Current status of artificial intelligence analysis for the treatment of pancreaticobiliary diseases using endoscopic ultrasonography and endoscopic retrograde cholangiopancreatography
Abstract Pancreatic and biliary diseases encompass a range of conditions requiring accurate diagnosis for appropriate treatment strategies. This diagnosis relies heavily on imaging techniques like endoscopic ultrasonography and endoscopic retrograde cholangiopancreatography. Artificial intelligence...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-04-01
|
| Series: | DEN Open |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/deo2.267 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849397692902408192 |
|---|---|
| author | Takamichi Kuwahara Kazuo Hara Nobumasa Mizuno Shin Haba Nozomi Okuno Toshitaka Fukui Minako Urata Yoshitaro Yamamoto |
| author_facet | Takamichi Kuwahara Kazuo Hara Nobumasa Mizuno Shin Haba Nozomi Okuno Toshitaka Fukui Minako Urata Yoshitaro Yamamoto |
| author_sort | Takamichi Kuwahara |
| collection | DOAJ |
| description | Abstract Pancreatic and biliary diseases encompass a range of conditions requiring accurate diagnosis for appropriate treatment strategies. This diagnosis relies heavily on imaging techniques like endoscopic ultrasonography and endoscopic retrograde cholangiopancreatography. Artificial intelligence (AI), including machine learning and deep learning, is becoming integral in medical imaging and diagnostics, such as the detection of colorectal polyps. AI shows great potential in diagnosing pancreatobiliary diseases. Unlike machine learning, which requires feature extraction and selection, deep learning can utilize images directly as input. Accurate evaluation of AI performance is a complex task due to varied terminologies, evaluation methods, and development stages. Essential aspects of AI evaluation involve defining the AI's purpose, choosing appropriate gold standards, deciding on the validation phase, and selecting reliable validation methods. AI, particularly deep learning, is increasingly employed in endoscopic ultrasonography and endoscopic retrograde cholangiopancreatography diagnostics, achieving high accuracy levels in detecting and classifying various pancreatobiliary diseases. The AI often performs better than doctors, even in tasks like differentiating benign from malignant pancreatic tumors, cysts, and subepithelial lesions, identifying gallbladder lesions, assessing endoscopic retrograde cholangiopancreatography difficulty, and evaluating the biliary strictures. The potential for AI in diagnosing pancreatobiliary diseases, especially where other modalities have limitations, is considerable. However, a crucial constraint is the need for extensive, high‐quality annotated data for AI training. Future advances in AI, such as large language models, promise further applications in the medical field. |
| format | Article |
| id | doaj-art-d340ebeb6a394b0e8bd41b4d8b96b336 |
| institution | Kabale University |
| issn | 2692-4609 |
| language | English |
| publishDate | 2024-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | DEN Open |
| spelling | doaj-art-d340ebeb6a394b0e8bd41b4d8b96b3362025-08-20T03:38:54ZengWileyDEN Open2692-46092024-04-0141n/an/a10.1002/deo2.267Current status of artificial intelligence analysis for the treatment of pancreaticobiliary diseases using endoscopic ultrasonography and endoscopic retrograde cholangiopancreatographyTakamichi Kuwahara0Kazuo Hara1Nobumasa Mizuno2Shin Haba3Nozomi Okuno4Toshitaka Fukui5Minako Urata6Yoshitaro Yamamoto7Department of Gastroenterology Aichi Cancer Center Hospital AichiJapanDepartment of Gastroenterology Aichi Cancer Center Hospital AichiJapanDepartment of Gastroenterology Aichi Cancer Center Hospital AichiJapanDepartment of Gastroenterology Aichi Cancer Center Hospital AichiJapanDepartment of Gastroenterology Aichi Cancer Center Hospital AichiJapanDepartment of Gastroenterology Aichi Cancer Center Hospital AichiJapanDepartment of Gastroenterology Aichi Cancer Center Hospital AichiJapanDepartment of Gastroenterology Aichi Cancer Center Hospital AichiJapanAbstract Pancreatic and biliary diseases encompass a range of conditions requiring accurate diagnosis for appropriate treatment strategies. This diagnosis relies heavily on imaging techniques like endoscopic ultrasonography and endoscopic retrograde cholangiopancreatography. Artificial intelligence (AI), including machine learning and deep learning, is becoming integral in medical imaging and diagnostics, such as the detection of colorectal polyps. AI shows great potential in diagnosing pancreatobiliary diseases. Unlike machine learning, which requires feature extraction and selection, deep learning can utilize images directly as input. Accurate evaluation of AI performance is a complex task due to varied terminologies, evaluation methods, and development stages. Essential aspects of AI evaluation involve defining the AI's purpose, choosing appropriate gold standards, deciding on the validation phase, and selecting reliable validation methods. AI, particularly deep learning, is increasingly employed in endoscopic ultrasonography and endoscopic retrograde cholangiopancreatography diagnostics, achieving high accuracy levels in detecting and classifying various pancreatobiliary diseases. The AI often performs better than doctors, even in tasks like differentiating benign from malignant pancreatic tumors, cysts, and subepithelial lesions, identifying gallbladder lesions, assessing endoscopic retrograde cholangiopancreatography difficulty, and evaluating the biliary strictures. The potential for AI in diagnosing pancreatobiliary diseases, especially where other modalities have limitations, is considerable. However, a crucial constraint is the need for extensive, high‐quality annotated data for AI training. Future advances in AI, such as large language models, promise further applications in the medical field.https://doi.org/10.1002/deo2.267artificial intelligencedeep learningEUSpancreasERCP |
| spellingShingle | Takamichi Kuwahara Kazuo Hara Nobumasa Mizuno Shin Haba Nozomi Okuno Toshitaka Fukui Minako Urata Yoshitaro Yamamoto Current status of artificial intelligence analysis for the treatment of pancreaticobiliary diseases using endoscopic ultrasonography and endoscopic retrograde cholangiopancreatography DEN Open artificial intelligence deep learning EUS pancreas ERCP |
| title | Current status of artificial intelligence analysis for the treatment of pancreaticobiliary diseases using endoscopic ultrasonography and endoscopic retrograde cholangiopancreatography |
| title_full | Current status of artificial intelligence analysis for the treatment of pancreaticobiliary diseases using endoscopic ultrasonography and endoscopic retrograde cholangiopancreatography |
| title_fullStr | Current status of artificial intelligence analysis for the treatment of pancreaticobiliary diseases using endoscopic ultrasonography and endoscopic retrograde cholangiopancreatography |
| title_full_unstemmed | Current status of artificial intelligence analysis for the treatment of pancreaticobiliary diseases using endoscopic ultrasonography and endoscopic retrograde cholangiopancreatography |
| title_short | Current status of artificial intelligence analysis for the treatment of pancreaticobiliary diseases using endoscopic ultrasonography and endoscopic retrograde cholangiopancreatography |
| title_sort | current status of artificial intelligence analysis for the treatment of pancreaticobiliary diseases using endoscopic ultrasonography and endoscopic retrograde cholangiopancreatography |
| topic | artificial intelligence deep learning EUS pancreas ERCP |
| url | https://doi.org/10.1002/deo2.267 |
| work_keys_str_mv | AT takamichikuwahara currentstatusofartificialintelligenceanalysisforthetreatmentofpancreaticobiliarydiseasesusingendoscopicultrasonographyandendoscopicretrogradecholangiopancreatography AT kazuohara currentstatusofartificialintelligenceanalysisforthetreatmentofpancreaticobiliarydiseasesusingendoscopicultrasonographyandendoscopicretrogradecholangiopancreatography AT nobumasamizuno currentstatusofartificialintelligenceanalysisforthetreatmentofpancreaticobiliarydiseasesusingendoscopicultrasonographyandendoscopicretrogradecholangiopancreatography AT shinhaba currentstatusofartificialintelligenceanalysisforthetreatmentofpancreaticobiliarydiseasesusingendoscopicultrasonographyandendoscopicretrogradecholangiopancreatography AT nozomiokuno currentstatusofartificialintelligenceanalysisforthetreatmentofpancreaticobiliarydiseasesusingendoscopicultrasonographyandendoscopicretrogradecholangiopancreatography AT toshitakafukui currentstatusofartificialintelligenceanalysisforthetreatmentofpancreaticobiliarydiseasesusingendoscopicultrasonographyandendoscopicretrogradecholangiopancreatography AT minakourata currentstatusofartificialintelligenceanalysisforthetreatmentofpancreaticobiliarydiseasesusingendoscopicultrasonographyandendoscopicretrogradecholangiopancreatography AT yoshitaroyamamoto currentstatusofartificialintelligenceanalysisforthetreatmentofpancreaticobiliarydiseasesusingendoscopicultrasonographyandendoscopicretrogradecholangiopancreatography |