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

Abstract

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.

Integration of smartphone technology and artificial intelligence for advanced ophthalmic care: A systematic review

“… 19 2023 ROP detection and grading Referral-warranted ROP (sensitivity ​= ​80.0%, specificity ​= ​59.3%) Treatment-requiring ROP (sensitivity ​= ​100.0%, specificity ​= ​58.6%) Fundus photographs taken by SBFI systems (the Make-In-India Retcam/Keeler Monocular Indirect Ophthalmoscope devices) ResNet18 / Qidwai et al. 18 2022 AMD prognosis prediction Accuracy >92.0% Measurements of baseline, changes in visual acuity and macular thickness after four months of treatment Adaptive neuro-fuzzy inference system Ophnosis AMD Nakahara et al. 21 2022 Glaucoma detection Glaucoma (AUC ​= ​0.842) Advanced glaucoma (AUC ​= ​0.900) Fundus photographs taken by an iPhone 8 with the D-Eye lens (D-EYE S.r.l., Padova, Italy) ResNet / Wu et al.…”

“…explored a smart AI-based App based on adaptive neuro-fuzzy inference system to aid the clinician to visualize the progression of the patient and make better decisions related to the treatment. 18 The model had ultimately shown to have a high accuracy (92%) and works in near-real-time scenarios. Another study aimed to acquire a cost-effective alternative in the ROP telemedicine screening program by smartphone-based fundus imaging (SBFI) systems with AI and finally revealed that the two SBFI systems used in the ROP screening program were highly sensitive for treatment requiring-ROP.…”

Risk factors for the progression of age-related macular degeneration in patients of the Ukrainian population

“…Перспективність складання індивідуалізованого прогнозу на підставі визначення певних біомаркерів підтверджена можливістю оцінки ризику неефективності лікування пізньої ВМД інтравітреальними ін'єкціями інгібітору фактора росту судинного ендотелію (VEGF) [10]. Експрес-оцінка проводиться із застосуванням смартфону та оригінальної програми розрахунку гостроти зору та товщини макули через 8 і 12 місяців після лікування на підставі цих показників через 4 місяці.…”

“…Модель має високу точність (>92%) і працює в режимі реального часу. Використання розробленої нами моделі (формула 1) також дозволяє на підставі невеликої кількості ознак спрогнозувати прогресію ВМД при різних початкових стадіях [10].…”

“…Останнім часом з'являються прогностичні експертні системи, які за допомогою оцінки біомаркерів пропонують моделі прогресування ВМД [9,10]. Так, створена модель оцінки ризику прогресії ВМД протягом 5 років, яка включає вік та показники спектральної доменної оптичної когерентної томографії -характеристику друз і патології сітківки [9].…”