Ability of artificial intelligence to detect T1 esophageal squamous cell carcinoma from endoscopic videos and the effects of real-time assistance
Abstract Diagnosis using artificial intelligence (AI) with deep learning could be useful in endoscopic examinations. We investigated the ability of AI to detect superficial esophageal squamous cell carcinoma (ESCC) from esophagogastroduodenoscopy (EGD) videos. We retrospectively collected 8428 EGD i...
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Nature Portfolio
2021
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oai:doaj.org-article:76434d4207d84508aeed12b069a34a1c2021-12-02T14:37:15ZAbility of artificial intelligence to detect T1 esophageal squamous cell carcinoma from endoscopic videos and the effects of real-time assistance10.1038/s41598-021-87405-62045-2322https://doaj.org/article/76434d4207d84508aeed12b069a34a1c2021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-87405-6https://doaj.org/toc/2045-2322Abstract Diagnosis using artificial intelligence (AI) with deep learning could be useful in endoscopic examinations. We investigated the ability of AI to detect superficial esophageal squamous cell carcinoma (ESCC) from esophagogastroduodenoscopy (EGD) videos. We retrospectively collected 8428 EGD images of esophageal cancer to develop a convolutional neural network through deep learning. We evaluated the detection accuracy of the AI diagnosing system compared with that of 18 endoscopists. We used 144 EGD videos for the two validation sets. First, we used 64 EGD observation videos of ESCCs using both white light imaging (WLI) and narrow-band imaging (NBI). We then evaluated the system using 80 EGD videos from 40 patients (20 with superficial ESCC and 20 with non-ESCC). In the first set, the AI system correctly diagnosed 100% ESCCs. In the second set, it correctly detected 85% (17/20) ESCCs. Of these, 75% (15/20) and 55% (11/22) were detected by WLI and NBI, respectively, and the positive predictive value was 36.7%. The endoscopists correctly detected 45% (25–70%) ESCCs. With AI real-time assistance, the sensitivities of the endoscopists were significantly improved without AI assistance (p < 0.05). AI can detect superficial ESCCs from EGD videos with high sensitivity and the sensitivity of the endoscopist was improved with AI real-time support.Sho ShiromaToshiyuki YoshioYusuke KatoYoshimasa HorieKen NamikawaYoshitaka TokaiShoichi YoshimizuNatsuko YoshizawaYusuke HoriuchiAkiyoshi IshiyamaToshiaki HirasawaTomohiro TsuchidaNaoki AkazawaJunichi AkiyamaTomohiro TadaJunko FujisakiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021) |
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Medicine R Science Q Sho Shiroma Toshiyuki Yoshio Yusuke Kato Yoshimasa Horie Ken Namikawa Yoshitaka Tokai Shoichi Yoshimizu Natsuko Yoshizawa Yusuke Horiuchi Akiyoshi Ishiyama Toshiaki Hirasawa Tomohiro Tsuchida Naoki Akazawa Junichi Akiyama Tomohiro Tada Junko Fujisaki Ability of artificial intelligence to detect T1 esophageal squamous cell carcinoma from endoscopic videos and the effects of real-time assistance |
| description |
Abstract Diagnosis using artificial intelligence (AI) with deep learning could be useful in endoscopic examinations. We investigated the ability of AI to detect superficial esophageal squamous cell carcinoma (ESCC) from esophagogastroduodenoscopy (EGD) videos. We retrospectively collected 8428 EGD images of esophageal cancer to develop a convolutional neural network through deep learning. We evaluated the detection accuracy of the AI diagnosing system compared with that of 18 endoscopists. We used 144 EGD videos for the two validation sets. First, we used 64 EGD observation videos of ESCCs using both white light imaging (WLI) and narrow-band imaging (NBI). We then evaluated the system using 80 EGD videos from 40 patients (20 with superficial ESCC and 20 with non-ESCC). In the first set, the AI system correctly diagnosed 100% ESCCs. In the second set, it correctly detected 85% (17/20) ESCCs. Of these, 75% (15/20) and 55% (11/22) were detected by WLI and NBI, respectively, and the positive predictive value was 36.7%. The endoscopists correctly detected 45% (25–70%) ESCCs. With AI real-time assistance, the sensitivities of the endoscopists were significantly improved without AI assistance (p < 0.05). AI can detect superficial ESCCs from EGD videos with high sensitivity and the sensitivity of the endoscopist was improved with AI real-time support. |
| format |
article |
| author |
Sho Shiroma Toshiyuki Yoshio Yusuke Kato Yoshimasa Horie Ken Namikawa Yoshitaka Tokai Shoichi Yoshimizu Natsuko Yoshizawa Yusuke Horiuchi Akiyoshi Ishiyama Toshiaki Hirasawa Tomohiro Tsuchida Naoki Akazawa Junichi Akiyama Tomohiro Tada Junko Fujisaki |
| author_facet |
Sho Shiroma Toshiyuki Yoshio Yusuke Kato Yoshimasa Horie Ken Namikawa Yoshitaka Tokai Shoichi Yoshimizu Natsuko Yoshizawa Yusuke Horiuchi Akiyoshi Ishiyama Toshiaki Hirasawa Tomohiro Tsuchida Naoki Akazawa Junichi Akiyama Tomohiro Tada Junko Fujisaki |
| author_sort |
Sho Shiroma |
| title |
Ability of artificial intelligence to detect T1 esophageal squamous cell carcinoma from endoscopic videos and the effects of real-time assistance |
| title_short |
Ability of artificial intelligence to detect T1 esophageal squamous cell carcinoma from endoscopic videos and the effects of real-time assistance |
| title_full |
Ability of artificial intelligence to detect T1 esophageal squamous cell carcinoma from endoscopic videos and the effects of real-time assistance |
| title_fullStr |
Ability of artificial intelligence to detect T1 esophageal squamous cell carcinoma from endoscopic videos and the effects of real-time assistance |
| title_full_unstemmed |
Ability of artificial intelligence to detect T1 esophageal squamous cell carcinoma from endoscopic videos and the effects of real-time assistance |
| title_sort |
ability of artificial intelligence to detect t1 esophageal squamous cell carcinoma from endoscopic videos and the effects of real-time assistance |
| publisher |
Nature Portfolio |
| publishDate |
2021 |
| url |
https://doaj.org/article/76434d4207d84508aeed12b069a34a1c |
| work_keys_str_mv |
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