Importance: Artificial Intelligence (AI) is necessary to address the large and growing deficit in retina and healthcare access globally. Mobile AI diagnostic platforms running in the Cloud may effectively and efficiently distribute such AI capability. Objective: To evaluate the feasibility of Cloud-based mobile artificial intelligence for detection of retinal disease. And to evaluate the accuracy of a particular such system for detection of subretinal fluid (SRF) and macula edema (ME) on OCT scans. Design: A multicenter retrospective image analysis was conducted in which board-certified ophthalmologists with fellowship training in retina evaluated OCT images of the macula. They noted the presence or absence of macula edema or subretinal fluid , then compared their assessment to that obtained from Fluid Intelligence, a mobile AI app that detects SRF and ME on OCT scans. Setting: Clinic setting with PACS access to OCT images of patients with or without retinal fluid. Participants: Board-certified ophthalmologists with fellowship training in retina retrospectively selected retinal OCT images in a consecutive fashion. Effort was made by each center to balance the number of scans with retinal fluid and scans without. Exclusion criteria included poor quality scans, ambiguous or overly subtle features, macula holes, retinoschisis, and dense epiretinal membranes. Main outcome and Measures: Accuracy in the form of sensitivity and specificity of the AI mobile app compared against assessments of board-certified retina specialists .
 Artificial Intelligence (AI) is necessary to address the large and growing deficit in retina and healthcare access globally. And mobile AI diagnostic platforms running in the Cloud may effectively and efficiently distribute such AI capability. Here we sought to evaluate the feasibility of Cloud-based mobile artificial intelligence for detection of retinal disease. And to evaluate the accuracy of a particular such system for detection of subretinal fluid (SRF) and macula edema (ME) on OCT scans. A multicenter retrospective image analysis was conducted in which board-certified ophthalmologists with fellowship training in retina evaluated OCT images of the macula. They noted the presence or absence of ME or SRF , then compared their assessment to that obtained from Fluid Intelligence, a mobile AI app that detects SRF and ME on OCT scans. Investigators consecutively selected retinal OCTs, while making effort to balance the number of scans with retinal fluid and scans without. Exclusion criteria included poor scan quality, ambiguous features, macula holes, retinoschisis, and dense epiretinal membranes. Accuracy in the form of sensitivity and specificity of the AI mobile App was determined by comparing its assessments to those of the retina specialists. At the time of this submission, five centers have completed their initial studies. This consists of a total of 283 OCT scans of which 155 had either ME or SRF ("wet") and 128 did not ("dry"). The sensitivity ranged from 82.5\% to 97\% with a weighted average of 89.3\%. The specificity ranged from 52\% to 100\% with a weighted average of 81.23\%. CONCLUSION: Cloud-based Mobile AI technology is feasible for the detection retinal disease. In particular, Fluid Intelligence (alpha version), is sufficiently accurate as a screening tool for SRF and ME, especially in underserved areas. Further studies and technology development is needed .
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Importance:
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Mobile
And mobile
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Objective: To
Here we sought to
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Design:
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macula edema or subretinal fluid
ME or SRF
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Setting: Clinic setting with PACS access to OCT images of patients with or without retinal fluid. Participants: Board-certified ophthalmologists with fellowship training in retina retrospectively selected retinal OCT images in a consecutive fashion. Effort was made by each center
Investigators consecutively selected retinal OCTs, while making effort
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quality scans, ambiguous or overly subtle
scan quality, ambiguous
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Main outcome and Measures:
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app compared against assessments of board-certified retina specialists
App was determined by comparing its assessments to those of the retina specialists. At the time of this submission, five centers have completed their initial studies. This consists of a total of 283 OCT scans of which 155 had either ME or SRF ("wet") and 128 did not ("dry"). The sensitivity ranged from 82.5\% to 97\% with a weighted average of 89.3\%. The specificity ranged from 52\% to 100\% with a weighted average of 81.23\%. CONCLUSION: Cloud-based Mobile AI technology is feasible for the detection retinal disease. In particular, Fluid Intelligence (alpha version), is sufficiently accurate as a screening tool for SRF and ME, especially in underserved areas. Further studies and technology development is needed