Comparing Raman Spectroscopy-Based Artificial Intelligence to High-Definition White Light Endoscopy for Endoscopic Diagnosis of Gastric Neoplasia: A Feasibility Proof-of-Concept Study
Background: Endoscopic assessment for the diagnosis of gastric cancer is limited by interoperator variability and lack of real-time capability. Recently, Raman spectroscopy-based artificial intelligence (AI) has been proposed as a solution to overcome these limitations. Objective: To compare the per...
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MDPI AG
2024-12-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/14/24/2839 |
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| author | Tse Kiat Soong Guo Wei Kim Daryl Kai Ann Chia Jimmy Bok Yan So Jonathan Wei Jie Lee Asim Shabbbir Jeffrey Huey Yew Lum Gwyneth Shook Ting Soon Khek Yu Ho |
| author_facet | Tse Kiat Soong Guo Wei Kim Daryl Kai Ann Chia Jimmy Bok Yan So Jonathan Wei Jie Lee Asim Shabbbir Jeffrey Huey Yew Lum Gwyneth Shook Ting Soon Khek Yu Ho |
| author_sort | Tse Kiat Soong |
| collection | DOAJ |
| description | Background: Endoscopic assessment for the diagnosis of gastric cancer is limited by interoperator variability and lack of real-time capability. Recently, Raman spectroscopy-based artificial intelligence (AI) has been proposed as a solution to overcome these limitations. Objective: To compare the performance of the AI-enabled Raman spectroscopy with that of high-definition white light endoscopy (HD-WLE) for the risk classification of gastric lesions. Methods: This was a randomized double-arm feasibility proof-of-concept trial in which participants with suspected gastric neoplasia underwent endoscopic assessment using either the Raman spectroscopy-based AI (SPECTRA IMDx™) or HD-WLE performed by expert endoscopists. Identified lesions were classified in real time as having either low or high risk for neoplasia. Diagnostic outcomes were compared between the two groups using histopathology as the reference. Results: A total of 20 patients with 25 lesions were included in the study. SPECTRA, in real-time, performed at a statistically similar level to that of HD-WLE performed by expert endoscopists, achieving an overall sensitivity, specificity, and accuracy of 100%, 80%, and 89.0%, respectively, by patient; and 100%, 80%, and 92%, respectively, by lesion, while expert endoscopists using HD-WLE attained a sensitivity, specificity, and accuracy of 100%, 80%, and 90%, respectively, by patient; and 100%, 83.3%, and 91.7%, respectively, by lesion, in differentiating high-risk from low-risk gastric lesions. Conclusions: The SPECTRA’s comparable performance with that of HD-WLE suggests that it can potentially be a valuable adjunct for less experienced endoscopists to attain accurate and real-time diagnoses of gastric lesions. Larger-scale prospective randomized trials are recommended to validate these promising results further. |
| format | Article |
| id | doaj-art-b37a3ee3dd5549a1a85245f9830df8f5 |
| institution | Kabale University |
| issn | 2075-4418 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj-art-b37a3ee3dd5549a1a85245f9830df8f52024-12-27T14:20:54ZengMDPI AGDiagnostics2075-44182024-12-011424283910.3390/diagnostics14242839Comparing Raman Spectroscopy-Based Artificial Intelligence to High-Definition White Light Endoscopy for Endoscopic Diagnosis of Gastric Neoplasia: A Feasibility Proof-of-Concept StudyTse Kiat Soong0Guo Wei Kim1Daryl Kai Ann Chia2Jimmy Bok Yan So3Jonathan Wei Jie Lee4Asim Shabbbir5Jeffrey Huey Yew Lum6Gwyneth Shook Ting Soon7Khek Yu Ho8Department of Surgery, National University Hospital, NUHS Tower Block Level 8, 1E Kent Ridge Road, Singapore 119228, SingaporeCrest Surgical Practice, #08-03 Gleneagles Medical Centre, 6 Napier Rd, Singapore 258499, SingaporeDepartment of Surgery, National University Hospital, NUHS Tower Block Level 8, 1E Kent Ridge Road, Singapore 119228, SingaporeDepartment of Surgery, National University Hospital, NUHS Tower Block Level 8, 1E Kent Ridge Road, Singapore 119228, SingaporeDepartment of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, NUHS Tower Block Level 10, 1E Kent Ridge Road, Singapore 119228, SingaporeDepartment of Surgery, National University Hospital, NUHS Tower Block Level 8, 1E Kent Ridge Road, Singapore 119228, SingaporeDepartment of Pathology, National University Hospital, 1 Main Building, Level 3, 5 Lower Kent Ridge Road, Singapore 119074, SingaporeDepartment of Pathology, National University Hospital, 1 Main Building, Level 3, 5 Lower Kent Ridge Road, Singapore 119074, SingaporeDepartment of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, NUHS Tower Block Level 10, 1E Kent Ridge Road, Singapore 119228, SingaporeBackground: Endoscopic assessment for the diagnosis of gastric cancer is limited by interoperator variability and lack of real-time capability. Recently, Raman spectroscopy-based artificial intelligence (AI) has been proposed as a solution to overcome these limitations. Objective: To compare the performance of the AI-enabled Raman spectroscopy with that of high-definition white light endoscopy (HD-WLE) for the risk classification of gastric lesions. Methods: This was a randomized double-arm feasibility proof-of-concept trial in which participants with suspected gastric neoplasia underwent endoscopic assessment using either the Raman spectroscopy-based AI (SPECTRA IMDx™) or HD-WLE performed by expert endoscopists. Identified lesions were classified in real time as having either low or high risk for neoplasia. Diagnostic outcomes were compared between the two groups using histopathology as the reference. Results: A total of 20 patients with 25 lesions were included in the study. SPECTRA, in real-time, performed at a statistically similar level to that of HD-WLE performed by expert endoscopists, achieving an overall sensitivity, specificity, and accuracy of 100%, 80%, and 89.0%, respectively, by patient; and 100%, 80%, and 92%, respectively, by lesion, while expert endoscopists using HD-WLE attained a sensitivity, specificity, and accuracy of 100%, 80%, and 90%, respectively, by patient; and 100%, 83.3%, and 91.7%, respectively, by lesion, in differentiating high-risk from low-risk gastric lesions. Conclusions: The SPECTRA’s comparable performance with that of HD-WLE suggests that it can potentially be a valuable adjunct for less experienced endoscopists to attain accurate and real-time diagnoses of gastric lesions. Larger-scale prospective randomized trials are recommended to validate these promising results further.https://www.mdpi.com/2075-4418/14/24/2839artificial intelligenceraman spectroscopyendoscopygastric cancerdiagnosis |
| spellingShingle | Tse Kiat Soong Guo Wei Kim Daryl Kai Ann Chia Jimmy Bok Yan So Jonathan Wei Jie Lee Asim Shabbbir Jeffrey Huey Yew Lum Gwyneth Shook Ting Soon Khek Yu Ho Comparing Raman Spectroscopy-Based Artificial Intelligence to High-Definition White Light Endoscopy for Endoscopic Diagnosis of Gastric Neoplasia: A Feasibility Proof-of-Concept Study Diagnostics artificial intelligence raman spectroscopy endoscopy gastric cancer diagnosis |
| title | Comparing Raman Spectroscopy-Based Artificial Intelligence to High-Definition White Light Endoscopy for Endoscopic Diagnosis of Gastric Neoplasia: A Feasibility Proof-of-Concept Study |
| title_full | Comparing Raman Spectroscopy-Based Artificial Intelligence to High-Definition White Light Endoscopy for Endoscopic Diagnosis of Gastric Neoplasia: A Feasibility Proof-of-Concept Study |
| title_fullStr | Comparing Raman Spectroscopy-Based Artificial Intelligence to High-Definition White Light Endoscopy for Endoscopic Diagnosis of Gastric Neoplasia: A Feasibility Proof-of-Concept Study |
| title_full_unstemmed | Comparing Raman Spectroscopy-Based Artificial Intelligence to High-Definition White Light Endoscopy for Endoscopic Diagnosis of Gastric Neoplasia: A Feasibility Proof-of-Concept Study |
| title_short | Comparing Raman Spectroscopy-Based Artificial Intelligence to High-Definition White Light Endoscopy for Endoscopic Diagnosis of Gastric Neoplasia: A Feasibility Proof-of-Concept Study |
| title_sort | comparing raman spectroscopy based artificial intelligence to high definition white light endoscopy for endoscopic diagnosis of gastric neoplasia a feasibility proof of concept study |
| topic | artificial intelligence raman spectroscopy endoscopy gastric cancer diagnosis |
| url | https://www.mdpi.com/2075-4418/14/24/2839 |
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