Method for training a prediction model, method for determining laser irradiation parameters and device for treating a retina

Abstract

The invention relates to a method for training a prediction model (1, 2) which assigns one or more laser irradiation parameters (10a, 10b, 10c) to current state parameters (3a, 3b, 3c, 4a, 4b, 4c) of a patient (5) for treating a retina, characterised in that the following are used as training data for a plurality of patients (9a, 9b, 9c) in each case: at least one parameter (6a, 6b, 6c), in particular a skin parameter, hair parameter, iris parameter, the age of the patient, a physiologically effective life habit or an underlying medical condition, determined independently of the retina of the patient, and at least one retina image (7a, 7b, 7c) of the patient obtained by an imaging method, as training input parameters; and a threshold for the laser irradiation parameter or parameters (8a, 8b, 8c), which, if exceeded, results in lasting damaging tissue changes, as a training result parameter. The invention also relates to such a prediction model and its used in the irradiation of retinas of patients.

Description

[0001] Method for forming a prediction model, method for determining laser irradiation parameters and device for retinal treatment

[0002] The invention lies in the field of medical technology and measurement technology as well as laser technology and data processing. It is particularly advantageous in the treatment of the retina of patients with various

[0003] diseases or age-related developments.

[0004] In laser retinal treatment, it is desirable to select and control the irradiation intensity appropriately to achieve a physiologically beneficial effect, while remaining below an efficacy threshold at which serious and undesirable retinal changes are produced. This threshold depends not only on other boundary conditions such as the laser parameters, but also significantly on the physiological properties of a patient's individual retina, such as the intensity of pigmentation. Efforts have been made to individually record the physiological parameters of a patient being treated in order to be able to perform optimized retinal treatment. For example, see the article: Ivanova, EV and Volodin, PL: "Development of the selective micropulse individual retinal therapy depends on age and type of Fitzpatrick scale", Graefes Arch. Clin. Exp. Ophtalmol.(2022), to determine the age and skin type of a patient according to the so-called Fitzpatrick scale, whereby the approach is based on the assumption that these parameters are related to properties of the retina.

[0005] Against the background of the prior art, the object of the present invention is to provide a method and a device for retinal treatment which allow the necessary parameters of a patient to be recorded with the least possible effort, which enable a reliably useful and safe treatment of the retina.

[0006] The invention solves this problem by providing a method for developing a predictive model, a method for determining laser irradiation parameters, and a device for treating the retina of patients using laser irradiation. Furthermore, advantageous embodiments of these aspects are provided.

[0007] Thus, the invention relates, inter alia, to a method for forming a prediction model, in particular for training a neural network or an algorithm, or for obtaining data for a regression analysis or a multivariate analysis to form an assignment function, wherein the prediction model, in particular the neural network, the algorithm or the assignment function, assigns one or more laser irradiation parameters for a retinal treatment to current condition parameters of a patient, wherein as training data for a plurality of patients, on the one hand, as training input parameters, at least one retinal image of the patient obtained by an imaging method, and in particular at least one parameter determined independently of the patient's retina, in particular one or more retina-independent,parameters independent of the patient's eye and / or one or more retina-independent parameters of the patient's eye, further in particular a skin parameter, a hair parameter, an iris parameter, a parameter relating to one or more of the elements cornea, lens and vitreous body, the patient's age, a physiologically effective lifestyle, an occupation, regular medication intake or a pre-existing condition, and on the other hand, as a training result parameter, a threshold value for the laser irradiation parameter(s) above which permanent damaging tissue changes occur, are used.

[0008] A parameter that affects one or more of the elements cornea, lens, and vitreous body can also be understood, for example, as the fact that an operation was performed on the eye, the type of operation, the date of the operation, and which elements of the eye were altered or replaced. Replacement of the crystalline lens is a particular example here.

[0009] Furthermore, a parameter can relate to a profession practiced by the patient or a predominantly practiced profession that can have a particular impact on the eye. For this purpose, for example, classes of professions can be created that each entail similar strains on the eye, such as professions carried out outdoors with high UV exposure and professions that are carried out indoors with lower UV exposure. Individual professions can also be specified / checked from a list, to which certain strains can then be automatically assigned, such as farmer, roofer or bricklayer (with high UV exposure) or office clerk, accountant, cook (with low UV exposure) or steelworker (with high IR radiation exposure). With regard to physiologically effective lifestyle habits, for example, the patient can be asked / specified whether they are a smoker or non-smoker or until when they smoked regularly.Certain medications, when taken regularly over a certain period of time, can also cause changes in the eye that can be taken into account as parameters.

[0010] Certain diseases, such as diabetes, can also be queried or specified and considered as parameters, as can the habit of exercising and the regular volume of physical activity. Certain sports that are practiced regularly can also be queried or specified, such as surfing, sailing, mountaineering with high UV exposure, and swimming with moderate UV exposure, but possibly with certain chemical exposures from water additives.

[0011] A prediction model can, for example, be designed as a programmable and / or trainable data processing device.

[0012] An assignment function can be understood as any mechanism that assigns one or more laser irradiation parameters for a retinal treatment to individual variables, groups of variables, or ranges of variables. For this purpose, assignment functions can also be created in the form of assignment tables with discrete values. For example, age periods (e.g., equally sized periods: 10-20 years, 20-30 years, 30-40 years, etc.) or unevenly sized periods with smaller periods around the ages of 34, 60, and 78, where more significant changes tend to occur, and larger periods in the remaining ranges can be created, with each of these age periods being assigned a set of irradiation parameters.

[0013] Similarly, the absorption strength can be divided into ranges (e.g. absorption = 0-0.19, 0.2-0.39, 0.4-0.59 etc.).

[0014] In this context, laser irradiation parameters are understood to be parameters that describe the intensity of the irradiation of the areas of the retina to be treated, i.e. to be irradiated.

[0015] This does not include excluding certain areas of the retina from treatment, such as blood vessels, as these areas are not being treated and no laser parameters need to be determined there. Laser parameters or laser irradiation parameters thus describe the active operation of the treatment laser, in which it actually emits radiation, such as laser power, power density, spot diameter, pulse duration, pulse frequency, duty cycle, number of pulses, and the total energy density delivered into the tissue.

[0016] A retinal image on its own or a group of different retinal images with partially overlapping information content already allows the derivation of a large amount of information, which is linked to the respective threshold value determined by the prediction model.

[0017] Pigmentation intensity or density can be used as a skin, hair, and iris parameter because it correlates to a certain extent with the retinal pigmentation intensity. Pigmentation intensity can be determined, for example, using a reflectivity measurement. However, the skin parameter, like the hair or iris parameters, can also be determined by color measurement, for example, by analyzing a photographic image, particularly a color image or IR image, or live view, and analyzing the color in a color space, for example, the RGB or HSV color space, based on the intensities in the respective color channels.

[0018] A physiologically effective lifestyle habit can be, for example, the habit of smoking, regular alcohol consumption or other drug use, or being overweight, while a pre-existing condition could be, for example, diabetes or high blood pressure.

[0019] The optional combination of a patient parameter determined independently of the patient's retina and at least one retinal image as a training input parameter allows for objective, yet fast and easy data collection. Using a retinal image alone can also allow, for example, the detection and treatment of retinal injuries or changes compared to a previous state, or even the exclusion of certain patient data sets from training due to certain atypical manifestations.

[0020] The patient's retina-independent parameters can therefore include, in particular, a skin parameter, a hair parameter, an iris parameter, a parameter relating to one or more of the elements cornea, lens, and vitreous body, the patient's age, a physiologically effective lifestyle, or a pre-existing condition. Skin parameters / skin color, a hair parameter / hair color, and an iris parameter / iris color can each refer to the color as well as the brightness or reflectivity of the skin, hair, or iris. The retina-independent parameters include, among others, variables that are completely independent of the patient's eye, such as skin and hair parameters, but also parameters that can be entered manually into the system / prediction model or measured directly on the eye, such as iris color or brightness, as well as parameters relating to one or more of the elements cornea, lens, and vitreous body.This could mean, for example, that a retina-independent parameter is determined by measurement, manually entered, or taken from patient data, whether the patient has an artificial lens and, optionally, also how long ago the corresponding surgery to replace their natural lens took place. It has been shown that this parameter has a particular influence on the absorption and / or scattering of the treatment radiation before it reaches the retina. Furthermore, this influence also applies when taking retinal images, so that the scattering and absorption of radiation reflected in the retina and subsequently recorded, for example, by an imaging system, is even doubly influenced by this scattering and absorption.

[0021] In general, retina-independent parameters can be optionally considered for training the prediction model. Further, eye-independent parameters and / or retina-independent parameters of the patient's eye, such as the properties of the iris and / or cornea and / or lens and / or vitreous body, can be optionally considered under these retina-independent parameters. Considering the retina-independent parameters of the eye can be particularly useful because they can be measured directly in the patient's eye.

[0022] These parameters can then be combined with one of the other parameters such as hair color or skin color as input for the system to be trained.

[0023] The scattering and / or absorption of radiation in the cornea and / or the lens and / or the vitreous body can be determined, for example, by measuring the reflectivity of different retinal regions. For this purpose, retinal regions known to have different reflectivities are selected, such as the optic disc, blood vessels carrying arterial blood, blood vessels carrying venous blood, and the other normally reflecting, sensitive regions of the retina. If the intensity of reflected radiation through the vitreous body, the lens, and the cornea is measured in two or more of these retinal regions, and the ratios of the reflectivities in these retinal regions are known, the radiation loss due to scattering and absorption on its way through the eye can be calculated as an offset.

[0024] The above statements regarding the parameters that can be taken into account in addition to the retinal images when training the prediction model also refer to the parameters that are determined and used in the process of determining laser irradiation parameters for treating a patient's retina during operation / use of the prediction model.

[0025] As a training outcome parameter, a threshold value for the laser irradiation parameter(s) is used for each patient's specific training input parameters. If exceeded, permanent damaging tissue changes occur. To determine the threshold values ​​for each individual retina providing training data, the patient's retina can be treated at different locations with different titration shots of varying intensity—i.e., with laser treatments of varying intensity. Image analysis can then be used to determine the laser treatment intensity at which harmful effects in the form of visible tissue changes can be detected.For this purpose, each retina providing training data is analyzed after one or more laser treatments of varying intensities using one or more imaging techniques. The lowest laser intensity at which tissue damage is already visually detectable is determined. This laser intensity value can be set as the threshold.

[0026] To visually detect tissue damage for each retina, several different imaging techniques can be used cumulatively, even after different waiting times after treatment. In each case, the lowest laser intensity threshold detectable in one of the techniques, at which damage can be detected using one of the techniques, can be adopted as the determined treatment threshold. Mapping of the retina and the determination of location-dependent thresholds, above which permanent damaging tissue changes occur, can occur independently of the development of the prediction model, for example, immediately during the acquisition of the remaining training data, but also weeks or months beforehand. It can, for example, take place at a different location and at a different time, and by different people, than the training of the prediction model.This mapping can, for example, also be carried out based on the available results of a previous laser treatment or by taking a retinal image and assigning values ​​from literature or experience.

[0027] Both the threshold values ​​determined from the training data and the threshold value determined by the prediction model or the determined irradiation parameters can be presented and processed according to the model as location-independent variables applicable to a patient's retina as a whole. This results in simple handling, while maintaining certain safety limits. However, a location-dependent design can result during treatment through location-dependent correction variables based on a fundus image acquired close to the treatment site. In principle, however, the prediction model should not be limited to location-independent variables, so that threshold values ​​for laser treatment can also be determined for specific regions of the retina.

[0028] When applying the prediction model to determine specific laser treatment parameters for a patient based on currently recorded condition parameters, it can generally be planned that the same or a similar selection of input parameters be recorded for the patient to be treated as those recorded in the training data. The assignment of treatment parameters then benefits from a large number of empirical values ​​with well-studied patients.

[0029] A particular advantage of the predictive model according to the invention, however, may be that significantly less input data is collected prior to the actual treatment of a patient than for training the model, thus saving time, effort, and costs. A further advantage of the model may be that it determines existing correlations between the training data, some of which are redundant or have been collected with regard to their information content. Thus, in a specific application, it can partially replace or render superfluous the data not collected for a patient being treated. This applies particularly to retinal images, which can be complex to obtain.It is advantageous if a single image or a few images are sufficient to capture a patient's current status data prior to treatment. It can be particularly advantageous if a single technical variant of retinal imaging (e.g., fundus color photography or infrared photography) is sufficient, and if necessary, only images need to be taken using a single imaging method under different conditions. This saves both the patient and the treating physician time and effort. Different conditions of an imaging method can, for example, mean that multiple images are taken under different illumination intensities or different illumination colors / wavelengths.

[0030] An exemplary, concrete selection of input parameters that has proven to be well-qualified and significant for training the prediction model includes one or more retinal images as well as the patient's age (if applicable, a life stage) and a retina-independent eye parameter, in particular a parameter that describes the status of the eye lens, i.e. whether it is a natural or artificial lens and / or whether and when certain operations have been performed on the eye.

[0031] In order to allow scaling of the illuminance when taking retinal images and the sensitivity of the imaging system, for example a camera, and to enable comparability of the recorded retinal images when training the prediction model on the one hand and when specifically applying the prediction model in the preparation of a patient's treatment, it is useful to determine these variables, i.e. the illuminance when taking retinal images and the sensitivity of the imaging system, both when training the prediction model and when specifically applying it, and to either adapt them to each other or take them into account using a scaling function.

[0032] The statement that the predictive model assigns one or more laser irradiation parameters for a retinal treatment to a patient's current condition parameters can mean that the predictive model specifies / assigns multiple laser irradiation parameters or sets of laser irradiation parameters for different treatment methods or treatment goals, or different sets of laser irradiation parameters that correspond to different probabilities or risk probabilities of tissue damage. For example, the predictive model can assign one set of laser irradiation parameters for a treatment that remains safely below the damage threshold and a second set of laser irradiation parameters for a treatment in which tissue coagulation / partial tissue damage is accepted.In the same sense, the retinal treatment facility can also be equipped for such different treatment approaches.

[0033] It should also be noted that, in the context of this text, laser treatment or irradiation can be understood to mean, in addition to treatment / irradiation with a laser, treatment / irradiation with a light source equivalent to a laser, such as a light-emitting diode with sufficient power, and a treatment laser can be understood to mean a light source equivalent to a laser, such as a light-emitting diode.

[0034] A particular embodiment of the method can provide that for training purposes for each patient at least two retinal images are used as training input parameters, which are acquired at different times, in particular before and after a titration, and / or with different imaging techniques and / or under different technical recording conditions.

[0035] It can be provided that one or two of the following methods are used as imaging methods for determining the training input parameters:

[0036] Photographic color image, photographic infrared image (IR photography), OCT-B-scan (depth section image of an image created with optical coherence tomography), BAF (blue autofluorescence, fluorescence image without additional dye, excitation wavelength 488 nm), scanning laser ophthalmoscopy, OCT in conjunction with scanning laser ophthalmoscopy), FAG (fluorescence angiography, fluorescence image with dye fluorescein, excitation wavelength 488 nm). It is thus possible, for example, to combine a retinal image taken using color fundus photography with an IR photograph, or with an OCT B-image, or with a blue autofluorescence image (BAF), or with a scanning laser ophthalmoscopy image (SLO), or to combine several color fundus photographs at different illumination intensities or different illumination spectra, or to combine an IR photograph with an OCT image or an SLO.An OCT image can also be combined with an SLO, or one of these with a BAF. Each of the methods mentioned can also be combined with, for example, an FAG image. Multiple fundus color photographs or IR images taken under different imaging conditions can also be combined, or multiple fundus color photographs or IR images taken under different imaging conditions can be combined with another of the methods mentioned.

[0037] The combination of three or more of the above-mentioned methods is of course also possible to generate training data.

[0038] This approach not only provides more data for training or for gathering data for regression analysis, but also identifies correlations between the different types of retinal images and redundancies / overlaps in information content and feeds these into the prediction model. For example, if two different types of images yield essentially the same conclusion, the collection of current patient data can be limited to one of the two types of images without substantial loss of quality when applying the prediction model to a specific treatment case, or at least to a smaller number of images than were used to create the training data.

[0039] Furthermore, in the application case, one or a group of the less complex methods for creating retinal images can be used to determine the current status data of a patient, i.e., for example, a less costly or time-consuming method or a method that can be carried out with less error-proneness without specialist personnel or at least with semi-skilled personnel.In a method of the type described, it can further be provided that the respective threshold value for the laser irradiation parameter(s) for each patient is determined by a series of titration shots on the patient's retina with different laser irradiation parameters and a subsequent detection of tissue changes on the basis of several retinal images obtained by different imaging methods, wherein in particular the lowest threshold value determined with one of the imaging methods is used as a training result parameter for forming the prediction model, in particular the training of a neural network or for obtaining data for a regression analysis or a multivariate analysis for forming an assignment function.

[0040] When creating training data, this allows the threshold for the laser irradiation parameter(s) for each patient, at which permanent tissue damage occurs when reached or exceeded, to be determined with increased certainty, sensitivity, and reliability. It cannot be ruled out that certain detection methods may be more sensitive in certain patient groups than in others. Furthermore, certain methods may become more sensitive in detecting tissue changes than others after a certain waiting period following the titration shots.

[0041] The evaluation of retinal images can be performed in any of the methods used, for example, by machine through digital image analysis. This provides objective criteria.

[0042] In order to specifically determine the threshold values ​​when obtaining training data, it can be provided that the respective threshold value for the laser irradiation parameter(s), in particular for the laser power, the power density, the spot diameter, the pulse duration, the number of pulses or the duty cycle or a combination of two or more of the aforementioned variables, is determined for each patient by a series of titration shots on the patient's retina with different laser irradiation parameters and a subsequent detection of tissue changes on the basis of several retinal images obtained by different imaging methods, wherein the training result parameter for developing the prediction model, in particular the training of a neural network, for all examined patients is the threshold value determined by the imaging method which shows the lowest threshold value for the most examined patients.

[0043] Specifically, one implementation of the method may provide for the respective threshold value for the laser irradiation parameter(s) for each patient to be determined as a training result parameter through a series of titration shots on the patient's retina with different laser irradiation parameters and subsequent detection of tissue changes using optical coherence tomography in conjunction with IR-scanning laser ophthalmoscopy.

[0044] It can further be provided in the method of the type mentioned above that a power of the irradiation laser or a power density of the laser spot with otherwise fixed laser irradiation parameters or a total energy deposited on a laser spot of a fixed size over the irradiation time is selected as the laser irradiation parameter which varies during the titration shots and whose threshold value is determined as a training result parameter.

[0045] Each of these variables can be varied during laser irradiation, assuming otherwise constant boundary conditions, which can produce permanent tissue changes if a threshold is exceeded. The threshold for tissue changes can be determined using the described series of tests.

[0046] A particular challenge when determining training input parameters can be the most objective specification of retina-independent parameters of the respective patient. These can be determined as part of a medical history and entered via an input device. This method is suitable, for example, for specifying age, illnesses or habits. However, the information relating to skin parameters, hair parameters and iris parameters is particularly meaningful. This can be obtained by assessment on a scale, for example by positioning on the well-known Fitzpatrick scale or on separate color or brightness scales for the individual parameters. However, technical measuring methods can also be used to determine the brightness or color of skin, hair and iris, for example through color photography or IR photography under defined lighting conditions.The brightness or color distribution in defined color channels can be objectively measured and specified as a parameter value. When evaluating photographs, a trained neural network can also be used for the evaluation. This network is trained with a number of photographs and associated parameterized ratings. The neural network trained in this way can also be identical to the trained neural network described above, meaning that the prediction model described above can also be trained to classify patient photographs into different skin / hair / iris types.

[0047] Thus, within the scope of the described method, it can also be provided that a skin parameter and / or a hair parameter and / or an iris parameter is determined by an optical measuring method, wherein in particular a color and / or a reflectivity of the skin, hair or iris of a patient is measured.

[0048] In addition to a method for forming a prediction model of the type described above, the invention also relates to a prediction model, in particular a programmed and / or trained data processing device which assigns one or more laser irradiation parameters for a retinal treatment to the current condition parameters of a patient and which is designed according to the method described above, in particular by training a neural network or a regression analysis or a multivariate analysis and forming an assignment function.

[0049] Such a prediction model can be in the form of a trained neural network, a data processing program, or a data processing device, and can be stored, for example, on a data storage medium. If such a data processing program is executed by a processor or a data processing system, the resulting method can assign laser irradiation parameters for a retinal treatment to a patient's current condition parameters.

[0050] The invention also relates to a method for determining laser irradiation parameters for treating the retina of a patient, in which, in a first step, current condition parameters of the patient are determined and, in a further step, one or more laser irradiation parameters for a retinal treatment are assigned to the condition parameters by means of a prediction model of the type described above, wherein the condition parameters comprise one, two or more than two retinal images of the patient, each obtained by an imaging method, wherein, in the case of more than one retinal image, the retinal images are obtained using different imaging methods and / or under different imaging conditions.The assignment of the laser irradiation parameters for a retinal treatment to the current condition parameters can be carried out by the prediction model described above, in particular the neural network that has been trained according to the method described above.

[0051] In addition, such a method can provide for the condition parameters to include at least one parameter determined independently of the patient's retina, in particular one or more retina-independent parameters, independent of the patient's eye and / or one or more retina-independent parameters of the patient's eye, further in particular a skin parameter or a hair parameter, an iris parameter, a parameter relating to one or more of the elements cornea, lens and vitreous body, the patient's age, a physiologically effective lifestyle habit, an occupation, regular medication intake or a pre-existing condition.

[0052] The retina-independent parameters of the patient's eye may include one or more of the following parameters: the iris color or brightness as well as the parameters that affect one or more of the elements cornea, lens and vitreous body, such as the absorption and / or scattering of radiation by these elements.

[0053] The parameters that relate to one or more of the elements cornea, lens and vitreous can, for example, describe whether and when a surgical operation was performed on one of the elements mentioned and / or one of the elements mentioned was replaced or modified. Many of the parameters mentioned can, for example, be entered manually into the system / prediction model, such as skin, hair or iris color or the fact whether the patient has an artificial lens or whether and what type of surgery has been performed on the eye. Some of the parameters can also be measured directly on the eye, such as iris color or brightness as well as the parameters that relate to one or more of the elements cornea, lens and vitreous.This could mean, for example, that a retina-independent parameter is determined by measurement, manually entered, or taken from patient data, whether the patient has an artificial lens and, optionally, also how long ago the corresponding surgery to replace their natural lens took place. It has been shown that this parameter has a particular influence on the absorption and / or scattering of the treatment radiation before it reaches the retina. Furthermore, this influence also applies when taking retinal images, so that the scattering and absorption of radiation reflected in the retina and subsequently recorded, for example, by an imaging system, is even doubly influenced by this scattering and absorption.

[0054] In general, when using the prediction model, optional retina-independent parameters, further optionally eye-independent parameters and / or retina-independent parameters of the patient's eye such as the properties of the iris and / or the cornea and / or the lens and / or the vitreous body can be determined, measured or entered and taken into account.

[0055] Consideration of the retina-independent parameters of the eye may be particularly useful, as these can be measured directly on the patient's eye.

[0056] These parameters can then be combined with one of the other parameters such as hair color or skin color as input for the system to be trained.

[0057] The scattering and / or absorption of radiation in the cornea and / or the lens and / or the vitreous body can also be determined, for example, when using the predictive model to determine irradiation parameters by measuring the reflectivity of various retinal regions. Retinal regions known to have different reflectivities are selected for this purpose, such as the optic disc, blood vessels carrying arterial blood, blood vessels carrying venous blood, and other retinal regions. If the intensity of reflected radiation is measured in two or more of these retinal regions and the reflectivity ratios in these regions are known, the radiation loss due to scattering and absorption en route through the eye can be calculated as an offset.

[0058] In general, the laser irradiation parameters to be determined relate to the irradiation parameters of the radiation source / treatment laser in active operation during irradiation of the retinal areas to be treated, i.e., during the phases in which radiation is actually emitted. The laser irradiation parameters to be determined can include, for example, laser power, maximum irradiation duration at a laser spot, laser power density, each potentially associated with a duty cycle, or the accumulated amount of energy delivered to the retinal tissue at a spot by the laser intensity. These parameters should be specifically selected so that laser irradiation with these parameters either does not cause permanent tissue damage to the retina or results in a precisely calculated tissue change.

[0059] One or more of the following imaging techniques can be used to create current retinal images used as current state parameters, which can also be used to generate training data sets for training the prediction model described above:

[0060] Photographic color image recording or multiple color image recordings under multiple sets of lighting conditions, for example lighting intensities or spectra, photographic infrared image (IR photography), also if necessary multiple images under different lighting conditions, for example lighting intensities or spectra, OCT-B-scan (depth section image of an image created with optical coherence tomography), BAF (Blue Autofluorescence, fluorescence image without additional dye, excitation wavelength 488nm), scanning laser ophthalmoscopy, OCT+SLO IR (optical coherence tomography in conjunction with IR-scanning laser ophthalmoscopy, a confocal imaging technique in the infrared range), FAG (Fluorescence Angiography, fluorescence image with dye fluorescein, excitation wavelength 488nm).

[0061] Particularly advantageous are imaging methods that can be performed simply, quickly, and without the assistance of specialist personnel, using easy-to-use, inexpensive equipment, such as color and infrared photography. The BAF method is also quick and easy to perform, as it does not use a contrast agent. Two of the last three methods can also be combined well. In principle, a single retinal image taken from the above-mentioned retinal images can be sufficient; even in this case, the last three methods are particularly inexpensive and easy to evaluate.

[0062] Pigmentation intensity or density can also be used as a skin, hair, and iris parameter to determine a patient's current condition, as this correlates to a certain extent with the retinal pigment intensity. Pigment intensity can be determined, for example, using a reflectivity measurement. However, the skin parameter, like the hair or iris parameters, can also be determined using a color measurement, for example, by recording the intensities in multiple color channels, such as the RGB system.In this case, it can further be specifically provided that a skin parameter and / or a hair parameter and / or an iris parameter of the patient is determined by an optical measuring method, wherein in particular a color and / or a reflectivity of the skin, hair or iris of the patient is measured and / or to determine the condition parameters in each case a photographic image, for example a photographic image, in particular a color image or IR image or live view of the skin, hair or iris of the patient is analyzed and the color is analyzed in a color space, for example in the RGB or HSV color space, based on the intensities in the respective color channels, for example evaluated by the prediction model.

[0063] A physiologically effective lifestyle habit can be, for example, the habit of smoking, regular alcohol consumption or other drug use or being overweight, while pre-existing conditions can include, for example, diabetes, obesity and high blood pressure.

[0064] The combination of a parameter determined independently of the patient's retina and at least one retinal image as a current condition parameter of a patient allows objective, yet quick and easy data collection.

[0065] An example of a specific selection of patient parameters that has proven to be well-qualified and significant when using the prediction model to determine radiation parameters includes one or more retinal images, the patient's age (or a life stage, if applicable), and a retina-independent eye parameter, in particular a parameter that describes the status of the eye lens, i.e. whether it is an endogenous or artificial lens and / or whether and when certain eye operations have been performed. These parameters can be made available to the prediction model before treatment by measuring a patient's retina or taking an image of the retina and by entering known patient data or transferring them from a database. In many cases, this data alone allows the prediction model to successfully determine promising radiation parameters.These irradiation parameters can then be applied in a subsequent treatment.

[0066] In addition, a scaling function can be used to take into account the relationship between the illuminance and sensitivity of the imaging system / camera when developing the prediction model, on the one hand, and the actual application of the measurement data for the patient, on the other. The prediction model can provide the illuminance and sensitivity data of the imaging system that were used in developing the prediction model.

[0067] A further embodiment of the method described above can provide that areas are determined from at least one retinal image of the patient and are excluded from irradiation for a subsequent laser treatment, the reflectivity of which deviates from an average reflectivity of the retina by at least 50%, in particular is more than 50% higher than the average reflectivity of the retina or 50% lower than the average reflectivity of the retina.

[0068] It is assumed that the areas of the retina where reflectivity is significantly higher or lower than in the remaining areas are the areas that are practically unpigmented and / or do not require treatment. When using color fundus photography or infrared photography, these are, in particular, the optic nerve head, which has high reflectivity, and the areas above the major blood vessels that supply the retina, which have significantly lower reflectivity than the remaining areas of the retina (the latter due to the radiation-absorbing hemoglobin). When using other imaging techniques, the contrast may appear different.In an image obtained by fluorescence angiography, the areas of the blood vessels appear clearly, i.e. at least 50% brighter than the other areas of the retina, whereas when using autofluorescence, these areas appear significantly darker than the other areas of the retina.The described method for determining laser irradiation parameters can also be designed by determining the variance of the reflectivity values ​​from the patient's retinal image, the difference of which from the average reflectivity differs by less than 50%, and from the variance a correction value for the laser irradiation parameters determined by the prediction model is determined and / or by determining a loss parameter from the reflectivity determined on the nerve head, which describes the losses due to absorption and scattering of light in the vitreous body of the eye and that this loss parameter is taken into account when determining the laser irradiation parameters.

[0069] By eliminating the areas whose average reflectivity is more than 50% higher than the remaining areas, the non-pigmented areas of the retina, described above, are excluded from the calculation. By determining the reflectivity variance in the remaining areas, a correction value for the laser irradiation parameters can be obtained, thereby increasing safety against harmful laser effects.

[0070] From a reflectivity value measured in the area of ​​the nerve head, the intensity loss that occurs as a laser beam travels through the vitreous body of the eye to the retina and back through the vitreous body can be calculated, taking into account residual retinal absorption, which is independent of pigmentation. This loss is the sum of the absorption losses in the vitreous body and the scattering, both of which depend, among other things, on the health of the eye, particularly the presence of cataracts. If there is a loss of intensity due to these effects, the laser intensity can be increased during treatment to correct the loss.

[0071] The losses of the laser radiation on its way through the interior of the eye to the retina can also be determined by reflectivity measurements.

[0072] The formula for absorption A is:

[0073] A= 1- (ReflMAc / REfloNH), where Refl MAC is the reflection intensity of the macula and

[0074] REfl oNH represents the reflection intensity of the optic nerve head. Alternatively, a formula can be used to calculate absorption by comparing the reflectivity of the retina in the macula region with and without blood vessels in the background.

[0075] When measuring retinal reflectivity, drusen, i.e., deposits beneath the RPE (retinal epithelium), must also be taken into account. Patients with macular degeneration have an increased incidence of drusen in the retina, which affects the backscattering of light. Therefore, when analyzing retinal reflectivity to determine the radiation intensity required for treatment, and also when measuring reflectivity to determine the absorption of radiation on its way through the eye to the retina, measurements in the area of ​​drusen should be excluded, or the presence of drusen should be considered in the evaluation model.

[0076] Finally, the invention also relates to a device for the retinal treatment of patients by means of laser irradiation with at least one treatment laser and at least one device for creating a retinal image and with a processing device with a prediction model of the type described above, which assigns one or more laser irradiation parameters for controlling the treatment laser to at least one retinal image currently created by the device and in particular to an additional, currently determined state parameter.

[0077] The device for treating patients' retinas using laser irradiation is designed in a manner known per se and, with regard to the optical system used, can be structurally connected to one or two or more devices for creating a retinal image. This makes it easy to create a common reference system for the retina to be treated for measurement and treatment, and if necessary. It is also reliably possible to exclude certain individually detected areas of the retina, such as the primary blood vessels and the optic nerve head, from irradiation. Furthermore, such a system can also be used for observing the retina during treatment and, if necessary, for eye tracking, i.e., the observation and compensation of eye movements during treatment.

[0078] For example, a system for fundus color photography and / or IR photography and / or FAG (fluorescence angiography) can be connected to the treatment laser.

[0079] In addition, a measuring device for determining a skin parameter, a hair parameter, or an iris parameter can be integrated into the device. This device can, for example, comprise a color camera or IR camera or a color image scanner for recording an image of the patient's skin, hair / head hair / eyebrows, or iris, as well as an analysis device that assigns a condition parameter to an image by evaluating reflectivity or color. The camera or scanner can, for example, be integrated into a chamber that has an illumination device and a window in front of which the patient's skin, hair, or iris can be placed. The illumination can be variable in terms of intensity and / or color spectrum in order to obtain additional information by calculating the difference or quotient of different images under different lighting conditions.

[0080] In the following, the invention is shown and explained using exemplary embodiments in figures of a drawing.

[0081] This shows

[0082] Figure 1: a representation of the training process for a prediction model,

[0083] Figure 2: a representation of the use of the trained prediction model to determine laser irradiation parameters, as well as

[0084] Figure 3: Another illustration of the training of the prediction model. Figure 1 schematically shows a process for training a prediction model, specifically for training a neural network 1 with training input data 6a, 6b, 6c, 7a, 7b, 7c and training result data 8a, 8b, 8c, each of which is determined from a larger group of individuals 9a, 9b, 9c and provided to the neural network 1 or a regression analysis device for training or evaluation.

[0085] As training input data, recording data from one, two, or more recordings 7a, 7b, 7c, in particular an image recording of the retina of the respective person 9a, 9b, 9c, can initially be provided. Possible recording modes include, for example, fundus color photographs or IR photographs, each in one or more lighting situations, where a lighting situation can be defined by a lighting intensity, an illumination angle, and a specific illumination spectrum.Other possible imaging techniques include an OCT-B-scan (depth-section image of an optical coherence tomography image), BAF (blue autofluorescence, fluorescence image without additional dye, excitation wavelength 488 nm), OCT+SLO IR (optical coherence tomography combined with IR-scanning laser ophthalmoscopy, a confocal imaging technique in the infrared range), and FAG (fluorescence angiography, fluorescence image with fluorescein dye, excitation wavelength 488 nm). In Figure 1, the imaging devices for various images are symbolically represented by cameras 13, 14, which are aligned to one another so that reference points of the various images can be related to one another.

[0086] Additionally, skin and / or hair and / or iris parameters, as well as other health-related parameters such as age, certain lifestyle habits, and pre-existing conditions, can be determined as further training input parameters 6a, 6b, and 6c and made available to the neural network or a regression analysis as training input parameters. The skin and / or hair and / or iris parameters can be determined using a measuring device 11, for example, a color camera, an IR camera, or a hyperspectral camera, each with an evaluation device. These and additional parameters can also be entered using a data input device 12, for example, a keyboard or a speech recognition device using a microphone.

[0087] The training input parameters mentioned above initially provide the prediction model with the data basis for a comprehensive description of the respective person’s condition.

[0088] The determined threshold data for the laser irradiation parameters of the respective individual person 9a, 9b, 9c are linked to the aforementioned data / training input parameters, which form the training result parameters 8a, 8b, 8c. These threshold data are determined for each person by delivering titration shots with different laser irradiation parameters, i.e., with different intensities, to their retina using a laser. For each person, it is individually evaluated which of the titration shots caused permanent tissue changes. This way, the threshold or laser threshold is individually determined for each person 9a, 9b, 9c and made available to the prediction model as a training result parameter.

[0089] To detect tissue changes, 15 different recording modes 15a, 15b, 15c can be used by an imaging device to image the retina of the respective person. In principle, all of the above-mentioned recording modes can be used for this purpose, or several in combination. The blue autofluorescence method has proven to be particularly sensitive. This can be combined with one or more of the other methods mentioned in order to detect tissue changes as sensitively as possible in each individual case. The results of the titration shots can be assessed by experts in various images created using different recording modes. For example, the threshold value that led to a detectable tissue change in any of the images in the titration shot with the lowest intensity can be used as a standard.It is also possible to wait a certain amount of time, for example one or more weeks after the titration shots, before examining the retina.

[0090] In principle, however, it is also possible to determine which of the TI acquisition modes is the most sensitive for the majority of test subjects, and this mode can then be used to assess all subjects. Tissue changes can also be detected using digital image processing.

[0091] Instead of a neural network 1, a regression analysis can also be performed using the acquired and collected data, and the influences of individual features of the training input parameters on the laser threshold can be determined and weighted. Using the regression analysis, an algorithm can be trained / educated, which then serves as a predictive model.

[0092] Figure 2 schematically shows a system and method for using the prediction model 1 in the specific treatment of patient 5. Patient 5 uses a recording device 18, 19 that creates an image 4a, 4b, 4c of his or her retina or parts of the retina.

[0093] Potentially, further recording devices 16 and / or 17, for example in the form of a color camera, IR camera, or hyperspectral camera, capture the reflectivity, brightness, or color of the patient's skin, hair, or iris, which represent examples of condition data 3a, 3b, 3c determined independently of the patient's retina. In the simplest case, this can also be done by a light-sensitive sensor and an illumination device arranged in a common measuring chamber, wherein the chamber has a window and the patient's skin, hair, or eye is positioned in front of the window.

[0094] The recording device can, for example, have only a single technical device for recording a single recording mode, for example from the list above, such as a fundus color or infrared camera or a device for blue autofluorescence recording. In principle, the recording device can also have only one camera for recording different recording modes under different lighting conditions, since in this case too the technical effort remains very low. However, two or more cameras can also be combined to record different recording modes. In principle, it can be provided that when recording the current status data of a patient 5 before his treatment, images with fewer different recording modes are taken and used for analysis than for training the prediction model 1, 2 used.

[0095] The current status data acquired by the local devices 16, 17, 18, 19 can be further processed at the treatment site, but they can also be further processed using a distributed computer network, for example, with a remote server, whereby the prediction model 1, 2 can also be located at the treatment site or remotely from it. The prediction model 1, 2 assigns one or more laser irradiation parameters to the data / current status data of the patient 5 acquired by the local devices 16, 17, 18, 19, for example, laser intensities for a tissue-altering treatment and an upper limit / threshold for the laser intensity at which tissue alterations are reliably avoided.In this context, laser intensity refers to all parameters of the laser and the beam guidance that influence potential tissue damage, such as the laser power, the power density, the duty cycle, the dwell time of the laser on a spot, and the total energy introduced into a laser spot.

[0096] In principle, the determined laser threshold can be uniform for the entire retina to be treated, but it can also be location-dependent, for example, excluding the nerve head and primary blood vessels from treatment. Furthermore, depending on the retinal image used, certain individual areas of the retina can also be excluded.

[0097] It can also be planned that the variance / variability of the retinal pigmentation / absorption intensity is determined using the current retinal image, i.e., the extent of deviations from the average pigmentation / absorption intensity from a determined average value. From this variance, a correction value for the laser irradiation parameters is determined, which reduces the laser intensity in the event of high variance in order to avoid damage to the areas of high absorption on the retina if the retina as a whole is treated with non-location-dependent laser irradiation parameters. When determining the average value of the retinal pigmentation / absorption, the areas of the nerve head and the primary blood vessels can be excluded.

[0098] It is also possible to determine a loss factor from the measured reflectivity at specific retinal locations, such as the junction of the retina, compared to the remaining retinal areas. This loss factor represents the absorption and scattering of the laser radiation in the vitreous body of the eye. If a loss factor is detectable, the laser intensity for a non-location-dependent treatment can lead to a correction factor for the laser irradiation intensity that is greater than 1, for example, between 1 and 1.5.

[0099] As can be seen in Figure 2, the data / current status data of patient 5 acquired with the local devices 16, 17, 18, 19 are fed to the prediction model 1, 2, which uses them to determine laser irradiation parameters and sends them to a treatment device 20. This device can be located at the same location as the local devices 16, 17, 18, 19, so that after the acquisition of their current status data, patient 5 can be treated at the same location and, in principle, with the same device with which the current retinal images were created. This has the advantage that the imaging methods used for the retinal images can, in some cases, also be used for controlling, aligning, and directing the treatment laser.For example, the treatment laser can pass through the same optical elements, such as apertures and lenses, that are used to record current status data. A device 21 for eye tracking can also be provided and connected to at least one of the devices 18, 19, as indicated by arrow 23. This device 21 detects eye movements and appropriately tracks the treatment laser 22 in a conventional manner.

[0100] Figure 3 shows, in a more detailed representation, the creation of the prediction model 1, 2 with training data 6a, 6b, 6c, 7a, 7b, 15a, which are summarized in an integration step 24 and fed into the prediction model for training. The result of the prediction model, when analyzing an individual patient, is a "risk curve" 25, from which the laser irradiation parameters 10a, 10b, 10c can be derived depending on the desired or permissible probability of tissue damage / change. Before extracting specific laser irradiation parameters from the risk curve 25 for a treatment case, an optional approval step 26 can be provided, which is shown in Figure 2 and can be performed, for example, by an ophthalmologist.

[0101] In many cases, a selection of the above-mentioned parameters may be sufficient as state parameters for training the prediction model and also when using the model in the context of treatment, such as the recording / input of hair color, eye color, previous illnesses and operations performed on the eye so far, whereby optionally a practiced or predominantly practiced profession and / or information about an intensively practiced sport can be added.

[0102] Optionally, these parameters can also include information about whether the patient is or was a smoker or not.

[0103] The described system enables simple and inexpensive, yet safe and non-hazardous retinal treatment of patients.

Claims

Patent claims 1. Method for forming a prediction model (1, 2), in particular for training a neural network (1) or an algorithm (2), or for obtaining data for a regression analysis or a multivariate analysis to form an assignment function, wherein the prediction model, in particular the neural network, the algorithm or the assignment function, assigns one or more laser irradiation parameters (10a, 10b, 10c) for a retinal treatment to current state parameters (3a, 3b, 3c, 4a, 4b, 4c) of a patient (5), characterized in that as training data for a plurality of patients (9a, 9b, 9c) on the one hand as training input parameters at least one retinal image (7a, 7b, 7c) of the patient obtained by an imaging method and in particular at least one parameter (6a, 6b, 6c) determined independently of the retina of the patient, in particular a or several retina-independent,parameters independent of the patient's eye and / or one or more retina-independent parameters of the patient's eye, further in particular a skin parameter, a hair parameter, an iris parameter, a parameter relating to one or more of the elements cornea, lens and vitreous body, the patient's age, a physiologically effective lifestyle, an occupation, regular medication intake or a pre-existing condition, and on the other hand, as a training result parameter, a threshold value for the laser irradiation parameter(s) (8a, 8b, 8c), upon exceeding which permanent damaging tissue changes occur, are used.

2. Method according to claim 1, characterized in that for training for each patient at least two retinal images (7a, 7b, 7c) are used as training input parameters, which are different imaging techniques or under different technical recording conditions.

3. Method according to claim 1 or 2, characterized in that one or two of the following methods are used as imaging methods for determining the training input parameters (7a, 7b, 7c): Photographic color image, photographic infrared image, OCT-B-scan (depth section image of an image created with optical coherence tomography), BAF (blue autofluorescence, fluorescence image without additional dye, excitation wavelength 488nm), OCT+SLO IR (optical coherence tomography in conjunction with IR-scanning laser ophthalmoscopy, a confocal imaging procedure in the infrared range), FAG (fluorescence angiography, fluorescence image with dye fluorescein, excitation wavelength 488nm), or a scanning laser ophthalmoscope with one or more wavelengths, in particular the wavelengths intended for subsequent treatment.

4. Method according to one of claims 1 to 3, characterized in that the respective threshold value (8a, 8b, 8c) for the laser irradiation parameter(s) for each patient (9a, 9b, 9c) is determined by a series of titration shots on the retina of the patient with different laser irradiation parameters and a subsequent detection of tissue changes on the basis of several retinal images obtained by different imaging methods, wherein in particular the lowest threshold value determined with one of the imaging methods is used as a training result parameter for forming the prediction model, in particular the training of a neural network or for obtaining data for a regression analysis or a multivariate analysis for forming an assignment function.

5. Method according to one of claims 1 to 3, characterized in that the respective threshold value (8a, 8b, 8c) for the laser irradiation parameter(s) for each patient (9a, 9b, 9c) is determined by a series of titration shots on the retina of the patient with different laser irradiation parameters and a subsequent detection of tissue changes on the basis of several retinal images obtained by different imaging methods, wherein the threshold value determined by the imaging method which indicates the lowest threshold value for the most patients examined is used as the training result parameter for forming the prediction model, in particular the training of a neural network, for all patients examined.

6. Method according to one of claims 1 to 3, characterized in that the respective threshold value (8a, 8b, 8c) for the laser irradiation parameter(s), in particular for the laser power, the power density, the spot diameter, the pulse duration, the pulse number or the duty cycle or a combination of two or more of the said variables, is determined for each patient (9a, 9b, 9c) as a training result parameter by a series of titration shots on the retina of the patient with different laser irradiation parameters and a subsequent detection of tissue changes using optical coherence tomography in conjunction with IR-scanning laser ophthalmoscopy.

7. Method according to one of claims 1 to 6, characterized in that a power of the irradiation laser or a power density of the laser spot with otherwise fixed laser irradiation parameters or a total energy deposited over the irradiation time on a laser spot of a fixed size is selected as the laser irradiation parameter which varies during the titration shots and whose threshold value (8a, 8b, 8c) is determined as a training result parameter.

8. Method according to one of claims 1 to 7, characterized in that a skin parameter and / or a hair parameter and / or an iris parameter (6a, 6b, 6c) is determined by an optical measuring method, wherein in particular a color and / or a reflectivity of the skin, hair or iris of a patient is measured.

9. Prediction model, in particular programmed and / or trained data processing device which assigns one or more laser irradiation parameters (10a, 10b, 10c) for a retinal treatment to the current state parameters (3a, 3b, 3c, 4a, 4b, 4c) of a patient (5) and which is designed according to one of claims 1 to 8, in particular by training a neural network (1) or a regression analysis or a multivariate analysis and formation of an assignment function.

10. Method for determining laser irradiation parameters (10a, 10b, 10c) for treating the retina of a patient (5), characterized in that In a first step, condition parameters (3a, 3b, 3c, 4a, 4b, 4c) of the patient are determined and in a further step, one or more laser irradiation parameters (10a, 10b, 10c) for a retinal treatment are assigned to the condition parameters by means of a prediction model (1, 2) according to claim 9, wherein the condition parameters comprise one, two or more than two retinal images (4a, 4b, 4c) of the patient (5), each obtained by an imaging method, wherein in the case of more than one retinal image, the retinal images are obtained using different imaging methods and / or under different imaging conditions.

11. Method for determining laser irradiation parameters (10a, 10b, 10c) according to claim 10, characterized in that the state parameters (3a, 3b, 3c, 4a, 4b, 4c) comprise at least one parameter (6a, 6b, 6c) determined independently of the retina of the patient, in particular one or more retina-independent parameters determined by the eye parameters independent of the patient and / or one or more retina-independent parameters of the patient's eye, more particularly a skin parameter or a hair parameter, an iris parameter, a parameter relating to one or more of the elements cornea, lens and vitreous body, the age of the patient, a physiologically effective lifestyle, an occupation, regular medication intake or a pre-existing condition.

12. The method according to claim 11, characterized in that a skin parameter and / or a hair parameter and / or an iris parameter (3a, 3b, 3c) of the patient (5) is determined by an optical measuring method, wherein in particular a color and / or a reflectivity of the skin, hair or iris of the patient is measured and / or to determine a condition parameter (3a, 3b, 3c) a photographic image of the skin, hair or iris of the patient is evaluated by the prediction model.

13. The method according to claim 10, 11 or 12, characterized in that the areas with high reflectivity are determined from at least one retinal image (4a, 4b, 4c) of the patient and are excluded from irradiation for a subsequent laser treatment, wherein an area with high reflectivity is defined in particular as an area in which the reflectivity is at least 50% higher than the average reflectivity value of the retina.

14. Method according to claim 10, 11, 12 or 13, characterized in that the variance of the reflectivity values, whose difference from the average reflectivity differs by less than 50%, is determined from the retinal image (4a, 4b, 4c) of the patient, and a correction value for the laser irradiation parameters (10a, 10b, 10c) determined by the prediction model (1, 2) is determined from the variance and / or that a loss parameter is determined from the reflectivity determined on the nerve head, which describes the losses due to absorption and scattering of light in the vitreous body of the eye, and that this loss parameter is taken into account when determining the laser irradiation parameters (10a, 10b, 10c). (Comment: here I wanted to The first part expresses that when determining the variance, the veins and the nerve head should, of course, be excluded; that is, it only concerns the variance of the areas actually to be irradiated. I cannot estimate whether the 50% limit is meaningful for this. In the second part of the claim, I wanted to include the absorption measurement without introducing an additional (chargeable) claim.

15. Device for the retinal treatment of patients by means of laser irradiation with at least one treatment laser (22) and at least one device (18, 19) for creating a retinal image and with a processing device with a prediction model (1, 2) according to claim 9, which at least one retinal image (4a, 4b, 4c) currently created by the device and in particular an additional, currently determined state parameter (3a, 3b, 3c) one or more laser irradiation parameters (10a, 10b, 10c) for Assigns control for the treatment laser.

Images