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Artificial Intelligence (AI) is necessary to address the large and growingdeficit in retina and healthcare access globally. And mobile AI diagnosticplatforms running in the Cloud may effectively and efficiently distribute suchAI capability. Here we sought to evaluate the feasibility of Cloud-based mobileartificial intelligence for detection of retinal disease. And to evaluate theaccuracy of a particular such system for detection of subretinal fluid (SRF)and macula edema (ME) on OCT scans. A multicenter retrospective image analysiswas conducted in which board-certified ophthalmologists with fellowshiptraining in retina evaluated OCT images of the macula. They noted the presenceor absence of ME or SRF, then compared their assessment to that obtained fromFluid Intelligence, a mobile AI app that detects SRF and ME on OCT scans.Investigators consecutively selected retinal OCTs, while making effort tobalance the number of scans with retinal fluid and scans without. Exclusioncriteria included poor scan quality, ambiguous features, macula holes,retinoschisis, and dense epiretinal membranes. Accuracy in the form ofsensitivity and specificity of the AI mobile App was determined by comparingits assessments to those of the retina specialists. At the time of thissubmission, five centers have completed their initial studies. This consists ofa total of 283 OCT scans of which 155 had either ME or SRF ("wet") and 128 didnot ("dry"). The sensitivity ranged from 82.5% to 97% with a weighted averageof 89.3%. The specificity ranged from 52% to 100% with a weighted average of81.23%. CONCLUSION: Cloud-based Mobile AI technology is feasible for thedetection retinal disease. In particular, Fluid Intelligence (alpha version),is sufficiently accurate as a screening tool for SRF and ME, especially inunderserved areas. Further studies and technology development is needed.

Mobile Artificial Intelligence Technology for Detecting Macula Edema and Subretinal Fluid on OCT Scans: Initial Results from the DATUM alpha Study