OBJECTIVEThe goal of this study was to describe the development and validation of an artificial intelligence-based, deep learning algorithm (DLA) for the detection of referable diabetic retinopathy (DR). RESEARCH DESIGN AND METHODSA DLA using a convolutional neural network was developed for automated detection of vision-threatening referable DR (preproliferative DR or worse, diabetic macular edema, or both). The DLA was tested by using a set of 106,244 nonstereoscopic retinal images. A panel of ophthalmologists graded DR severity in retinal photographs included in the development and internal validation data sets (n = 71,043); a reference standard grading was assigned once three graders achieved consistent grading outcomes. For external validation, we tested our DLA using 35,201 images of 14,520 eyes (904 eyes with any DR; 401 eyes with vision-threatening referable DR) from population-based cohorts of Malays, Caucasian Australians, and Indigenous Australians. RESULTSAmong the 71,043 retinal images in the training and validation data sets, 12,329 showed vision-threatening referable DR. In the internal validation data set, the area under the curve (AUC), sensitivity, and specificity of the DLA for vision-threatening referable DR were 0.989, 97.0%, and 91.4%, respectively. Testing against the independent, multiethnic data set achieved an AUC, sensitivity, and specificity of 0.955, 92.5%, and 98.5%, respectively. Among false-positive cases, 85.6% were due to a misclassification of mild or moderate DR. Undetected intraretinal microvascular abnormalities accounted for 77.3% of all false-negative cases. CONCLUSIONSThis artificial intelligence-based DLA can be used with high accuracy in the detection of vision-threatening referable DR in retinal images. This technology offers potential to increase the efficiency and accessibility of DR screening programs.

Using in silico drug screening by Connectivity Map followed by empirical validations, we repurposed an existing phenothiazine-like antipsychotic drug, trifluoperazine, as a potential anti-CSC agent that could overcome epidermal growth factor receptor-tyrosine kinase inhibitor and chemotherapy resistance.

The purpose of this study is to evaluate the feasibility and patient acceptability of a novel artificial intelligence (AI)-based diabetic retinopathy (DR) screening model within endocrinology outpatient settings. Adults with diabetes were recruited from two urban endocrinology outpatient clinics and single-field, non-mydriatic fundus photographs were taken and graded for referable DR ( ≥ pre-proliferative DR). Each participant underwent; (1) automated screening model; where a deep learning algorithm (DLA) provided real-time reporting of results; and (2) manual model where retinal images were transferred to a retinal grading centre and manual grading outcomes were distributed to the patient within 2 weeks of assessment. Participants completed a questionnaire on the day of examination and 1-month following assessment to determine overall satisfaction and the preferred model of care. In total, 96 participants were screened for DR and the mean assessment time for automated screening was 6.9 minutes. Ninety-six percent of participants reported that they were either satisfied or very satisfied with the automated screening model and 78% reported that they preferred the automated model over manual. The sensitivity and specificity of the DLA for correct referral was 92.3% and 93.7%, respectively. AI-based DR screening in endocrinology outpatient settings appears to be feasible and well accepted by patients.

Induced pluripotent stem (iPS) cells have potential for multilineage differentiation and provide a resource for stem cell-based treatment. However, the therapeutic effect of iPS cells on acute kidney injury (AKI) remains uncertain. Given that the oncogene c-Myc may contribute to tumorigenesis by causing genomic instability, herein we evaluated the therapeutic effect of iPS cells without exogenously introduced c-Myc on ischemia-reperfusion (I/R)-induced AKI. As compared with phosphate-buffered saline (PBS)-treated group, administration of iPS cells via intrarenal arterial route into kidneys improved the renal function and attenuated tubular injury score at 48 h after ischemia particularly at the dose of 5 × 10 5 iPS cells. However, a larger number of iPS cells (5 × 10 7 per rat) diminished the therapeutic effects for AKI and profoundly reduced renal perfusion detected by laser Doppler imaging in the reperfusion phase. In addition, the green fluorescence protein-positive iPS cells mobilized to the peritubular area at 48 h following ischemia, accompanied by a significant reduction in infiltration of macrophages and apoptosis of tubular cells, and a remarkable enhancement in endogenous tubular cell proliferation. Importantly, transplantation of iPS cells reduced the expression of oxidative substances, proinflammatory cytokines, and apoptotic factors in I/R kidney tissues and eventually improved survival in rats of ischemic AKI. Six months after transplantation in I/R rats, engrafted iPS cells did not result in tumor formation in kidney and other organs. In summary, considering the antioxidant, anti-inflammatory, and antiapoptotic properties of iPS cells without c-Myc, transplantation of such cells may be a treatment option for ischemic AKI.