Theory-motivated domain control for ophthalmological machine-learning-based prediction method
A computer-implemented method for determining the refractive power of an intraocular lens includes providing a physical model for determining refractive power 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 for training includes: a first component taking into account clinical ophthalmological training data and associated and desired results and a second component taking into account limitations of the physical model wherein a loss function component value is greater the further a predicted value of the refractive power during the training is from results of the physical model with the same clinical ophthalmological training data as input values. Moreover, the method includes providing ophthalmological data of a patient and predicting the refractive power of the intraocular lens to be used by means of the trained machine learning system.
1 . A computer-implemented method for determining refractive power for an intraocular lens to be inserted, the method comprising
providing a physical model for determining refractive power for an intraocular lens,
training a machine learning system with measured clinical ophthalmological training data and associated desired results to form a learning model for determining the refractive power, wherein a loss function for the training comprises two components, and wherein:
a first component of the loss function takes into account corresponding items of the measured clinical ophthalmological training data and associated and desired results, and
a 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 all the greater, the further a predicted value of the refractive power during the training deviates from results of the physical model with the measured clinical ophthalmological training data as input values,
providing measured ophthalmological data of a patient, and
predicting the refractive power of the intraocular lens to be inserted by means of the trained machine learning system, wherein the measured ophthalmological data is used as input data for the machine learning system.
2 . The method of claim 1 , wherein the first and second components of the loss function are weightable in a configurable manner.
3 . The method of claim 2 , further comprising applying a weighting function (“W L ”), wherein the weighting function “W L ” is defined by the following equation:
W L =B*[a *(Delta)−(1− a )* Phy ], wherein
W L =value of the loss function,
B=general constant or further function term of the loss function,
a=weighting constant,
Delta=first components, and
Phy=second component.
4 . The method of claim 1 , wherein the measured clinical ophthalmological training data comprises:
OCT image data; or
explicit values derived from OCT image data or
both OCT image data and values derived from OCT image data.
5 . The method of claim 1 , wherein an expected position of the intraocular lens to be inserted is used as additional input data for the machine learning system.
6 . The method of claim 1 , wherein the learning model of the machine learning system, before the training with the measured clinical ophthalmological training data, has already been trained using artificially generated training data based on laws of the physical model provided.
7 . The method of claim 1 , wherein the physical model also comprises literature data for determining refractive power for an intraocular lens.
8 . The method of claim 1 , wherein the intraocular lens to be inserted is a spherical, toric or multifocal intraocular lens to be inserted.
9 . A system for determining refractive power for an intraocular lens to be inserted, the system comprising:
a providing module in which a physical model for determining refractive power for an intraocular lens is stored,
a training module adapted for training a machine learning system with measured clinical ophthalmological training data and associated desired results to form a learning model for determining the refractive power, wherein parameter values of the learning model are stored in the learning system, wherein a loss function for the training comprises two components, and wherein:
a first component of the loss function takes into account corresponding items of the measured clinical ophthalmological training data and associated and desired results, and
a 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 all the greater, the further a predicted value of the refractive power during the training deviates from results of the physical model with the measured clinical ophthalmological training data as input values,
a memory for measured ophthalmological data of a patient, and
a prediction unit adapted for predicting the refractive power of the intraocular lens to be inserted by means of the trained machine learning system, wherein the measured ophthalmological data is used as input data for the trained machine learning system.
10 . A non-transitory computer-readable storage medium storing program instructions, wherein the program instructions, when executed by one or more computers or control units, cause said one or more computers or control units to:
provide a physical model for determining refractive power for an intraocular lens,
train a machine learning system with measured clinical ophthalmological training data and associated desired results to form a learning model for determining the refractive power, wherein a loss function for the training comprises two components, and wherein:
a first component of the loss function takes into account corresponding items of the measured clinical ophthalmological training data and associated and desired results, and
a 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 all the greater, the further a predicted value of the refractive power during the training deviates from results of the physical model with the clinical ophthalmological training data as input values,
provide measured ophthalmological data of a patient, and
predict the refractive power of the intraocular lens to be inserted by means of the trained machine learning system, wherein the measured ophthalmological data are used as input data for the machine learning system.
11 . The non-transitory computer-readable storage medium of claim 10 , wherein the first and second components of the loss function are weightable in a configurable manner.
12 . The non-transitory computer-readable storage medium of claim 11 , wherein the program instructions also cause the one or more computers or control units to apply a weighting function (“W L ”), wherein the weighting function “W L ” is defined by the following equation:
W L =B*[a *(Delta)−(1− a )* Phy ], wherein
W L =value of the loss function,
B=general constant or further function term of the loss function,
a=weighting constant,
Delta=first component, and
Phy=second component.
13 . The non-transitory computer-readable storage medium of claim 10 , wherein the measured clinical ophthalmological training data comprises:
OCT image data;
explicit values derived from OCT image data; or
both OCT image data and values derived from OCT image data.
14 . The non-transitory computer-readable storage medium of claim 10 , wherein an expected position of the intraocular lens to be inserted is used as additional input data for the machine learning system.
15 . The non-transitory computer-readable storage medium of claim 10 , wherein the learning model of the machine learning system, before the training with the measured clinical ophthalmological training data, has already been trained using artificially generated training data based on laws of the physical model.
16 . The non-transitory computer-readable storage medium of claim 10 , wherein the physical model also comprises literature data for determining refractive power for an intraocular lens.
17 . The non-transitory computer-readable storage medium of claim 10 , wherein the intraocular lens to be inserted is a spherical, toric or multifocal intraocular lens to be inserted.