Comparison of performances of artificial intelligence versus expert endoscopists for real-time assisted diagnosis of esophageal squamous cell carcinoma (with video)

Fukuda, Hiromu; Ishihara, Ryu; Kato, Yusuke; Matsunaga, Takashi; Nishida, Tsutomu; Yamada, Takuya; Ogiyama, Hideharu; Horie, Mai; Kinoshita, Kazuo; Tada, Takeshi

doi:10.1016/j.gie.2020.05.043

Cited by 69 publications

(95 citation statements)

References 25 publications

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“…We evaluated the computer-aided detection of ESCC from EGD videos that employed AI-based CNN with deep learning. AI diagnosis of ESCC in videos has been reported recently in other studies [17,18] . In the videos of these studies, endoscopists carefully observed the ESCC lesions and diagnosed them using AI.…”

Section: Discussionmentioning

confidence: 71%

“…In daily clinical practice, false-positive results for cancer screening are considered more acceptable than false-negative results. Adding magnifying endoscopy reportedly improves PPV [18][19] . However, we believe that the AI system without magnifying endoscopy presented here would be most useful for primary detection in clinics or hospitals without well-experienced endoscopists on staff, so we speci cally aimed to develop a non-magnifying system in this study.…”

Section: Discussionmentioning

confidence: 99%

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Ability of artificial intelligence to detect T1 esophageal squamous cell carcinoma from endoscopic videos: supportive effects of real-time assistance

Shiroma

Yoshio

Kato³

et al. 2020

Preprint

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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 ESCC 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, 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 ESCC from EGD videos with high sensitivity and improve endoscopists’ detection of ESCC with real-time support.

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Section: Discussionmentioning

confidence: 71%

Section: Discussionmentioning

confidence: 99%

Ability of artificial intelligence to detect T1 esophageal squamous cell carcinoma from endoscopic videos: supportive effects of real-time assistance

Shiroma

Yoshio

Kato³

et al. 2020

Preprint

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“…The sensitivity of the AI system in lesion detection was significantly higher than that of experts (91% vs 79%), but with significantly lower specificity (51% vs 72%). The sensitivity and specificity of the AI system in differentiating cancer from non‐cancer was higher than that of experts (86% vs 74% and 89% vs 76%, respectively) 26 . Cai et al .…”

Section: The Use Of Artificial Intelligence In the Esophagusmentioning

confidence: 97%

“…The sensitivity and specificity of the AI system in differentiating cancer from non-cancer was higher than that of experts (86% vs 74% and 89% vs 76%, respectively). 26 Cai et al trained their deep learning system using images of 1332 early SCC and 1096 normal controls. Using per-image analysis, an accuracy rate of 91.4% was achieved, compared with an accuracy rate of 88.8% by experienced endoscopists.…”

Section: The Use Of Artificial Intelligence In the Stomachmentioning

confidence: 99%

Artificial intelligence in upper GI endoscopy ‐ current status, challenges and future promise

Singh

Shin

et al. 2021

J of Gastro and Hepatol

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White‐light endoscopy with biopsy is the current gold standard modality for detecting and diagnosing upper gastrointestinal (GI) pathology. However, missed lesions remain a challenge. To overcome interobserver variability and learning curve issues, artificial intelligence (AI) has recently been introduced to assist endoscopists in the detection and diagnosis of upper GI neoplasia. In contrast to AI in colonoscopy, current AI studies for upper GI endoscopy are smaller pilot studies. Researchers currently lack large volume, well‐annotated, high‐quality datasets in gastric cancer, dysplasia in Barrett's esophagus and early esophageal squamous cell cancer. This review will look at the latest studies of AI in upper GI endoscopy, discuss some of the challenges facing researchers, and predict what the future may hold in this rapidly changing field.

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“…Deep learning, which is typically based on convolutional neural networks, is the mainstay of AI systems, which have shown good performance in visual tasks. This technology has been applied to the diagnosis of GI cancers, including esophageal SCC (15) (16) (17), and previous studies have shown that AI systems have favorable performance in the detection of ESCC (15) (18) (19) (20). In these reports, endoscopists and AI systems used the same magni ed still images (15) (18) and video images (19) (20) to diagnose the lesions.…”

Section: Introductionmentioning

confidence: 99%

Utility of an Artificial Intelligence System for Classification of Esophageal Lesions when Simulating its Clinical use

Tajiri

Ishihara

Kato³

et al. 2021

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Background:Previous reports have shown favorable performance of artificial intelligence (AI) systems for diagnosing esophageal squamous cell carcinoma (ESCC) compared with endoscopists. However, these findings don’t reflect performance in clinical situations, as endoscopists classify lesions based on both magnified and non-magnified videos, while AI systems often use only a few magnified narrow band imaging (NBI) still images. We evaluated the performance of the AI system in simulated clinical situations.Methods:We used 25,048 images from 1,433 superficial ESCC and 4,746 images from 410 noncancerous esophagi to construct our AI system. For the validation dataset, we took NBI videos of suspected superficial ESCCs. The AI system diagnosis used one magnified still image taken from each video, while 19 endoscopists used whole videos. Results:We used 147 datasets including 83 superficial ESCC and 64 non-ESCC lesions. The accuracy, sensitivity and specificity for the classification of ESCC were, respectively, 80.9%, 85.5%, and 75.0% for the AI system and 69.2%, 67.5%, and 71.5% for the endoscopists. The AI system correctly classified all ESCCs invading the muscularis mucosa or submucosa and 96.8% of lesions ≥ 20 mm, whereas even the experts misdiagnosed some of them.Conclusions:Our AI system showed higher diagnostic ability for classifying ESCC and non-ESCC than endoscopists. It may provide valuable diagnostic support to endoscopists.

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Comparison of performances of artificial intelligence versus expert endoscopists for real-time assisted diagnosis of esophageal squamous cell carcinoma (with video)

Cited by 69 publications

References 25 publications

Ability of artificial intelligence to detect T1 esophageal squamous cell carcinoma from endoscopic videos: supportive effects of real-time assistance

Ability of artificial intelligence to detect T1 esophageal squamous cell carcinoma from endoscopic videos: supportive effects of real-time assistance

Artificial intelligence in upper GI endoscopy ‐ current status, challenges and future promise

Utility of an Artificial Intelligence System for Classification of Esophageal Lesions when Simulating its Clinical use

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