Regular health screening plays a crucial role in the early detection of common chronic diseases and prevention of their progression. An AI system capable of recapitulating early disease detection, staging and incidence prediction would help to improve healthcare access and delivery, particularly in resource-poor or remote settings. Using a total of 115,344 retinal fundus photographs from 57,672 patients (with data split into mutually exclusive training, internal testing, and external validation sets), we first developed AI models capable of identifying chronic kidney disease (CKD) and type 2 diabetes mellitus (T2DM) based on fundus images. The AI system was shown to be capable of predicting the clinical indicators of CKD and T2DM (including eGFR and blood glucose levels), which indicates its potential for extracting quantitative clinical metrics embedded subtly within retinal fundus images. We further developed an AI system to predict the risk of disease progression using baseline images of 10,269 patients for whom longitudinal clinical data were available for up to 6 years, which demonstrated potential utility in optimizing health screening intervals and clinical management. The generalizability of the AI system in identifying and predicting the progression of CKD and T2DM was evaluated using population-based external validation cohorts. Moreover, a prospective pilot study with 3,081 patients was also conducted to demonstrate the broader applicability of the AI system at the 'point-of-care' using fundus images captured with smartphones. The results provide proof-of-concept for a reliable and non-invasive AI-based clinical screening tool based on fundus photographs for the early detection and incidence prediction of two common systemic diseases.

Common lung diseases are first diagnosed via chest X-rays. Here, we show that a fully automated deep-learning pipeline for chest-X-ray-image standardization, lesion visualization and disease diagnosis can identify viral pneumonia caused by Coronavirus disease 2019 (COVID-19), assess its severity, and discriminate it from other types of pneumonia. The deep-learning system was developed by using a heterogeneous multicentre dataset of 145,202 images, and tested retrospectively and prospectively with thousands of additional images across four patient cohorts and multiple countries. The system generalized across settings, discriminating between viral pneumonia, other types of pneumonia and absence of disease with areas under the receiver operating characteristic curve (AUCs) of 0.88–0.99, between severe and non-severe COVID-19 with an AUC of 0.87, and between severe or non-severe COVID-19 pneumonia and other viral and non-viral pneumonia with AUCs of 0.82–0.98. In an independent set of 440 chest X-rays, the system performed comparably to senior radiologists, and improved the performance of junior radiologists. Automated deep-learning systems for the assessment of pneumonia could facilitate early intervention and provide clinical-decision support.

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Background Gallbladder cancer (GBC) is the most malignant cancer occurring in the biliary tract cancer featured with undesirable prognosis, in which most patients die within a year of cholecystectomy. Long noncoding RNAs (lncRNAs) function as critical regulators of multiple stages of cancers. Herein, the mechanism of lncRNA metastasis associated lung adenocarcinoma transcript 1 (MALAT1) in GBC is investigated. Methods Microarray-based analysis initially provided data suggesting that the expression of MALAT1 was up-regulated while that of the ABI family member 3 binding protein (ABI3BP) was down-regulated in GBC tissues and cell lines. Kaplan-Meier method was then adopted to analyze the relationship between the MALAT1 expression and overall survival and disease-free survival of patients with GBC. A set of in vitro and in vivo experiments were conducted by transducing ABI3BP-vector or sh-MALAT1 into GBC cells. Results The results confirmed that the cancer prevention effects triggered by restored ABI3BP and depleted MALAT1 as evidenced by suppressed cell growth and enhanced cell senescence. MALAT1 was observed to down-regulate ABI3BP expression through recruitment of the enhancer of zeste homolog 2 (EZH2) to the ABI3BP promoter region while the silencing of MALAT1 or suppression of H3K27 methylation was observed to promote the expression of ABI3BP. Furthermore, GBC patients with high expression of MALAT1 indicated poor prognosis. Conclusion The current study clarifies that MALAT1 silencing and ABI3BP elevation impede the GBC development through the H3K27 methylation suppression induced by EZH2, highlighting a promising competitive paradigm for therapeutic approaches of GBC.