Endoscopy 2022; 54(11): E622-E623
DOI: 10.1055/a-1724-6958
E-Videos

Real-time identification of gastric lesions and anatomical landmarks by artificial intelligence during magnetically controlled capsule endoscopy

Jun Pan*
1   National Clinical Research Center for Digestive Diseases, Department of Gastroenterology, Changhai Hospital, Shanghai, China
,
Ji Xia*
1   National Clinical Research Center for Digestive Diseases, Department of Gastroenterology, Changhai Hospital, Shanghai, China
2   Department of Gastroenterology, No. 926 Hospital, Yunnan, China
,
Bin Jiang
1   National Clinical Research Center for Digestive Diseases, Department of Gastroenterology, Changhai Hospital, Shanghai, China
3   Department of Gastroenterology, No. 422 Hospital, Guangdong, China
,
Hang Zhang
4   Ankon Technologies Co., Ltd., Wuhan, China
,
Hao Zhang
4   Ankon Technologies Co., Ltd., Wuhan, China
,
Zhao-Shen Li
1   National Clinical Research Center for Digestive Diseases, Department of Gastroenterology, Changhai Hospital, Shanghai, China
,
1   National Clinical Research Center for Digestive Diseases, Department of Gastroenterology, Changhai Hospital, Shanghai, China
› Author Affiliations
Supported by: Shanghai Municipal Hospital Emerging Frontier Technology Joint Project SHDC12019105
Supported by: National Natural Science Foundation of China 81900600
Supported by: “Ten Thousand Plan”-National High Level Talents Special Support Plan na
 

Artificial intelligence (AI) has revolutionized the diagnosis of gastrointestinal endoscopy, including capsule endoscopy. In our previous study, we developed and validated an AI-based auxiliary system for diagnosing gastric lesions based on still images [1]. Here, we demonstrate the performance of the first AI-based real-time diagnostic system in magnetically controlled capsule endoscopy (MCE) for detecting gastric lesions (Smart Data Service System-AI [SDSS-AI]; Ankon Technologies Co., Ltd., Wuhan, China) ([Fig. 1]).

Zoom Image
Fig. 1 Monitor interface of the artificial intelligence-based real-time diagnostic system in magnetically controlled capsule endoscopy.

A total of 34 062 MCE images from 856 patients treated at Changhai Hospital from January 2016 to October 2019 were used to train the SDSS-AI system. In addition, 50 patients referred for MCE at Changhai Hospital from December 2019 to January 2020 were enrolled to evaluate the diagnostic accuracy of SDSS-AI, using expert readings as the gold standard. Overall sensitivity of SDSS-AI for detecting gastric lesions was 98.9 % (95 % confidence interval [CI], 93.3 %–99.9 %), with sensitivities of 98.7 % (95 %CI 91.9 %–99.9 %) and 100 % (95 %CI 77.1 %–100 %) for detecting gastric erosion/bleeding/ulcer and polyp/submucosal tumor, respectively ([Fig. 2], [Fig. 3], [Fig. 4], [Fig. 5]). Overall accuracy of SDSS-AI for identifying gastric anatomical landmarks was 94.2 % (95 %CI 92.9 %–95.2 %), with accuracies of 97.8 % (95 %CI 95.7 %–98.9 %), 96.5 % (95 %CI 94.2 %–98.0 %), 73.8 % (95 %CI 69.2 %–77.8 %), 96.0 % (95 %CI 93.6 %–97.6 %), 98.0 % (95 %CI 96.0 %–99.1 %), 96.0 % (95 %CI 93.6 %–97.6 %), 96.8 % (95 %CI 94.5 %–98.2 %), and 98.8 % (95 %CI 97.0 %–99.6 %) for identifying cardia, fundus, body, greater curvature, lesser curvature, angulus, antrum and pylorus, respectively. Image processing time of the system was 94 ms per image ([Video 1]).

Zoom Image
Fig. 2 Real-time identification of erosion/bleeding/ulcer (blue frame) in the gastric antrum.
Zoom Image
Fig. 3 Lesion detection by heat map in the gastric antrum.
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Fig. 4 Real-time identification of erosion/bleeding/ulcer (blue frame) in the gastric body.
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Fig. 5 Lesion detection by heat map in the gastric body.

Video 1 Performance of the artificial intelligence-based real-time diagnostic system in magnetically controlled capsule endoscopy for identifying gastric lesions and anatomical landmarks.


Quality:

In summary, SDSS-AI is a promising tool for real-time diagnosis and localization of gastric lesions in MCE examination, and aids physicians in improving lesion detection and avoiding blind spots. Further improvement of the deep learning system is needed, and studies with large sample sizes are warranted to evaluate the accuracy and efficacy of the system.

Endoscopy_UCTN_Code_CCL_1AG_2AF

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Correction

Real-time identification of gastric lesions and anatomical landmarks by artificial intelligence during magnetically controlled capsule endoscopy
Pan J, Xia J, Jiang B et al. Real-time identification of gastric lesions and anatomical landmarks by artificial intelligence during magnetically controlled capsule endoscopy. Endoscopy 2022, doi:10.1055/a-1724-6958
In the above-mentioned article, the authorship has been corrected. Drs. Pan and Xia are contributing equally. This was corrected in the online version on February 8, 2022.


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Competing interests

The authors declare that they have no conflict of interest.

* Drs. Pan and Xia contributed equally to this work.



Corresponding author

Zhuan Liao, MD
National Clinical Research Center for Digestive Diseases
Department of Gastroenterology
Changhai Hospital
168 Changhai Road
Shanghai 200433
China   

Publication History

Article published online:
26 January 2022

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Zoom Image
Fig. 1 Monitor interface of the artificial intelligence-based real-time diagnostic system in magnetically controlled capsule endoscopy.
Zoom Image
Fig. 2 Real-time identification of erosion/bleeding/ulcer (blue frame) in the gastric antrum.
Zoom Image
Fig. 3 Lesion detection by heat map in the gastric antrum.
Zoom Image
Fig. 4 Real-time identification of erosion/bleeding/ulcer (blue frame) in the gastric body.
Zoom Image
Fig. 5 Lesion detection by heat map in the gastric body.