A Survey of Machine Learning Approaches for Age Related Macular Degeneration Diagnosis and Prediction

Martínez-Velasco, Antonieta; Martínez-Villaseñor, Lourdes

doi:10.1007/978-3-030-02837-4_21

Cited by 4 publications

(1 citation statement)

References 26 publications

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“…[11] has shown the use of Fuzzy Segmentation in classifying the macular lesions in OCT images. Several other machine learning algorithms have also been used where a number of techniques were utilized in classification of the AMD grades and stages [12][13][14][15]. In these techniques, all types of ophthalmological retinal images have been used with either feature extraction first using some form of image preprocessing and then using data-centric classifiers to distinguish various degradation stages, lesions, and overall AMD progression.…”

Section: Introductionmentioning

confidence: 99%

Smart AMD prognosis through cellphone: an innovative localized AI-based prediction system for anti-VEGF treatment prognosis in nonagenarians and centenarians

Qidwai

Raja

et al. 2022

Int Ophthalmol

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Background and ObjectiveAge-related macular-degeneration (AMD) is one of the most common reasons for blindness in the world today. The most common treatment for wet AMD is the intra-vitreal injections for inhibiting Vascular-Endothelial-Derived-Growth-Factor (VEGF). This treatment usually involves multiple injections and thus multiple clinic visits which not only causes increased cost on national health services but also causes exposure to the hospital environment which is sometimes high risk considering current COVID crisis. The treatment, in spite of the above concerns, is usually effective. However, in some cases, either the medicine fails to produce the anticipated favorable outcome, resulting in waste of time, medication, efforts, and above all, psychological distress to the patients. Hence, early predictability of anatomical as well as functional effectiveness of the treatment appears to be a very desirable capability to have. MethodA Machine Learning approach using Adaptive Neuro-Fuzzy Inference System (ANFIS) twosample prediction model has been presented that requires only the base line measurements and changes in Visual Acuity (VA) as well as Macular Thickness (MAC) after four months of treatment to estimate the values of VA and MAC at 8 th and 12 th months. In contrast to most of the AI techniques, ANFIS approach has shown the capability of the algorithm to work with very small dataset as well, which makes it a perfect candidate for the presented solution. ResultsThe presented model has shown to have a very high accuracy (>92%) and works in near-realtime scenarios. It has been converted into a smart-phone-App, Ophnosis AMD , for convenient usage. With this App, the clinician can visualize the progression of the patient for a specific treatment and can decide on continuing or changing the treatment accordingly. The complete AIengine developed with ANFIS algorithm is localized to the phone through the App, implying that there is no need for internet or cloud connectivity for this App to function. This makes it ideal for remote usage, especially under the current COVID scenarios. ConclusionsWith a smart AI-based App on their fingertips, the presented system provides ample opportunity to the doctors to make a better decision based on the estimated progression, if the same drug is continued with (Good/Fair Prognosis) or alternate treatment should be sought (Bad Prognosis). From a functional point of view, a prediction algorithm is triggered through simple entry of the relevant parameters (base-line and 4 months only). No internet/cloud connectivity is needed since the algorithm and the trained network are fully embedded in the App locally. Hence, using the App in remote and/or non-connected isolated areas is possible, especially in the secluded patients during the COVID scenarios.

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Section: Introductionmentioning

confidence: 99%

Smart AMD prognosis through cellphone: an innovative localized AI-based prediction system for anti-VEGF treatment prognosis in nonagenarians and centenarians

Qidwai

Raja

et al. 2022

Int Ophthalmol

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A multimodal deep learning system to distinguish late stages of AMD and to compare expert vs. AI ocular biomarkers

Thakoor

Yao

Bordbar

et al. 2022

Sci Rep

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Within the next 1.5 decades, 1 in 7 U.S. adults is anticipated to suffer from age-related macular degeneration (AMD), a degenerative retinal disease which leads to blindness if untreated. Optical coherence tomography angiography (OCTA) has become a prime technique for AMD diagnosis, specifically for late-stage neovascular (NV) AMD. Such technologies generate massive amounts of data, challenging to parse by experts alone, transforming artificial intelligence into a valuable partner. We describe a deep learning (DL) approach which achieves multi-class detection of non-AMD vs. non-neovascular (NNV) AMD vs. NV AMD from a combination of OCTA, OCT structure, 2D b-scan flow images, and high definition (HD) 5-line b-scan cubes; DL also detects ocular biomarkers indicative of AMD risk. Multimodal data were used as input to 2D-3D Convolutional Neural Networks (CNNs). Both for CNNs and experts, choroidal neovascularization and geographic atrophy were found to be important biomarkers for AMD. CNNs predict biomarkers with accuracy up to 90.2% (positive-predictive-value up to 75.8%). Just as experts rely on multimodal data to diagnose AMD, CNNs also performed best when trained on multiple inputs combined. Detection of AMD and its biomarkers from OCTA data via CNNs has tremendous potential to expedite screening of early and late-stage AMD patients.

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An Explainable Tool to Support Age-related Macular Degeneration Diagnosis

Martínez-Villaseñor

Ponce

Martínez-Velasco

et al. 2022

2022 International Joint Conference on Neural Networks (IJCNN)

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A Survey of Machine Learning Approaches for Age Related Macular Degeneration Diagnosis and Prediction

Cited by 4 publications

References 26 publications

Smart AMD prognosis through cellphone: an innovative localized AI-based prediction system for anti-VEGF treatment prognosis in nonagenarians and centenarians

Smart AMD prognosis through cellphone: an innovative localized AI-based prediction system for anti-VEGF treatment prognosis in nonagenarians and centenarians

A multimodal deep learning system to distinguish late stages of AMD and to compare expert vs. AI ocular biomarkers

An Explainable Tool to Support Age-related Macular Degeneration Diagnosis

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