derived from OCT image data, or both OCT image data and values derived from the OCT image data.
• OCT image data i.e., complete “raw” image data or explicit ophthalmological values derived from OCT image data, or both OCT image data and values derived from the OCT image data.
• the image data are also biometric data. In this way there is great flexibility in the use of the training data to be used.
• an expected position of the intraocular lens to be used can be used as an additional input data value for the machine learning system in productive operation. It can be expected that in this way a further improved determination of the refractive power of the IOL will be possible.
• the learning model of the machine learning system can already be trained before the training with ophthalmological data by artificially generated training data that are based on laws of the physical model provided.
• the laws can be represented by a physical model - ie formulas.
• the physical model for the pre-training discussed here differs from the physical model during the main training discussed above. In this way, at least two different physical models could be considered: (i) one during the pre-training of the 2-stage training of the learning model of the machine learning system implemented in this way and (ii) a second during the subsequent main training of the learning model of the machine learning system.
• the above weighting of the loss function could easily be adjusted via the specially adapted user interface.
• the loss function would not have to be supplemented by an additional term.
• the training time and/or the amount of real training data can be reduced in this way. Existing resources would be better used.
• the physical model can also have literature data for determining the refractive power of the intraocular lens.
• the literature data can be available in tabular form, from which value tuples - e.g. also by interpolation of the existing values - could be provided as a supplement or replacement for the physical model.
• the physical model could be dispensed with, but without having to forego the influence of known limit values ("contraints").
• the intraocular lens to be used can be a spherical, toric or multifocal intraocular lens—or other lens shapes—to be used. This would allow the concept presented here to be used comprehensively.
• the training data and the physical model (or models) would also be selected accordingly.
• the machine learning system can be a neural network.
• This can be a convolutional neural network (CNN).
• CNNs turn out to be particularly helpful when it comes to a Processing of image data that has to be classified, such as the raw data of the ophthalmological data can be.
• any time-dependent data from 4D scans of the eye that may be present could also be used.
• an RNN Recurrent Neural Network
• an RNN Recurrent Neural Network
• the ophthalmological data of an eye can have at least one from the group consisting of an axial length, an anterior chamber depth, a lens thickness, a posterior chamber depth, a cornea thickness, a cornea keratometry, a lens - Equatorial plane, white-to-white distance and a pupil size.
• the second physical model can be represented as a mathematical model or as a ray tracing model. Consequently, in the second stage of generating training data, there are also options to use different methods in order to make improved model-based training data available. This can expand the scope for an individualization of the proposed method for specific purposes.
• the clinical ophthalmological training data can be determined or generated manually or by means of a third machine learning system.
• manual would mean that they would be measured using an eye scanning device.
• the training data that would be generated using a third machine learning system would be more artificial in nature, although it would also be possible to use a comparatively small amount of clinical ophthalmological data to generate a larger amount of training data using the third, already trained, machine learning system to provide for the final learning step.
• the method presented here could also be used with a comparatively small amount of clinical ophthalmological data would not normally suffice to be refined from the physical model(s) to real clinical data by means of the two-stage training.
• a GAN generative adversarial network
• FIG. 1 shows a flowchart-like representation of an embodiment of the computer-implemented method for determining the refractive power of an intraocular lens to be inserted.
• FIG 2 depicts an eye along with various biometric parameters of the eye.
• FIG. 3 shows a schematic structure of essential functional components of the underlying proposed method or the associated system.
• Figure 4 represents a diagram of the inventive prediction system.
• FIG. 5 is a diagram of a computer system that may additionally include the system of FIG. 4 in whole or in part.
• intraocular lens describes an artificial lens that can be surgically inserted into a patient's eye in place of the natural, biological lens.
• the term "loss function" describes an error function that outputs a value or a set of error values during training of a machine learning system, which is usually larger the further the predicted value and the expected value of the machine learning system at a Set of associated input values are apart.
• MSE mean square error or cross-entropy.
• the output value or values of the loss function are fed into the neural network—or the nodes or the weight functions—(backpropagation). In this way, actually predicted output values of the machine learning system converge in the direction of the annotated - i.e. desired - result values.
• machine learning system describes a system that is also typically associated with a method that learns from examples.
• the machine learning system is fed with annotated (ie also containing metadata) training data in order to predict previously defined output values - in the case of a classification system, output classes. If the output classes are correctly output with sufficient precision - ie a predetermined error rate - the machine learning system is said to be trained.
• machine learning is a basic concept or a basic function from the field of artificial intelligence, with e.g. statistical methods being used to give computer systems the ability to "learn”. For example, certain behavioral patterns are optimized within a specific area of responsibility.
• the methods used enable trained machine learning systems to analyze data without the need for explicit procedural programming.
• a NN neural network
• CNN convolutional neural network
• machine learning systems to form a network of nodes that act as artificial neurons and artificial connections between the artificial neurons ( so-called links), whereby parameters (e.g. weight parameters for the connection) can be assigned to the artificial connections.
• the weight parameter values of the connections automatically adjust based on input signals to produce a desired result.
• desired output data annotations
• a mapping from input data to output data is learned.
• neural network describes a network of electronically implemented nodes with one or more inputs and one or more outputs for performing arithmetic operations (activation functions). Selected nodes are connected to each other by means of connections - so-called links or edges.
• the links may have certain attributes, such as weight parameter values, which may affect output values from previous nodes.
• Neural networks are typically constructed in several layers. There is at least an input layer, a hidden layer and an output layer.
• image data can be fed to the input layer and the output layer can have classification results regarding the image data.
• typical neural networks have a large number of hidden layers up. The way in which the nodes are connected to links depends on the type of neural network in question.
• the predicted value of the neural learning system can be the sought-after refractive power of the intraocular lens.
• recurrent neural network refers to neural networks which, in contrast to feed-forward networks, are characterized by connections from neurons (i.e. nodes) in a layer to neurons in the same or in a previous layer. In the brain, this is the preferred way of connecting neural networks, especially in the neocortex.
• recurrent connections of model neurons are often used to discover time-coded - i.e. dynamic - information in the data. Examples of such recurrent neural networks are the Elman network, the Jordan network, the Hopfield network and the fully interconnected neural network. They are also suitable for examining dynamic behavior in images of eyes, in particular for taking into account the accommodation behavior of the eye.
• CNN Convolutional Neural Network
• CNN convolutional Neural Network
• parameter value describes geometric or biometric values or ophthalmological data of a patient's eye. Examples of parameter values of an eye are discussed in more detail with reference to FIG. 2 .
• scan result describes digital data, e.g. based on digital images/recordings, which represents the result of an OCT examination (optical coherence tomography) on a patient's eye.
• OCT examination optical coherence tomography
• OCT optical coherence tomography
• OCT optical coherence tomography
• A-scan also axial depth scan
• A-scan describes a one-dimensional result of a scan of a patient's eye, which provides information about geometric dimensions and locations of structures within the eye.
• B-scan describes a lateral superimposition of several of the A-scans mentioned in order to produce a section through the eye. Volume views can also be generated by combining several layers of the eye generated in this way.
• en-face OCT here describes a method for producing transversal sectional images of the eye—in contrast to longitudinal sectional images with the above-mentioned A or B scans.
• dynamic eye data describes a sequence of 2D sectional images of the eye—usually at the same point—in order to identify dynamic, i.e. temporal, changes—e.g. the eye’s ability to adapt.
• digital image e.g. from a scan - here describes an image or the result of generating a quantity of data in the form of pixel data of a real existing object: here, for example, a retina of an eye.
• a "digital image” can be understood as a two-dimensional signal matrix.
• the individual vectors of the matrix can also be joined together in order to generate an input vector for a layer of a CNN.
• the digital images can also be individual frames of video sequences.
• clinical ophthalmological training data describes data about patient eyes and intraocular lenses that have already been used in these patients in the past.
• the clinical ophthalmological training data can be determined ophthalmological parameter values, as well as the refractive power and the position of the inserted lens. This data is used to train the machine learning system, which has previously been trained on the basis of data from a physical model.
• the clinical ophthalmological training data are usually annotated.
• training data describes data with which the machine learning system can be trained. This training data for the machine learning system is ophthalmic data and associated refractive power values from previous successful lens exchange surgeries.
• the term "physical model” refers to a mathematical formula that relates various parameters of an eye to make refractive power determinations. Well-known formulas are those of Haigis and the Universal Barrett II formula. A ray tracing method could also be used.
• refractive power of an intraocular lens describes the refractive index of the IOL.
• Figure 1 depicts a flowchart-like representation of an embodiment of the inventive computer-implemented method 100.
• the method 100 includes providing 102 a physical model for a refractive power determination for an intraocular lens. This can be a formula for determining a refractive power based on a set of input parameters, data from another trained machine learning system, or literature data, e.g. stored in tabular form.
• the method 100 includes training 104 a machine learning system with clinical ophthalmological training data and associated desired results to form a learning model for determining refractive power, with a loss function for training having two components.
• the desired results are the predicted results of the machine learning system given the presence of certain input parameter values.
• the combination of input data and expected result data is also referred to as 'ground truth' in the context of machine learning. This applies in particular to the so-called "supervised learning" undertaken here.
• a first component of the loss function takes into account the corresponding clinical ophthalmological training data and associated desired results.
• This component of the loss function can use the well-known mean square error method.
• the component of the loss function quaddratic becomes larger the further the predicted value deviates from the annotated associated result (prediction) value. Using the square ensures that both numerically positive values and numerically negative deviation values are taken into account in the same way.
• the second component of the loss function takes into account limitations of the physical model in that a loss function component value of this second component becomes larger the further a predicted value of the refractive power is by the machine learning system during training from results of the physical model with the same clinical ophthalmological training data as input values for the physical model.
• the method 100 also includes providing 106 determined ophthalmological data of a patient and predicting 108 the refractive power of the intraocular lens to be used by means of the trained machine learning system, with the ophthalmological data provided being used as input data for the machine learning system
• a position of the intraocular lens to be inserted can also be used as an additional input value for the machine learning system (cf. 110).
• 2 shows an eye 200 with different biometric or ophthalmological parameters of an eye.
• the following parameters are shown: axial length 202 (AL, axial length), anterior chamber thickness 204 (ACD, anterior chamber depth), keratometry value 206 (K, Radius), refractive power of the lens (Power), lens thickness 208 (LT, central cornea thickness 210 (CCT), white-to-white distance 212 (WTW, white-to-white distance ), pupil size 214 (PS), posterior chamber depth (PCD) 216, retina thickness 218 (RT).
• At least one of these parameters is contained both in the ophthalmological training data and in the ophthalmological data of a patient, which are each contained in the subject matter of the concept presented here.
• FIG. 3 shows a schematic structure 300 of essential function blocks that are useful for carrying out the proposed method.
• a suitable physical model 302 of an eye for a refractive power determination is selected and provided.
• training data 304 for the machine learning system 310 is made available.
• this is so-called ground truth data, i.e. result values for the prediction of the refractive power values 308 and (annotated) measured ophthalmological data 306.
• the complete image data of the corresponding eye can also be used additionally or alternatively (e.g. A-Scan, B-Scan, etc.).
• the input values of the training data are provided to a calculation module for result values for the physical model.
• this determines the deviation of the output of the machine learning system 310 (described in more detail in the next paragraph) from the physically correct solution and returns a value that increases as the output from of the machine learning system 310 removed from this solution.
• other sources such as literature values, can also be used.
• the machine learning system 310 under training is represented as a deep neural network (DNN).
• DNN deep neural network
• This has an input layer of nodes (left) and an output layer (right) of nodes. True, only four or two knots shown, but with a neural network that can actually be used, the number of input nodes and output nodes would typically be significantly higher.
• the parameters for the nodes or the corresponding weight functions of the connections between the nodes are determined iteratively.
• the loss function 312 determines which values the weight functions or also parameter values of the nodes assume during the training. Simply put, training continues until a deviation between the desired IOL power and the IOL power predicted by the machine learning system falls below a predetermined minimum value.
• the special feature of the method proposed here is that the value of the loss function 312 is not only based on the difference described above, but also has a second - typically additive, e.g. additionally linear - component, which is determined by the results of the Calculation module for the underlying physical model 302 is determined.
• a second - typically additive e.g. additionally linear - component, which is determined by the results of the Calculation module for the underlying physical model 302 is determined.
• a synchronization unit is advantageously available, which controls the supply of further training data in such a way that new training data are only made available when both components of the loss function have been used for one Back-Propagation Cycle step were available and so the training step could be fully completed.
• the then trained machine learning system 314 can now receive ophthalmological data 316 of a patient and, using its trained machine learning model, can predict the refractive power 318 by means of the prediction unit 320 for an intraocular lens to be used.
• image data of the patient's eye determined can be used as input values for the trained machine learning system 314 either instead of or in addition to the ophthalmologic data 316 .
• FIG. 4 shows—for the sake of completeness—a preferred exemplary embodiment of components of the system 400 for determining refractive power, which support the training of the machine learning system of the proposed method 100 and which are also used in the operative phase of the method.
• the system 400 includes a processor 402 that can execute program modules or program code stored in the memory 404.
• the processor influences the function of the following components in such a way that the elements of the method can be executed.
• the system 400 has a provision module 406 for storing the physical model.
• the physical model In this case, for example, literature values for combinations of measured ophthalmological data and associated IOL refractive power values can also be stored, or the model can be stored in the form of a physical formula with corresponding parameters.
• a physical model calculation unit 408 using the memory of the physical model providing module 406 may be provided.
• a calculation unit 418 for the loss function can also be present, which takes into account the two components described above.
• the training module 410 which is adapted to train a machine learning system with clinical ophthalmic training data and associated desired results to form a learning model for IOL refractive power determination, uses the results of the loss function during training.
• the loss function has the following components: (i) a first component, which takes into account the corresponding clinical ophthalmological training data and associated and desired results, and (ii) a second component, which takes into account limitations of the physical model in that an associated loss -Function component value of this second component becomes larger, the further away a predicted value of the refractive power during the training is from results of the physical model - or possibly other boundary conditions ("constraints") with the same clinical ophthalmological training data as input values.
• a polynomial or exponential function for example, can also be used for this.
• a patient's ophthalmic data is provided to machine learning system 412 (which corresponds to machine learning system 310 of Figure 3).
• the prediction unit 416 (cf. FIG. 3, 320) outputs the prediction data determined by the machine learning system 412 for the refractive power of the intraocular lens to be used, with the ophthalmological data provided being used as input data for the machine learning system.
• the memory 414 can also be used for the ophthalmological training data.
• the modules and units - in particular the processor 402, the memory 404, the provision module 406 for storing the physical model, the calculation unit 408 for the physical model, the calculation unit 418 for the loss function , the training module 410, the machine learning system 412, the memory 416 for the ophthalmological data and the prediction unit 416 - can be connected to electrical signal lines or via a system-internal bus system 420 for the purpose of signal or data exchange.
• a display unit can also be connected to the system-internal bus system 420 or the prediction unit 416 in order to output, display or otherwise further process or forward the refractive power.
• the predicted refractive power results according to the predicted class that is predicted with the greatest probability.
• the final refractive power of the IOL can also be implemented using a regression system as a machine learning system with numerical output variables.
• Figure 5 illustrates a block diagram of a computer system which may include at least portions of the refractive power determination system.
• Embodiments of the concept proposed here can in principle be used together with practically any type of Computer, regardless of the platform used in it for storing and/or executing program codes.
• 5 shows a computer system 500 by way of example, which is suitable for executing program code in accordance with the method presented here, but which can also contain the prediction system in whole or in part.
• the computer system 500 has a plurality of general purpose functions.
• the computer system can be a tablet computer, a laptop or notebook computer, another portable or mobile electronic device, a microprocessor system, a microprocessor-based system, a smartphone, a computer system with specially configured special functions, or even a component of a microscope system be.
• the computer system 500 can be set up to execute instructions that can be executed by the computer system—such as, for example, program modules—that can be executed in order to implement functions of the concepts proposed here.
• the program modules can have routines, programs, objects, components, logic, data structures, etc. in order to implement specific tasks or specific abstract data types.
• the components of the computer system may include: one or more processors or processing units 502, a memory system 504, and a bus system 506 that connects various system components, including the memory system 504, to the processor 502.
• computer system 500 includes a plurality of volatile or non-volatile storage media accessible by computer system 500 .
• the data and/or instructions (commands) of the storage media can be stored in volatile form - such as in a RAM (random access memory) 508 - to be executed by the processor 502.
• RAM random access memory
• Further components of the memory system 504 can be a permanent memory (ROM) 510 and a long-term memory 512, in which the program modules and data (reference number 516), as well as workflows, can be stored.
• the computer system has a number of dedicated devices (keyboard 518, mouse/pointing device (not shown), display 520, etc.) for communication. These dedicated devices can also be integrated into a touch-sensitive display be united.
• a separately provided I/O controller 514 ensures smooth data exchange with external devices.
• a network adapter 522 is available for communication via a local or global network (LAN, WAN, for example via the Internet). The network adapter can be accessed by other components of computer system 500 via bus system 506 . It should be understood that other devices may be connected to computer system 500, although not shown.
• At least parts of the system 400 for determining the refractive power of an IOL can be connected to the bus system 506.
• the principle presented here can be embodied both as a system, as a method, combinations thereof and/or also as a computer program product.
• the computer program product can have one (or more) computer-readable storage medium(s) having computer-readable program instructions for causing a processor or a control system to execute various aspects of the present invention.
• Electronic, magnetic, optical, electromagnetic, infrared media or semiconductor systems are used as the transmission medium; for example SSDs (solid state device/drive), RAM (Random Access Memory) and/or ROM (Read-Only Memory), EEPROM (Electrically Eraseable ROM) or any combination thereof.
• Propagating electromagnetic waves, electromagnetic waves in waveguides or other transmission media (eg light pulses in optical cables) or electrical signals that are transmitted in wires can also be used as transmission media.
• the computer-readable storage medium may be an embodying device that stores instructions for use by an instruction execution device.
• the computer-readable program instructions described here can also be downloaded onto a corresponding computer system, for example as a (smartphone) app from a service provider via a cable-based connection or a cellular network.
• the computer-readable program instructions for performing operations of the invention described herein may be machine-dependent or machine-independent instructions, microcode, firmware, state-defining data, or any source or object code written, for example, in C++, Java, or the like or be written in conventional procedural programming languages, such as the "C" programming language or similar programming languages.
• the computer-readable program instructions are executable entirely by a computer system. In some embodiments, it can also be electronic circuits, such as programmable logic circuits, field-programmable gate arrays (FPGA) or programmable logic arrays (PLA), which execute the computer-readable program instructions by using status information of the computer-readable program instructions. to configure or customize the electronic circuitry in accordance with aspects of the present invention.
• the computer-readable program instructions may be provided to a general-purpose computer, special-purpose computer, or other programmable data processing system to produce a machine such that the instructions are executed by the processor or the computer or other programmable data processing device be, generate means to the functions or implement operations illustrated in the flowchart and/or block diagrams.
• these computer-readable program instructions can also be stored on a computer-readable storage medium.
• each block in the illustrated flowchart or block diagrams may represent a module, segment, or portion of instructions, which represents a plurality of executable instructions for implementing the specific logical function.
• the functions that are shown in the individual blocks can be executed in a different order—if appropriate, also in parallel.

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• 2021
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