Artificial intelligence-based detection of epimacular membrane from color fundus photographs.

Epiretinal membrane (ERM) is a common ophthalmological disorder of high prevalence. Its symptoms include metamorphopsia, blurred vision, and decreased visual acuity. Early diagnosis and timely treatment of ERM is crucial to preventing vision loss. Although optical coherence tomography (OCT) is regarded as a de facto standard for ERM diagnosis due to its intuitiveness and high sensitivity, ophthalmoscopic examination or fundus photographs still have the advantages of price and accessibility. Artificial intelligence (AI) has been widely applied in the health care industry for its robust and significant performance in detecting various diseases. In this study, we validated the use of a previously trained deep neural network based-AI model in ERM detection based on color fundus photographs. An independent test set of fundus photographs was labeled by a group of ophthalmologists according to their corresponding OCT images as the gold standard. Then the test set was interpreted by other ophthalmologists and AI model without knowing their OCT results. Compared with manual diagnosis based on fundus photographs alone, the AI model had comparable accuracy (AI model 77.08% vs. integrated manual diagnosis 75.69%, χ2 = 0.038, P = 0.845, McNemar's test), higher sensitivity (75.90% vs. 63.86%, χ2 = 4.500, P = 0.034, McNemar's test), under the cost of lower but reasonable specificity (78.69% vs. 91.80%, χ2 = 6.125, P = 0.013, McNemar's test). Thus our AI model can serve as a possible alternative for manual diagnosis in ERM screening.

Application of Comprehensive Artificial intelligence Retinal Expert (CARE) system: a national real-world evidence study.

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. FINDINGS: The 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. FUNDING: This study was funded by the National Key R&D Programme of China, the Science and Technology Planning Projects of Guangdong Province, the National Natural Science Foundation of China, the Natural Science Foundation of Guangdong Province, and the Fundamental Research Funds for the Central Universities. TRANSLATION: For the Chinese translation of the abstract see Supplementary Materials section.

Universal artificial intelligence platform for collaborative management of cataracts.

PURPOSE: 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 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. RESULTS: The 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. CONCLUSIONS: 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.

Effectiveness of Prophylactic Surgeries in BRCA1 or BRCA2 Mutation Carriers: A Meta-analysis and Systematic Review.

PURPOSE: To systematically investigate the effectiveness of prophylactic surgeries (PS) implemented in women carrying BRCA1/2 mutations. EXPERIMENTAL DESIGN: The PubMed database was searched till August 2014 and 15 studies met the inclusion criteria. Fixed- or random-effects models were conducted according to study heterogeneity. We calculated the pooled relative risks (RR) for cancer risk or mortality along with 95% confidence intervals (CI). RESULTS: Prophylactic bilateral salpingo-oophorectomy (PBSO) and bilateral prophylactic mastectomy (BPM) were both associated with a decreased breast cancer risk in BRCA1/2 mutation carriers (RR, 0.552; 95% CI, 0.448-0.682; RR, 0.114; 95% CI, 0.041-0.317, respectively). Similar findings were observed in BRCA1 and BRCA2 mutation carriers separately. Moreover, contralateral prophylactic mastectomy (CPM) significantly decreased contralateral breast cancer incidence in BRCA1/2 mutation carriers (RR, 0.072; 95% CI, 0.035-0.148). Of note, PBSO was associated with significantly lower all-cause mortality in BRCA1/2 mutation carriers without breast cancer (HR, 0.349; 95% CI, 0.190-0.639) and those with breast cancer (HR, 0.432; 95% CI, 0.318-0.588). In addition, all-cause mortality was significantly lower for patients with CPM than those without (HR, 0.512; 95% CI, 0.368-0.714). However, BPM was not significantly associated with reduced all-cause mortality. Data were insufficient to obtain separate estimates of survival benefit with PS in BRCA1 or BRCA2 mutation carriers. CONCLUSIONS: 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.