Artificial intelligence assists operators in real-time detection of focal liver lesions during ultrasound: A randomized controlled study.

PURPOSE: Detection of hepatocellular carcinoma (HCC) is crucial during surveillance by ultrasound. We previously developed an artificial intelligence (AI) system based on convolutional neural network for detection of focal liver lesions (FLLs) in ultrasound. The primary aim of this study was to evaluate whether the AI system can assist non-expert operators to detect FLLs in real-time, during ultrasound examinations. METHOD: This single-center prospective randomized controlled study evaluated the AI system in assisting non-expert and expert operators. Patients with and without FLLs were enrolled and had ultrasound performed twice, with and without AI assistance. McNemar's test was used to compare paired FLL detection rates and false positives between groups with and without AI assistance. RESULTS: 260 patients with 271 FLLs and 244 patients with 240 FLLs were enrolled into the groups of non-expert and expert operators, respectively. In non-experts, FLL detection rate in the AI assistance group was significantly higher than the no AI assistance group (36.9 % vs 21.4 %, p < 0.001). In experts, FLL detection rates were not significantly different between the groups with and without AI assistance (66.7 % vs 63.3 %, p = 0.32). False positive detection rates in the groups with and without AI assistance were not significantly different in both non-experts (14.2 % vs 9.2 %, p = 0.08) and experts (8.6 % vs 9.0 %, p = 0.85). CONCLUSIONS: The AI system resulted in significant increase in detection of FLLs during ultrasound examinations by non-experts. Our findings may support future use of the AI system in resource-limited settings where ultrasound examinations are performed by non-experts. The study protocol was registered under the Thai Clinical Trial Registry (TCTR20201230003), which is part of the WHO ICTRP Registry Network. The registry can be accessed via the following URL: https://trialsearch.who.int/Trial2.aspx?TrialID=TCTR20201230003.

Student academic performance in non-lecture physiology topics following the abrupt change from traditional on-site teaching to online teaching during COVID-19 pandemic.

BACKGROUND: During the COVID-19 pandemic, pre-clerkship medical education, including all physiology classes, was obliged to change to online teaching due to limitations of on-site (face-to-face) classes. However, the effectiveness of online teaching in non-lecture physiology topics during the COVID-19 pandemic has not been thoroughly investigated. METHOD: We conducted a prospective study to evaluate the students' academic achievement and opinions on online teaching during the COVID-19 academic year. Academic achievement of 312 students in the COVID-19 year was compared with that of 299 students in the pre-COVID-19 year. Student opinions regarding social interactions and the preferred learning method were also collected. RESULTS: We found that student academic achievement in the non-lecture physiology topics, assessed by summative scores, was 4.80±0.92 percent higher in the pre-COVID-19 year than in the COVID-19 year (P < 0.01, Cohen's d = 0.42). Students rated that online classes tended to reduce their interactions with peers and teachers; however, students preferred online learning over traditional on-site learning. CONCLUSIONS: This study pointed out that students' academic performance related to the physiology topics taught by online non-lecture methods during the COVID-19 pandemic was lower than their performance when the topics were taught by the traditional (on-site) methods, although students reported that they preferred the online teaching. Hence, we suggest that medical teachers should deliberately plan and utilise a variety of tools and techniques when developing online non-lecture classes to preserve the interactivity of the classes, which might overcome this gap in students' academic performance.