Portal de Periódicos da CAPES

Effectiveness of data-augmentation on deep learning in evaluating rapid on-site cytopathology at endoscopic ultrasound-guided fine needle aspiration

2024; Nature Portfolio; Volume: 14; Issue: 1 Linguagem: Inglês

10.1038/s41598-024-72312-3

2045-2322

Yuki Fujii, Daisuke Uchida, Ryosuke Sato, Taisuke Obata, Matsumi Akihiro, Kazuya Miyamoto, Kosaku Morimoto, Hiroyuki Terasawa, Tatsuhiro Yamazaki, Kazuyuki Matsumoto, Shigeru Horiguchi, Koichiro Tsutsumi, Hironari Kato, Hirofumi Inoue, Ten Cho, Takayoshi Tanimoto, Akimitsu Ohto, Yoshiro Kawahara, Motoyuki Otsuka,

Pancreatic and Hepatic Oncology Research

Rapid on-site cytopathology evaluation (ROSE) has been considered an effective method to increase the diagnostic ability of endoscopic ultrasound-guided fine needle aspiration (EUS-FNA); however, ROSE is unavailable in most institutes worldwide due to the shortage of cytopathologists. To overcome this situation, we created an artificial intelligence (AI)-based system (the ROSE-AI system), which was trained with the augmented data to evaluate the slide images acquired by EUS-FNA. This study aimed to clarify the effects of such data-augmentation on establishing an effective ROSE-AI system by comparing the efficacy of various data-augmentation techniques. The ROSE-AI system was trained with increased data obtained by the various data-augmentation techniques, including geometric transformation, color space transformation, and kernel filtering. By performing five-fold cross-validation, we compared the efficacy of each data-augmentation technique on the increasing diagnostic abilities of the ROSE-AI system. We collected 4059 divided EUS-FNA slide images from 36 patients with pancreatic cancer and nine patients with non-pancreatic cancer. The diagnostic ability of the ROSE-AI system without data augmentation had a sensitivity, specificity, and accuracy of 87.5%, 79.7%, and 83.7%, respectively. While, some data-augmentation techniques decreased diagnostic ability, the ROSE-AI system trained only with the augmented data using the geometric transformation technique had the highest diagnostic accuracy (88.2%). We successfully developed a prototype ROSE-AI system with high diagnostic ability. Each data-augmentation technique may have various compatibilities with AI-mediated diagnostics, and the geometric transformation was the most effective for the ROSE-AI system.