Background CC-Cruiser is an artificial intelligence (AI) platform developed for diagnosing childhood cataracts and providing risk stratification and treatment recommendations. The high accuracy of CC-Cruiser was previously validated using specific datasets. The objective of this study was to compare the diagnostic efficacy and treatment decision-making capacity between CC-Cruiser and ophthalmologists in real-world clinical settings. Methods This multicentre randomized controlled trial was performed in five ophthalmic clinics in different areas across China. Pediatric patients (aged ≤ 14 years) without a definitive diagnosis of cataracts or history of previous eye surgery were randomized (1:1) to receive a diagnosis and treatment recommendation from either CC-Cruiser or senior consultants (with over 5 years of clinical experience in pediatric ophthalmology). The experts who provided a gold standard diagnosis, and the investigators who performed slit-lamp photography and data analysis were blinded to the group assignments. The primary outcome was the diagnostic performance for childhood cataracts with reference to cataract experts' standards. The secondary outcomes included the evaluation of disease severity and treatment determination, the time required for the diagnosis, and patient satisfaction, which was determined by the mean rating. This trial is registered with ClinicalTrials.gov ( NCT03240848 ). Findings Between August 9, 2017 and May 25, 2018, 350 participants (700 eyes) were randomly assigned for diagnosis by CC-Cruiser (350 eyes) or senior consultants (350 eyes). The accuracies of cataract diagnosis and treatment determination were 87.4% and 70.8%, respectively, for CC-Cruiser, which were significantly lower than 99.1% and 96.7%, respectively, for senior consultants ( p < 0.001, OR = 0.06 [95% CI 0.02 to 0.19]; and p < 0.001, OR = 0.08 [95% CI 0.03 to 0.25], respectively). The mean time for receiving a diagnosis from CC-Cruiser was 2.79 min, which was significantly less than 8.53 min for senior consultants ( p < 0.001, mean difference 5.74 [95% CI 5.43 to 6.05]). The patients were satisfied with the overall medical service quality provided by CC-Cruiser, typically with its time-saving feature in cataract diagnosis. Interpretation CC-Cruiser exhibited less accurate performance comparing to senior consultants in diagnosing childhood cataracts and making treatment decisions. However, the medical service provided by CC-Cruiser was less time-consuming and achieved a high level of patient satisfaction. CC-Cruiser has the capacity to assist human doctors in clinical practice in its current state. Funding National Key R&D Program of China (2018YFC0116500) and the Key Research Plan for the National Natural Science Foundation of China in Cultivation P...
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.
Abstract-A supergraph containment search is to retrieve the data graphs contained by a query graph. In this paper, we study the problem of efficiently retrieving all data graphs approximately contained by a query graph, namely similarity search on supergraph containment. We propose a novel and efficient index to boost the efficiency of query processing. We have studied the query processing cost and propose two index construction strategies aimed at optimizing the performance of different types of data graphs: top-down strategy and bottomup strategy. Moreover, a novel indexing technique is proposed by effectively merging the indexes of individual data graphs; this not only reduces the index size but also further reduces the query processing time. We conduct extensive experiments on real data sets to demonstrate the efficiency and the effectiveness of our techniques.
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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