Dataset Information

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 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.

SUBMITTER: Shiroma S

PROVIDER: S-EPMC8032773 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature

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Ability of artificial intelligence to detect T1 esophageal squamous cell carcinoma from endoscopic videos and the effects of real-time assistance.

Shiroma Sho S Yoshio Toshiyuki T Kato Yusuke Y Horie Yoshimasa Y Namikawa Ken K Tokai Yoshitaka Y Yoshimizu Shoichi S Yoshizawa Natsuko N Horiuchi Yusuke Y Ishiyama Akiyoshi A Hirasawa Toshiaki T Tsuchida Tomohiro T Akazawa Naoki N Akiyama Junichi J Tada Tomohiro T Fujisaki Junko J

Scientific reports 20210408 1

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 EG ...[more]

PMID: 33833355

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Project description:BackgroundThe use of artificial intelligence (AI) assisted white light imaging (WLI) detection systems for superficial esophageal squamous cell carcinoma (SESCC) is limited by training with images from one specific endoscopy platform.MethodsIn this study, we developed an AI system with a convolutional neural network (CNN) model using WLI images from Olympus and Fujifilm endoscopy platforms. The training dataset consisted of 5892 WLI images from 1283 patients, and the validation dataset included 4529 images from 1224 patients. We assessed the diagnostic performance of the AI system and compared it with that of endoscopists. We analyzed the system's ability to identify cancerous imaging characteristics and investigated the efficacy of the AI system as an assistant in diagnosis.ResultsIn the internal validation set, the AI system's per-image analysis had a sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) of 96.64%, 95.35%, 91.75%, 90.91%, and 98.33%, respectively. In patient-based analysis, these values were 90.17%, 94.34%, 88.38%, 89.50%, and 94.72%, respectively. The diagnostic results in the external validation set were also favorable. The CNN model's diagnostic performance in recognizing cancerous imaging characteristics was comparable to that of expert endoscopists and significantly higher than that of mid-level and junior endoscopists. This model was competent in localizing SESCC lesions. Manual diagnostic performances were significantly improved with the assistance by AI system, especially in terms of accuracy (75.12% vs. 84.95%, p = 0.008), specificity (63.29% vs. 76.59%, p = 0.017) and PPV (64.95% vs. 75.23%, p = 0.006).ConclusionsThe results of this study demonstrate that the developed AI system is highly effective in automatically recognizing SESCC, displaying impressive diagnostic performance, and exhibiting strong generalizability. Furthermore, when used as an assistant in the diagnosis process, the system improved manual diagnostic performance.

Project description:In the gastroenterology field, the impact of artificial intelligence was investigated for the purposes of diagnostics, risk stratification of patients, improvement in quality of endoscopic procedures and early detection of neoplastic diseases, implementation of the best treatment strategy, and optimization of patient prognosis. Computer-assisted diagnostic systems to evaluate upper endoscopy images have recently emerged as a supporting tool in endoscopy due to the risks of misdiagnosis related to standard endoscopy and different expertise levels of endoscopists, time-consuming procedures, lack of availability of advanced procedures, increasing workloads, and development of endoscopic mass screening programs. Recent research has tended toward computerized, automatic, and real-time detection of lesions, which are approaches that offer utility in daily practice. Despite promising results, certain studies might overexaggerate the diagnostic accuracy of artificial systems, and several limitations remain to be overcome in the future. Therefore, additional multicenter randomized trials and the development of existent database platforms are needed to certify clinical implementation. This paper presents an overview of the literature and the current knowledge of the usefulness of different types of machine learning systems in the assessment of premalignant and malignant esophageal lesions via conventional and advanced endoscopic procedures. This study makes a presentation of the artificial intelligence terminology and refers also to the most prominent recent research on computer-assisted diagnosis of neoplasia on Barrett's esophagus and early esophageal squamous cell carcinoma, and prediction of invasion depth in esophageal neoplasms. Furthermore, this review highlights the main directions of future doctor-computer collaborations in which machines are expected to improve the quality of medical action and routine clinical workflow, thus reducing the burden on physicians.

Project description:Background and objectiveBleeding following endoscopic variceal ligation (EVL) may occur as a result of numerous factors, including a diameter of esophageal varices (EV) that is too large to be completely ligated. The present study aimed to develop an artificial intelligence-based endoscopic virtual ruler (EVR) to measure the diameter of EV with a view to finding more suitable cases for EVL.MethodsThe present study was a multicenter retrospective study that included a total of 1,062 EVLs in 727 patients with liver cirrhosis with EV, who underwent EVL from April 2016 to March 2023. Patients were divided into early rebleeding (n = 80) and non-rebleeding groups (n = 982) according to whether postoperative bleeding occurred at 6 weeks. The characteristics of patient baseline data, the status of rebleeding at 6 weeks after surgery and the survival status at 6 weeks after rebleeding were analyzed.ResultsThe early rebleeding rate following 1,062 EVL procedures was 7.5%, and the mortality rate at 6 weeks after bleeding was 16.5%. Results of the one-way binary logistic regression analysis demonstrated that the risk factors for early rebleeding following EVL included: high TB (P = 0.009), low Alb (P = 0.001), high PT (P = 0.004), PVT (P = 0.026), HCC (P = 0.018), high Child-Pugh score (P < 0.001), Child-Pugh grade C(P < 0.001), high MELD score(P = 0.004), Japanese variceal grade F3 (P < 0.001), diameter of EV (P < 0.001), and number of ligature rings (P = 0.029). Results of the multifactorial binary logistic regression analysis demonstrated that Child-Pugh grade C (P = 0.007), Japanese variceal grade F3 (P = 0.009), and diameter of EV (P < 0.001) may exhibit potential in predicting early rebleeding following EVL. ROC analysis demonstrated that the area under curve (AUC) for EV diameter was 0.848, and the AUC for Japanese variceal grade was 0.635, which was statistically significant (P < 0.001). Thus, results of the present study demonstrated that EV diameter was more optimal in predicting early rebleeding following EVL than Japanese variceal grade criteria. The cut-off value of EV diameter was calculated to be 1.35 cm (sensitivity, 70.0%; specificity, 89.2%).ConclusionIf the diameter of EV is ≥1.4 cm, there may be a high risk of early rebleeding following EVL surgery; thus, we recommend caution with EVL.

Project description:BackgroundEarly screening and treatment of esophageal cancer (EC) is particularly important for the survival and prognosis of patients. However, early EC is difficult to diagnose by a routine endoscopic examination. Therefore, convolutional neural network (CNN)-based artificial intelligence (AI) has become a very promising method in the diagnosis of early EC using endoscopic images. The aim of this study was to evaluate the diagnostic performance of CNN-based AI for detecting early EC based on endoscopic images.MethodsA comprehensive search was performed to identify relevant English articles concerning CNN-based AI in the diagnosis of early EC based on endoscopic images (from the date of database establishment to April 2022). The pooled sensitivity (SEN), pooled specificity (SPE), positive likelihood ratio (LR+), negative likelihood ratio (LR-), diagnostic odds ratio (DOR) with 95% confidence interval (CI), summary receiver operating characteristic (SROC) curve, and area under the curve (AUC) for the accuracy of CNN-based AI in the diagnosis of early EC based on endoscopic images were calculated. We used the I2 test to assess heterogeneity and investigated the source of heterogeneity by performing meta-regression analysis. Publication bias was assessed using Deeks' funnel plot asymmetry test.ResultsSeven studies met the eligibility criteria. The SEN and SPE were 0.90 (95% confidence interval [CI]: 0.82-0.94) and 0.91 (95% CI: 0.79-0.96), respectively. The LR+ of the malignant ultrasonic features was 9.8 (95% CI: 3.8-24.8) and the LR- was 0.11 (95% CI: 0.06-0.21), revealing that CNN-based AI exhibited an excellent ability to confirm or exclude early EC on endoscopic images. Additionally, SROC curves showed that the AUC of the CNN-based AI in the diagnosis of early EC based on endoscopic images was 0.95 (95% CI: 0.93-0.97), demonstrating that CNN-based AI has good diagnostic value for early EC based on endoscopic images.ConclusionsBased on our meta-analysis, CNN-based AI is an excellent diagnostic tool with high sensitivity, specificity, and AUC in the diagnosis of early EC based on endoscopic images.

Dataset Information

Ability of artificial intelligence to detect T1 esophageal squamous cell carcinoma from endoscopic videos and the effects of real-time assistance.

Publications

Ability of artificial intelligence to detect T1 esophageal squamous cell carcinoma from endoscopic videos and the effects of real-time assistance.

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