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

BACKGROUND AND OBJECTIVE: Age-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 intravitreal 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 favourable 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. METHOD: A machine learning approach using adaptive neuro-fuzzy inference system (ANFIS) of two-sample prediction model has been presented that requires only the baseline 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 and 12 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. RESULTS: The presented model has shown to have a very high accuracy (> 92%) and works in near-real-time scenarios. It has been converted into a smart phone App, OphnosisAMD, 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 AI engine developed with the 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. CONCLUSIONS: With 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 (baseline 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.

ANFIS-Net for automatic detection of COVID-19.

Among the most leading causes of mortality across the globe are infectious diseases which have cost tremendous lives with the latest being coronavirus (COVID-19) that has become the most recent challenging issue. The extreme nature of this infectious virus and its ability to spread without control has made it mandatory to find an efficient auto-diagnosis system to assist the people who work in touch with the patients. As fuzzy logic is considered a powerful technique for modeling vagueness in medical practice, an Adaptive Neuro-Fuzzy Inference System (ANFIS) was proposed in this paper as a key rule for automatic COVID-19 detection from chest X-ray images based on the characteristics derived by texture analysis using gray level co-occurrence matrix (GLCM) technique. Unlike the proposed method, especially deep learning-based approaches, the proposed ANFIS-based method can work on small datasets. The results were promising performance accuracy, and compared with the other state-of-the-art techniques, the proposed method gives the same performance as the deep learning with complex architectures using many backbone.