A study on the improvement in the ability of endoscopists to diagnose gastric neoplasms using an artificial intelligence system.

BACKGROUND

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.

METHODS

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.

RESULTS

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).

CONCLUSION

With the assistance of artificial intelligence (AI) systems, the ability of endoscopists with intermediate experience to diagnose gastric neoplasms is significantly improved, but AI systems have little effect on experts.