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.

Aims: To evaluate primary periocular basal cell carcinomas (BCCs) in depth including comparing histological margins with subtype, location and surgical specialty after wide local excision. Methods: A retrospective review was performed for all BCCs excised from three hospitals over 5 years, covering a population of just over 1 million. Tumours were classified according to histological subtype location. Incomplete excision rates and margins were analysed in detail and comparisons made. Results: The most common subtype found was nodular followed by infiltrative. Lesions were most commonly located at the lower lid. Infiltrative BCCs were associated with perineural invasion and incomplete excision despite the largest peripheral margins. Superficial BCCs had the smallest mean peripheral margin but the largest mean deep margin. 2 mm histological margins gave an 83.7% complete excision rate, 6.4% incomplete excision rate and 7.1% where the clearance margin was 0.3 mm or less. Conclusion: Distribution of eyelid BCCs based on subtype and periocular location mirrored the general consensus. Infiltrative BCCs should be excised with wider margins or referred for Mohs surgery, especially if the medial canthus is involved. Superficial BCCs should be excised with wider but shallower surgical margins. Ophthalmologists were more likely than dermatologists or plastic surgeons to incompletely excise a periocular BCC, which is reflective of their more difficult case mix.