BRCA1/2 mutation carriers who have been treated with PS have a substantially reduced breast cancer incidence and mortality. Clin Cancer Res; 22(15); 3971-81. ©2016 AACR.

PurposeTo establish and validate a universal artificial intelligence (AI) platform for collaborative management of cataracts involving multilevel clinical scenarios and explored an AI-based medical referral pattern to improve collaborative efficiency and resource coverage.MethodsThe training and validation datasets were derived from the Chinese Medical Alliance for Artificial Intelligence, covering multilevel healthcare facilities and capture modes. The datasets were labelled using a three-step strategy: (1) capture mode recognition; (2) cataract diagnosis as a normal lens, cataract or a postoperative eye and (3) detection of referable cataracts with respect to aetiology and severity. Moreover, we integrated the cataract AI agent with a real-world multilevel referral pattern involving self-monitoring at home, primary healthcare and specialised hospital services.ResultsThe universal AI platform and multilevel collaborative pattern showed robust diagnostic performance in three-step tasks: (1) capture mode recognition (area under the curve (AUC) 99.28%–99.71%), (2) cataract diagnosis (normal lens, cataract or postoperative eye with AUCs of 99.82%, 99.96% and 99.93% for mydriatic-slit lamp mode and AUCs >99% for other capture modes) and (3) detection of referable cataracts (AUCs >91% in all tests). In the real-world tertiary referral pattern, the agent suggested 30.3% of people be ‘referred’, substantially increasing the ophthalmologist-to-population service ratio by 10.2-fold compared with the traditional pattern.ConclusionsThe universal AI platform and multilevel collaborative pattern showed robust diagnostic performance and effective service for cataracts. The context of our AI-based medical referral pattern will be extended to other common disease conditions and resource-intensive situations.

Background Medical artificial intelligence (AI) has entered the clinical implementation phase, although real-world performance of deep-learning systems (DLSs) for screening fundus disease remains unsatisfactory. Our study aimed to train a clinically applicable DLS for fundus diseases using data derived from the real world, and externally test the model using fundus photographs collected prospectively from the settings in which the model would most likely be adopted.Methods In this national real-world evidence study, we trained a DLS, the Comprehensive AI Retinal Expert (CARE) system, to identify the 14 most common retinal abnormalities using 207 228 colour fundus photographs derived from 16 clinical settings with different disease distributions. CARE was internally validated using 21 867 photographs and externally tested using 18 136 photographs prospectively collected from 35 real-world settings across China where CARE might be adopted, including eight tertiary hospitals, six community hospitals, and 21 physical examination centres. The performance of CARE was further compared with that of 16 ophthalmologists and tested using datasets with non-Chinese ethnicities and previously unused camera types. This study was registered with ClinicalTrials.gov, NCT04213430, and is currently closed. FindingsThe area under the receiver operating characteristic curve (AUC) in the internal validation set was 0•955 (SD 0•046). AUC values in the external test set were 0•965 (0•035) in tertiary hospitals, 0•983 (0•031) in community hospitals, and 0•953 (0•042) in physical examination centres. The performance of CARE was similar to that of ophthalmologists. Large variations in sensitivity were observed among the ophthalmologists in different regions and with varying experience. The system retained strong identification performance when tested using the non-Chinese dataset (AUC 0•960, 95% CI 0•957-0•964 in referable diabetic retinopathy).Interpretation Our DLS (CARE) showed satisfactory performance for screening multiple retinal abnormalities in real-world settings using prospectively collected fundus photographs, and so could allow the system to be implemented and adopted for clinical care.

Background Common diseases are not satisfactorily managed under the current health-care system because of inadequate medical resources and limited accessibility. We aimed to establish and validate a universal artificial intelligence (AI) platform for collaborative management of cataracts involving multilevel clinical scenarios, and explored an AI-based medical referral pattern to improve collaborative efficiency and resource coverage. Methods The training and validation datasets were derived from the Chinese Medical Alliance for Artificial Intelligence, covering multilevel health-care facilities and capture modes. The datasets were labeled using a three-step strategy: capture mode recognition (modes: mydriatic-diffuse, mydriatic-slit lamp, non-mydriatic-diffuse, and nonmydriatic-slit lamp); cataract diagnosis as a normal lens, cataract, or a postoperative eye; and detection of referable cataracts with respect to cause and severity. Area under curve [AUC] was measured at each stage. We also integrated the above cataract AI agent with a real-world multilevel referral pattern involving self-monitoring at home, primary health care, and specialised hospital services. The diagnostic accuracy, treatment referral, and ophthalmologist-topopulation service ratio were used to evaluate the performance and efficacy of the system. Findings The universal AI platform and multilevel collaborative pattern showed robust diagnostic performance in threestep tasks: capture mode recognition (AUC 99•28-99•71% for the four different capture modes), cataract diagnosis (AUC for mydriatic-slit lamp mode 99•82% [95%CI 98•93-100] for normal lens vs 99•96% [99•90-100] for cataract vs 99•93% [99•78-100] for postoperative eye, and AUCs >99% for other capture modes), and detection of referable cataracts (AUCs >91% in all tests). In the real-world tertiary referral pattern, the agent suggested 30•3% of people be referred to treatment, substantially increasing the ophthalmologist-to-population service ratio by 10•2-times compared with the traditional pattern. Interpretation The universal AI platform and multilevel collaborative pattern showed robust diagnostic performance and effective service for cataracts. The context of our AI-based medical referral pattern will be extended to other common disease conditions and resource-intensive situations.

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