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Artificial intelligence versus expert endoscopists for diagnosis of gastric cancer in patients who have undergone upper gastrointestinal endoscopy.
Niikura, Ryota; Aoki, Tomonori; Shichijo, Satoki; Yamada, Atsuo; Kawahara, Takuya; Kato, Yusuke; Hirata, Yoshihiro; Hayakawa, Yoku; Suzuki, Nobumi; Ochi, Masanori; Hirasawa, Toshiaki; Tada, Tomohiro; Kawai, Takashi; Koike, Kazuhiko.
Afiliação
  • Niikura R; Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Japan.
  • Aoki T; Gastroenterological Endoscopy, Tokyo Medical University, Tokyo, Japan.
  • Shichijo S; Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Japan.
  • Yamada A; Department of Gastrointestinal Oncology, Osaka International Cancer Institute, Osaka, Japan.
  • Kawahara T; Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Japan.
  • Kato Y; Clinical Research Promotion Center, The University of Tokyo Hospital, Tokyo, Japan.
  • Hirata Y; AI Medical Service Inc., Tokyo, Japan.
  • Hayakawa Y; Division of Advanced Genome Medicine, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan.
  • Suzuki N; Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Japan.
  • Ochi M; Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Japan.
  • Hirasawa T; Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Japan.
  • Tada T; Department of Gastroenterology, Cancer Institute Hospital Ariake, Japanese Foundation for Cancer Research, Tokyo, Japan.
  • Kawai T; AI Medical Service Inc., Tokyo, Japan.
  • Koike K; Department of Surgical Oncology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Endoscopy ; 54(8): 780-784, 2022 08.
Article em En | MEDLINE | ID: mdl-34607377
AIMS: To compare endoscopy gastric cancer images diagnosis rate between artificial intelligence (AI) and expert endoscopists. PATIENTS AND METHODS: We used the retrospective data of 500 patients, including 100 with gastric cancer, matched 1:1 to diagnosis by AI or expert endoscopists. We retrospectively evaluated the noninferiority (prespecified margin 5 %) of the per-patient rate of gastric cancer diagnosis by AI and compared the per-image rate of gastric cancer diagnosis. RESULTS: Gastric cancer was diagnosed in 49 of 49 patients (100 %) in the AI group and 48 of 51 patients (94.12 %) in the expert endoscopist group (difference 5.88, 95 % confidence interval: -0.58 to 12.3). The per-image rate of gastric cancer diagnosis was higher in the AI group (99.87 %, 747 /748 images) than in the expert endoscopist group (88.17 %, 693 /786 images) (difference 11.7 %). CONCLUSIONS: Noninferiority of the rate of gastric cancer diagnosis by AI was demonstrated but superiority was not demonstrated.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias Gástricas / Inteligência Artificial Tipo de estudo: Diagnostic_studies / Observational_studies Limite: Humans Idioma: En Revista: Endoscopy Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Japão

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias Gástricas / Inteligência Artificial Tipo de estudo: Diagnostic_studies / Observational_studies Limite: Humans Idioma: En Revista: Endoscopy Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Japão