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WO2019169166A1 - Progression de champ visuel - Google Patents

Progression de champ visuel Download PDF

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Publication number
WO2019169166A1
WO2019169166A1 PCT/US2019/020106 US2019020106W WO2019169166A1 WO 2019169166 A1 WO2019169166 A1 WO 2019169166A1 US 2019020106 W US2019020106 W US 2019020106W WO 2019169166 A1 WO2019169166 A1 WO 2019169166A1
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WO
WIPO (PCT)
Prior art keywords
visual field
archetypes
visual
weighted
patient
Prior art date
Application number
PCT/US2019/020106
Other languages
English (en)
Inventor
Mengyu WANG
Lucy Q. SHEN
Louis R. Pasquale
Tobias ELZE
Original Assignee
The Schepens Eye Research Institute, Inc.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by The Schepens Eye Research Institute, Inc. filed Critical The Schepens Eye Research Institute, Inc.
Publication of WO2019169166A1 publication Critical patent/WO2019169166A1/fr

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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
    • A61B3/02—Subjective types, i.e. testing apparatus requiring the active assistance of the patient
    • A61B3/024—Subjective types, i.e. testing apparatus requiring the active assistance of the patient for determining the visual field, e.g. perimeter types
    • 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
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/48—Other medical applications
    • A61B5/4842—Monitoring progression or stage of a disease
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/48—Other medical applications
    • A61B5/486—Biofeedback
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • A61B5/7267—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7271—Specific aspects of physiological measurement analysis
    • A61B5/7275—Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/74—Details of notification to user or communication with user or patient; User input means
    • A61B5/7475—User input or interface means, e.g. keyboard, pointing device, joystick

Definitions

  • the present disclosure relates generally to visual field (VF) progression, and more particularly, to detecting and/or determining visual field progression based on special pattern analysis.
  • a visual field test is an eye examination on a patient that can detect various dysfunctions or issues in central and peripheral vision that may be caused by (and thus be a sign of) various medical conditions such as glaucoma, stroke, pituitary disease, etc.
  • Visual field testing can be performed in a clinic on a machine called a perimeter by keeping a gaze of a patient fixed and presenting various objects at various places within the patient’s visual field.
  • a Humphrey visual field analyzer can be used, which is a machine used by clinicians to measure the visual field of a patient. For example, it is used by optometrists, orthoptists, and ophthalmologists.
  • the HFA produces a standard analyzer printout that includes a variety of information about the eye being tested at that point in time. Clinicians then use this information to attempt to diagnose various medical conditions based on outputs from the machine, various expected patterns that may indicate one condition over another, etc.
  • a method for determining visual field progression of a patient including testing, on a visual device, a present visual field of the patient.
  • the method also includes decomposing, by a processor, the present visual field and a plurality of past visual fields of the patient into a plurality of weighted visual field archetypes.
  • the method further includes determining, by the processor, whether each of the plurality of weighted visual field archetypes is increasing or decreasing in size relative to itself across the plurality of past visual fields and the present visual field.
  • the method also includes determining, by the processor, if the patient is experiencing a loss of visual field based on the determination of whether each of the plurality of weighted visual field archetypes is increasing or decreasing.
  • the plurality of weighted visual field archetypes can include 16 weighted visual field archetypes. Determining whether each of the plurality of weighted visual field archetypes is increasing or decreasing can include using linear regression. Determining whether each of the plurality of weighted visual field archetypes is increasing or decreasing can include calculating a slope of each of the plurality of weighted visual field archetypes over time.
  • the plurality of weighted visual field archetypes can also include one weighted visual field archetype configured to represent a normal vision field. Determining if the patient is experiencing the loss of visual field can include finding a decreasing slope of the one weighted visual field archetype configured to represent the normal vision field.
  • the plurality of weighted visual field archetypes can include one or more weighted visual field archetypes configured to represent a loss of vision field. Determining if the patient is experiencing the loss of visual field can also include finding an increasing slope of at least one of the one or more weighted visual field archetypes configured to represent the loss of vision field.
  • decomposing the present visual field and the plurality of past visual fields of the patient into the plurality of weighted visual field archetypes can include determining a percentage of each of the plurality of weighted visual field archetypes present in the present visual field and the plurality of past visual fields of the patient. The method can also include using unsupervised machine learning.
  • the processor can be part of the visual device, and the processor can be included in a remote computing device.
  • the visual device can be a Humphrey visual field analyzer.
  • the method can also include testing, on the visual device, a plurality of visual fields of the patient over a period of time.
  • a visual device for determining visual field progression of a patient that includes at least one input, at least one display, at least one sensor, at least one memory, and at least one processor.
  • the visual device is configured to test a present visual field of the patient; to decompose, by the processor, the present visual field and a plurality of past visual fields of the patient into a plurality of weighted visual field archetypes; to determine, by the processor, whether each of the plurality of weighted visual field archetypes is increasing or decreasing in size relative to itself across the plurality of past visual fields and the present visual field; and to determine, by the processor, if the patient is experiencing a loss of visual field based on the determination of whether each of the plurality of weighted visual field archetypes is increasing or decreasing.
  • the device can have various embodiments.
  • the plurality of weighted visual field archetypes can include 16 weighted visual field archetypes.
  • the visual device can also be further configured to use linear regression.
  • the visual device can be further configured to calculate a slope of each of the plurality of weighted visual field archetypes over time.
  • the plurality of weighted visual field archetypes can include one weighted visual field archetype configured to represent a normal vision field.
  • the plurality of weighted visual field archetypes can include one or more weighted visual field archetypes configured to represent a loss of vision field.
  • the visual device can also be further configured to determine a percentage of each of the plurality of weighted visual field archetypes present in the present visual field and the plurality of past visual fields of the patient.
  • the device can be further configured to use unsupervised machine learning.
  • the visual device can be further configured to test a plurality of visual fields of the patient over a period of time.
  • FIG. 1 illustrates an embodiment of a visual field analyzer
  • FIG. 2 illustrates an analyzer printout produced by the visual field analyzer of FIG. 1;
  • FIG. 3 illustrates an example diagrammatic view of a device architecture
  • FIG. 4 illustrates one embodiment of a procedure for determining visual field progression
  • FIG. 5 illustrates an embodiment for performing the procedure for determining visual field progression of FIG. 4
  • FIG. 6 illustrates a possible data exchange step of the procedure for determining visual field progression of FIG. 4
  • FIG. 7 illustrates an embodiment of a 24-2 VF plot
  • FIG. 8 illustrates total deviation from the 24-2 VF plot of FIG. 7;
  • FIG. 9 illustrates an example of 16 visual field archetypes
  • FIG. 10 illustrates an example of visual field decomposition into weighted visual field archetypes
  • FIG. 11 illustrates an exemplary patient’s visual field information determined through the process discussed herein;
  • FIG. 12 illustrates decomposed visual field test results performed on patients using the process provided herein.
  • FIG. 13 illustrates additional decomposed visual field test results performed on the same patients as in FIG. 12.
  • the term“and/or” includes any and all combinations of one or more of the associated listed items.
  • the term“coupled” denotes a physical relationship between two components whereby the components are either directly connected to one another or indirectly connected via one or more intermediary components.
  • the term“patient” or other similar term as used herein is inclusive of any subject— human or animal— on which an ocular assessment could be performed.
  • the term“user” as used herein is inclusive of any entity capable of interacting with or controlling a device.
  • The“user” may also be the“patient,” or the“user” and“patient” may be separate entities, as described herein.
  • one or more of the below methods, or aspects thereof, may be executed by at least one processor.
  • the processor may be implemented in various devices, as described herein.
  • a memory configured to store program instructions may also be implemented in the device(s), in which case the processor is specifically programmed to execute the stored program instructions to perform one or more processes, which are described further below.
  • the below methods may be executed by a specially designed device, a mobile device, a computing device, etc.
  • the methods, or aspects thereof, of the present disclosure may be embodied as non-transitory computer readable media on a computer readable medium containing executable program instructions executed by the processor.
  • the computer readable mediums include, but are not limited to, ROM, RAM, compact disc (CD)- ROMs, magnetic tapes, floppy disks, flash drives, smart cards and optical data storage devices.
  • the computer readable recording medium can also be distributed in network coupled computer systems so that the computer readable media is stored and executed in a distributed fashion, e.g., by a telematics server or a Controller Area Network (CAN).
  • a telematics server or a Controller Area Network (CAN).
  • CAN Controller Area Network
  • results of a test are provided to identify normal vision or a type of vision defect, condition, disease state, etc. in an eye of a patient.
  • the results provide information regarding various defects, for example various locations of any disease processes or lesion(s) throughout the visual pathway of a patient. These results then guide the clinician to diagnose any conditions affecting the patient’s vision.
  • results from the visual field test can be stored and used for monitoring the progression of vision loss and the patient’ s condition.
  • a clinician still must perform a subjective, personal analysis of the test results over a period of time, such as over a period of months and/or years, which might be inaccurate or miss important considerations.
  • a clinician may perform a visual field test on a patient using the visual field analyzer 2 over several years. Each result is generated on an analyzer printout 4 that provides a variety of standard information, as illustrated in FIG. 2.
  • the information provided relates generally to reliability indices 10, numerical displays 20, grey scale 30, total deviation 40, probability display 50, pattern deviation 60, global indices 70, glaucoma hemifield test 80, and visual field index 90.
  • a clinician examines the results, especially the pattern of vision loss provided on the pattern deviation 60 plot, in an attempt to diagnose the type of vision loss present, if any. This identification can then be used by the clinician along with other clinical findings in the diagnosis of certain conditions.
  • the clinician looks at the pattern deviation 60 plot for typical patterns or known examples of visual field loss. Based on the types of vision loss subjectively identified by the clinician, the clinician then attempts to diagnose various associated conditions that commonly produce the identified patterns of visual field loss.
  • FIG. 3 illustrates an example diagrammatic view of an exemplary device architecture according to embodiments of the present disclosure.
  • a device 109 may contain multiple components, including, but not limited to, a processor (e.g., central processing unit (CPU) 110, a memory 120, a wired or wireless communication unit 130, one or more input units 140, and one or more output units 150.
  • a processor e.g., central processing unit (CPU) 110
  • memory 120 e.g., central processing unit (CPU) 110
  • wired or wireless communication unit 130 e.g., a wired or wireless communication unit 130
  • input units 140 e.g., input units 140
  • output units 150 e.g., wired or wireless communication unit
  • FIG. 3 is simplified and provided merely for demonstration purposes.
  • the architecture of the device 109 can be modified in any suitable manner as would be understood by a person having ordinary skill in the art, in accordance with the present claims.
  • the device architecture depicted in FIG. 3 should be treated as exemplary only and should not be treated as limiting the scope of the present disclosure.
  • the processor 110 is capable of controlling operation of the device 109. More specifically, the processor 110 may be operable to control and interact with multiple components installed in the device 109, as shown in FIG. 3.
  • the memory 120 can store program instructions that are executable by the processor 110 and data. The process described herein may be stored in the form of program instructions in the memory 120 for execution by the processor 110.
  • the communication unit 130 can allow the device 109 to transmit data to and receive data from one or more external devices via a communication network.
  • the input unit 140 can enable the device 109 to receive input of various types, such as audio/visual input, user input, data input, and the like.
  • the input unit 140 may be composed of multiple input devices for accepting input of various types, including, for instance, one or more cameras 142 (i.e., an“image acquisition unit”), touch panel 144, microphone, sensors 146, one or more buttons or switches, and so forth.
  • the input devices included in the input 140 may be manipulated by a user.
  • the term“image acquisition unit,” as used herein, may refer to the camera 142, but is not limited thereto.
  • the output unit 150 can display information on the display screen 152 for a user to view.
  • the display screen 152 can also be configured to accept one or more inputs, such as a user tapping or pressing the screen 152, through a variety of mechanisms known in the art.
  • the output unit 150 may further include a light source 154.
  • the device 109 can thus be programmed in a manner allowing it to perform the techniques for determining visual field progression described herein.
  • the various approaches provided herein can provide both status outcome and spatial patterns of visual field progression to assist clinicians in assessing progression in patients as opposed to only providing global assessments of functional deterioration or requiring a clinician’s judgement of visual field progression to heavily rely on subjective examination of visual field spatial patterns.
  • a clinician can test a patient’s visual field using a variety of testing means, such as by using a variety of visual devices such as automated standard peri merry like a Humphrey visual field analyzer.
  • the results can be decomposed by a processor, such as the processor in the device, into various visual field archetypes (AT), such as 16 weighted visual field archetypes.
  • a processor such as the processor in the device
  • various visual field archetypes such as 16 weighted visual field archetypes.
  • numbers of visual field archetypes can be used depending on the applicable eye condition(s) and scenario(s) of the patient(s), such as between 1 and 40, or such as 5, 10, 15, 20, 25, 30, etc.
  • Results from multiple time periods for the same patient can then be analyzed, for example by a processor in the device or a separate computing device, to determine if there is visual field loss over time (such as comparing a first month to a second month or a first year to a second year) and, if so, if the loss matches one or more predetermined patterns representative of one or more disease conditions.
  • a processor in the device or a separate computing device to determine if there is visual field loss over time (such as comparing a first month to a second month or a first year to a second year) and, if so, if the loss matches one or more predetermined patterns representative of one or more disease conditions.
  • the pattern over time and any change or shift in the pattern can be identified.
  • a clinician 102 can test a visual field of a patient 104 using a visual device 106, such as a Humphrey visual field analyzer.
  • a visual device 106 such as a Humphrey visual field analyzer.
  • it can be determined if the results of the visual field test of the patient 104 are reliable.
  • test can be repeated.
  • the reliability of the test can be determined by the device 106, for example by identifying false positives and/or false negatives. If the test is reliable, analysis of the results can proceed.
  • the information can either be kept on the device 106 and analyzed directly or can be transferred to another device 108 for analysis, such as a computing device, as illustrated in FIG. 6.
  • the results of the test can be decomposed by processor(s) in one or both of the device(s) 106, 108.
  • the results can take a variety of forms, such as various visual field plots like an illustrated 24-2 visual field plot 302 shown in FIG. 7 and similar to the analyzer printout 4 produced by a Humphrey visual field analyzer.
  • a variety of other visual field plots can be used, and the present application is not limited thereto.
  • the visual field plot 302 the total deviation from the 24-2 visual field plot can be used by processor(s) on the device(s) 106, 108 to analyze visual field progression of the patient, as illustrated in FIG. 8.
  • the results of the test can be decomposed into one or more visual field archetypes, such as into 16 visual field archetypes illustrated in FIG. 9. As mentioned above, however, various different numbers of visual field archetypes can be used.
  • the results can be decomposed into 16 visual field archetypes previously identified through a variety of techniques, such as by unsupervised machine learning. In FIG. 9, for example, a normal archetype is represented in AT1, and 15 visual field loss archetypes are represented in AT2-AT16.
  • the results can also include various mean deviation or defect (MD) values and/or various pattern standard deviation (PSD) values.
  • MD can be a weighted mean value of all test points in a total deviation plot
  • PSD can be a metric that indicates a difference in a sensitivity of adjacent tested points.
  • the 16 visual field archetypes illustrated in FIG. 9 were generated using visual field information from glaucoma patients. However, the same archetypes can be used for a variety of other conditions. Alternatively, new archetypes can be identified using visual field information from other conditions, such as stroke, pituitary disease, age-related macular degeneration, cataract, etc., by identifying a new set of archetypes to be used.
  • the visual field of the patient from the current test is decomposed along with the visual field of the patient from a plurality of previous visual field tests.
  • a patient may participate in a visual field test once a year, and each year the results are saved and added to current and future analyses. While the exact time period may vary, the process discussed herein takes in and performs an analysis using each visual field from a series of at least two visual fields from one patient, for example patient 104, over a period of time, such as over a plurality of days, weeks, months, years, etc.
  • the process can be conducted over 2 years, 3 years, 4, years, 5 years, 6 years, 7 years, 8 years, 9 years, 10 years, etc.
  • each visual field from a series of visual fields from the patient 104 are decomposed into the weighted combination of 16 visual field archetypes that have been previously identified by unsupervised machine learning.
  • visual field decomposition into weighted visual field archetypes can be illustrated, showing illustrative archetype coefficients, the visual field measurement, the computational decomposition of the analysis, and the total deviation.
  • the illustrative archetype coefficient can represent, for example, the decomposition of the series of visual fields for the patient 104 into a single visual field measurement.
  • the computational decomposition can represent, for example, a breakdown or analysis of what archetypes are present in the decomposition of the series of visual fields and approximate percentages of one or more archetypes.
  • FIG. 10 illustrates a computational decomposition for the patient 104 of 43.7% AT1 (or the normal AT), 51.5% of AT3 (a loss AT), and 4.8% of AT4 (another loss AT).
  • AT1 or the normal AT
  • 51.5% of AT3 a loss AT
  • AT4 another loss AT
  • the exact values will vary depending on the patient and the condition.
  • a detailed analysis of the visual field of the patient is possible using the process discussed herein.
  • the possible progression of a condition and/or loss of visual field can be determined.
  • the slope over time of the visual field archetypes can be calculated by processor(s) of the device(s) 106, 108, such as over days, weeks, months, or years.
  • the slope over time of the 16 archetype weights can be calculated.
  • Whether the possible condition and/or loss of visual field of the patient 104 is progressing or not can be determined at step 500 by analyzing if the normal AT (represented by AT1) has substantially decreased over the series of visual fields (for example, such that AT slope > 0.01 / year; p value ⁇ 0.01) or if any of the 15 visual field loss ATs (represented by AT2-AT16 and representing loss of visual field associated with one or more visual conditions) have substantially increased (for example, such that AT slope ⁇ - 0.01 / year; p value ⁇ 0.01). While specific values are provided herein, these are illustrative and non- limiting.
  • a variety of different values can be used depending on the condition(s) being tracked, such as by looking for a slope of -1.0, -0.1, -0.01, -0.001, 0, 0.001, 0.01, 0.1, 1.0, etc. and/or a p value of -1.0, -0.1, -0.01, -0.001, 0, 0.001, 0.01, 0.1, 1.0, etc.
  • analysis can be performed by processor(s) of the device(s) 106, 108 to determine if the normal archetype has gotten worse or declined in prominence (by decreasing) or if any of the archetypes identifying visual field loss that can correspond to one or more visual conditions has improved in prominence (by increasing), either of which would indicate a worsening of the visual field of the patient 104.
  • This determination can be made using linear regression.
  • the processor(s) can calculate the slopes b of the 16 visual field archetype weights, and if the normal AT slope b ⁇ decreases and/or if any of the loss AT slopes b2-16 increases, the patient 104 is progressed, representing a loss of visual field and/or worsening of the patient’s condition. If the normal AT slope b ⁇ does not decrease and/or if any of the loss AT slopes b2-16 do not increase, the patient 104 is not progressed, representing no change in the patient’s visual field and/or condition. If no visual condition or disease state has been previously identified for the patient 104, this lack of progression can indicate that no disease state is present.
  • the patient 104 can have eight visual fields resulting from 7.2 years of follow-ups with the clinician 102. Again, however, a variety of numbers of visual fields can be used over a variety of time periods.
  • the visual fields for each visit can be represented in VF1-VF8.
  • the slope over time of the 16 archetype weights discussed herein can be calculated, and a determination can be made regarding whether the patient is progressed or not progressed.
  • the slope for AT3 (which is a loss VF) equals 0.066 / year, and the p ⁇ 0.006.
  • AT3 which is a loss VF
  • the same process of analysis can be applied to any AT values depending on the specific patient results and/or the specific condition being analyzed.
  • unsupervised machine learning can be used to quantify each visual field into weighted sub-patterns, and linear regression can be used to detect whether and which sub-pattems of visual field progresses over the course of a period of time (for example, over several years).
  • An unsupervised machine learning based progression detection algorithm is used that provides both progression global outcomes and a progression spatial pattern.
  • the results over years can be combined through this process to show the pattern of development of loss of visual field and/or a condition.
  • the ability to provide both progression global outcomes and a progression spatial pattern can lead to more accurate and correct diagnosis and treatment along with assisting clinicians in assessing progression of one or more visual conditions as compared to the approaches currently available.
  • the eye was determined to be worsening.
  • the specific values provided herein and the specific determination are for exemplary purpose only and are not limiting thereto.
  • the algorithm and process discussed herein was compared to existing algorithms including MD slope, Advanced Glaucoma Intervention Study (AGIS) scoring, Collaborative Initial Glaucoma Treatment Study (CIGTS) scoring, and the permutation of pointwise linear regression (PoPLR). The concordance between those algorithms was evaluated by Kappa coefficient.
  • the overall Kappa coefficient between existing algorithms was 0.34.
  • 89.6%, 9.8%, and 0.6% of the eyes had 1, 2 and 3 ATs progressed, and the 3 most frequent progressed archetypes were AT8 (17.3%), AT6 (15.2%) and AT3 (8.3%), as illustrated in FIGS. 12 and 13.
  • a specific analysis with age ranges, initial results, follow-up times, numbers of visual field measurements, ongoing results, etc. is provided above.
  • patients can be treated with a range of ages (e.g. 15 years old to 100 years old, 30 to 90, 50 to 85, etc.), initial results, follow-up times (e.g. 1 month to 30 years, 1 year to 20 years, 2 years to 10 years, etc.), numbers of visual field measurements (e.g. 2 to 20, 3 to 10, 4 to 8, etc.), ongoing results (e.g.
  • the algorithm and process discussed herein based on AT analysis can thus provide information of spatial pattern of visual field progression in addition to status outcome.
  • the spatial patterns of visual field progression can be used to assist clinicians to assess progression.

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Abstract

L'invention concerne des procédés, des systèmes et des dispositifs permettant de déterminer la progression du champ visuel d'un patient. Un procédé donné à titre d'exemple peut consister à décomposer un champ visuel présent et une pluralité de champs visuels antérieurs du patient en une pluralité d'archétypes de champ visuel pondérés; à déterminer si chacun de la pluralité d'archétypes de champ visuel pondérés augmente ou diminue en taille par rapport à lui-même à travers la pluralité de champs visuels antérieurs et le présent champ visuel; et à déterminer si le patient subit une perte de champ visuel sur la base de la détermination du fait que chacun de la pluralité d'archétypes de champ visuel pondérés augmente ou diminue.
PCT/US2019/020106 2018-03-01 2019-02-28 Progression de champ visuel WO2019169166A1 (fr)

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