Real-time automated diagnosis of precancerous lesions and early esophageal squamous cell carcinoma using a deep learning model (with videos)

Guo, Linjie; Xiao, Xiao; Wu, Chuncheng; Zeng, Xianhui; Zhang, Yuhang; Du, Jiang; Bai, Shan; Xie, Jinwei; Zhang, Zhiwei; Li, Yuhong; Wang, Xuedan; Cheung, Onpan; Sharma, Malay; Liu, Jingjia; Hu, Bing

doi:10.1016/j.gie.2019.08.018

Cited by 146 publications

(145 citation statements)

References 16 publications

Supporting

Mentioning

142

Contrasting

Order By: Relevance

“…The accuracy, sensitivity, and specificity for abnormal IPCL patterns were 93.7%, 89.3%, and 98%, respectively. Another recent study introduced a computer-aided diagnosis system for the real-time automated diagnosis of precancerous lesions and SESCCs in 6473 narrow-band imaging images [21]. The sensitivity and specificity for diagnosing precancerous lesions and SESCCs were 98.0% and 95.0%, respectively.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Intelligent Computer-Aided Diagnostic System for Magnifying Endoscopy Images of Superficial Esophageal Squamous Cell Carcinoma

Kim

et al. 2020

Applied Sciences

View full text Add to dashboard Cite

Predicting the depth of invasion of superficial esophageal squamous cell carcinomas (SESCCs) is important when selecting treatment modalities such as endoscopic or surgical resections. Recently, the Japanese Esophageal Society (JES) proposed a new simplified classification for magnifying endoscopy findings of SESCCs to predict the depth of tumor invasion based on intrapapillary capillary loops with the SESCC microvessels classified into the B1, B2, and B3 types. In this study, a four-step classification method for SESCCs is proposed. First, Niblack’s method was applied to endoscopy images to select a candidate region of microvessels. Second, the background regions were delineated from the vessel area using the high-speed fast Fourier transform and adaptive resonance theory 2 algorithm. Third, the morphological characteristics of the vessels were extracted. Based on the extracted features, the support vector machine algorithm was employed to classify the microvessels into the B1 and non-B1 types. Finally, following the automatic measurement of the microvessel caliber using the proposed method, the non-B1 types were sub-classified into the B2 and B3 types via comparisons with the caliber of the surrounding microvessels. In the experiments, 114 magnifying endoscopy images (47 B1-type, 48 B2-type, and 19 B3-type images) were used to classify the characteristics of SESCCs. The accuracy, sensitivity, and specificity of the classification into the B1 and non-B1 types were 83.3%, 74.5%, and 89.6%, respectively, while those for the classification of the B2 and B3 types in the non-B1 types were 73.1%, 73.7%, and 72.9%, respectively. The proposed machine learning based computer-aided diagnostic system could obtain the objective data by analyzing the pattern and caliber of the microvessels with acceptable performance. Further studies are necessary to carefully validate the clinical utility of the proposed system.

show abstract

Section: Discussionmentioning

confidence: 99%

“…In Equation (20), F even (u) and F odd (u) represent the calculation results for the even and odd data, respectively. F(u) can be expressed as shown in Equation (21).…”

Section: Noise Elimination In the Vascular Boundary Region Using A Famentioning

confidence: 99%

Intelligent Computer-Aided Diagnostic System for Magnifying Endoscopy Images of Superficial Esophageal Squamous Cell Carcinoma

Kim

et al. 2020

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Guo et al [4] propose a CNN that can classify images as dysplastic or non-dysplastic. Using a dataset of 6671 images, they demonstrate per-frame sensitivity of 98% for the detection of ESCN.…”

Section: Related Workmentioning

confidence: 99%

Intrapapillary capillary loop classification in magnification endoscopy: open dataset and baseline methodology

García-Peraza-Herrera

Everson

Lovat

et al. 2020

Int J CARS

View full text Add to dashboard Cite

Purpose Early squamous cell neoplasia (ESCN) in the oesophagus is a highly treatable condition. Lesions confined to the mucosal layer can be curatively treated endoscopically. We build a computer-assisted detection system that can classify still images or video frames as normal or abnormal with high diagnostic accuracy. Methods We present a new benchmark dataset containing 68K binary labelled frames extracted from 114 patient videos whose imaged areas have been resected and correlated to histopathology. Our novel convolutional network architecture solves the binary classification task and explains what features of the input domain drive the decision-making process of the network. Results The proposed method achieved an average accuracy of 91.7% compared to the 94.7% achieved by a group of 12 senior clinicians. Our novel network architecture produces deeply supervised activation heatmaps that suggest the network is looking at intrapapillary capillary loop patterns when predicting abnormality. Conclusion We believe that this dataset and baseline method may serve as a reference for future benchmarks on both video frame classification and explainability in the context of ESCN detection. A future work path of high clinical relevance is the extension of the classification to ESCN types.

show abstract

“…Unaltered, full-range normal esophagus videos included 33 videos (per-frame specificity, 99.9%; per-case specificity, 90.9%). The model was capable of processing at least 25 frames per second, with a latency period of <100 ms. 4 Despite the high sensitivity and specificity, this study had several limitations, including inability to detect advanced esophageal adenocarcinoma, dependence on the background color, exclusion of suboptimal quality images from the training dataset causing preselection bias, and the need for future randomized controlled trials to validate the findings. 4 Although the exclusion of images of suboptimal quality creates preselection bias, it also has implications for the adoption of AI in general endoscopy.…”

mentioning

confidence: 97%

“…In the recent study by Guo et al, 4 the authors developed a computer-assisted diagnostic (CAD) system for real-time automated diagnosis of precancerous lesions and early esophageal squamous cell carcinoma (ESCC) to assist in the diagnosis of early esophageal cancer. The CAD model was trained on over 6000 narrow-band images, including precancerous lesions, early ESCC, and noncancerous lesions and was validated by the use of both endoscopic images and video datasets.…”

mentioning

confidence: 99%

Artificial intelligence for real-time detection of early esophageal cancer: another set of eyes to better visualize

Thakkar

Kochhar

2020

Gastrointestinal Endoscopy

View full text Add to dashboard Cite

Real-time automated diagnosis of precancerous lesions and early esophageal squamous cell carcinoma using a deep learning model (with videos)

Cited by 146 publications

References 16 publications

Intelligent Computer-Aided Diagnostic System for Magnifying Endoscopy Images of Superficial Esophageal Squamous Cell Carcinoma

Intelligent Computer-Aided Diagnostic System for Magnifying Endoscopy Images of Superficial Esophageal Squamous Cell Carcinoma

Intrapapillary capillary loop classification in magnification endoscopy: open dataset and baseline methodology

Artificial intelligence for real-time detection of early esophageal cancer: another set of eyes to better visualize

Contact Info

Product

Resources

About