首页|Xi'an Medical University Reports Findings in Artificial Intelligence (A study on the improvement in the ability of endoscopists to diag- nose gastric neoplasms using an artificial intelligence system)
Xi'an Medical University Reports Findings in Artificial Intelligence (A study on the improvement in the ability of endoscopists to diag- nose gastric neoplasms using an artificial intelligence system)
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New research on Artificial Intelligence is the subject of a report. According to news re- porting out of Xi'an, People's Republic of China, by NewsRx editors, research stated, "Artificial intelligence- assisted gastroscopy (AIAG) based on deep learning has been validated in various scenarios, but there is a lack of studies regarding diagnosing neoplasms under white light endoscopy. This study explored the potential role of AIAG systems in enhancing the ability of endoscopists to diagnose gastric tumor lesions under white light." Our news journalists obtained a quote from the research from Xi'an Medical University, "A total of 251 patients with complete pathological information regarding electronic gastroscopy, biopsy, or ESD surgery in Xi'an Gaoxin Hospital were retrospectively collected and comprised 64 patients with neoplasm lesions (excluding advanced cancer) and 187 patients with non-neoplasm lesions. The diagnosis competence of endoscopists with intermediate experience and experts was compared for gastric neoplasms with or without the assistance of AIAG, which was developed based on ResNet-50. For the 251 patients with difficult clinical diagnoses included in the study, compared with endoscopists with intermediate experience, AIAG's diagnostic competence was much higher, with a sensitivity of 79.69% (79.69% vs. 72.50%, = 0.012) and a specificity of 73.26% (73.26% vs. 52.62%, ＜0.001). With the help of AIAG, the endoscopists with intermediate experience (＜8 years) demonstrated a relatively higher specificity (59.79% vs. 52.62%, ＜0.001). Experts ( 8 years) had similar results with or without AI assistance (with AI vs. without AI; sensitivities, 70.31% vs. 67.81%, = 0.358; specificities, 83.85% vs. 85.88%, = 0.116)."
Xi'anPeople's Republic of ChinaAsiaArtificial IntelligenceEmerging TechnologiesHealth and MedicineMachine LearningNeoplasms
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NSTL
Frontiers Media Sa
