The aim of this study was to establish and implement an objective structured clinical examination (OSCE) for stage assessment in standardized training for oral residents, evaluating its validity and suitability. An OSCE was established for stage assessment of 158 residents in a program in Jiangsu Province, China. Its validity and suitability were evaluated using indices such as reliability and discrimination coefficients of assessment results. The established OSCE had eight stations: interpretation of auxiliary examination results, basic knowledge, doctor-patient communication and clinical examination, clinical case analysis, medical record and prescription writing, public skills, first aid skills, and specialist's operation skills. The mean overall score and consistency coefficient (Cronbach's alpha) for the test subjects were 83.64±3.69 points and 0.732, respectively. The developed OSCE was reasonably established, with credible assessment results, and indices such as mean and discrimination coefficient of test scores from each station were appropriate. Therefore, the proposed protocol was found to be reliable and suitable.

The sense of discomfort when watching stereoscopic display caused by visual fatigue has hindered the widespread applications of stereoscopic display. In this work, we explore the relationship between visual fatigue and stereoscopic parallax using subjective assessment. The visual fatigue of the subjects is evaluated after viewing different parallax stereoscopic samples. It shows that visual fatigue exacerbates greater in negative parallax than positive parallax. Surprisingly, the results indicate that visual fatigue can be alleviated with tiny parallax, which increases the comfort of stereoscopic visual perception. We envision that this relationship is able to provide helpful hints in designing visual healthy stereoscopic images.

The emergence of optochemical approaches has had diverse impact over a broad range of biological researches due to spatiotemporal regulation. Herein we integrate this feature into bioorthogonal chemical reporter strategy,...

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Objective. To explore a centralized approach to build test sets and assess the performance of an artificial intelligence medical device (AIMD) which is intended for computer-aided diagnosis of diabetic retinopathy (DR). Method. A framework was proposed to conduct data collection, data curation, and annotation. Deidentified colour fundus photographs were collected from 11 partner hospitals with raw labels. Photographs with sensitive information or authenticity issues were excluded during vetting. A team of annotators was recruited through qualification examinations and trained. The annotation process included three steps: initial annotation, review, and arbitration. The annotated data then composed a standardized test set, which was further imported to algorithms under test (AUT) from different developers. The algorithm outputs were compared with the final annotation results (reference standard). Result. The test set consists of 6327 digital colour fundus photographs. The final labels include 5 stages of DR and non-DR, as well as other ocular diseases and photographs with unacceptable quality. The Fleiss Kappa was 0.75 among the annotators. The Cohen’s kappa between raw labels and final labels is 0.5. Using this test set, five AUTs were tested and compared quantitatively. The metrics include accuracy, sensitivity, and specificity. The AUTs showed inhomogeneous capabilities to classify different types of fundus photographs. Conclusions. This article demonstrated a workflow to build standardized test sets and conduct algorithm testing of the AIMD for computer-aided diagnosis of diabetic retinopathy. It may provide a reference to develop technical standards that promote product verification and quality control, improving the comparability of products.