Automated Recognition of Abnormalities in Gastrointestinal Endoscopic Images – Evaluation of an AI Tool for Identifying Polyps and Other Irregularities
This study investigates the application of artificial intelligence (AI) for the automatic detection of pathological abnormalities in gastrointestinal endoscopic images. Specifically, it evaluates the performance of an AI tool in identifying and classifying lesions such as polyps and other irregular...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nicolaus Copernicus University in Toruń
2025-05-01
|
| Series: | Quality in Sport |
| Subjects: | |
| Online Access: | https://apcz.umk.pl/QS/article/view/60070 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850189689933266944 |
|---|---|
| author | Weronika Jarych Elżbieta Tokarczyk Patryk Iglewski Daria Ziemińska Karina Motolko Rafał Burczyk Konrad Duszyński Michał Kociński Jan Reinald Wendt |
| author_facet | Weronika Jarych Elżbieta Tokarczyk Patryk Iglewski Daria Ziemińska Karina Motolko Rafał Burczyk Konrad Duszyński Michał Kociński Jan Reinald Wendt |
| author_sort | Weronika Jarych |
| collection | DOAJ |
| description |
This study investigates the application of artificial intelligence (AI) for the automatic detection of pathological abnormalities in gastrointestinal endoscopic images. Specifically, it evaluates the performance of an AI tool in identifying and classifying lesions such as polyps and other irregularities, including inflammatory changes, within real-time endoscopic procedures. The primary objective is to assess the tool's diagnostic accuracy and its potential to improve lesion detection, thereby reducing the likelihood of overlooked abnormalities. Leveraging advanced machine learning techniques, particularly convolutional neural networks (CNNs), the AI system aims to enhance diagnostic precision and support clinicians in making prompt, evidence-based decisions. Key advantages of AI integration in endoscopy include improved sensitivity, minimized detection errors, and the potential to optimize clinical workflow efficiency. However, the study also addresses significant challenges, including the necessity for large, heterogeneous datasets for model validation, the need for standardized AI applications, and the ethical implications of AI-assisted clinical decision-making. Additionally, the potential benefits of combining AI with complementary imaging technologies, such as fluorescence imaging and spectroscopy, are explored to further enhance diagnostic capabilities. In conclusion, the study highlights the promising role of AI in gastrointestinal endoscopy while underscoring the importance of continued research, algorithmic refinement, and the establishment of regulatory frameworks to fully harness its clinical potential.
|
| format | Article |
| id | doaj-art-d60c04afc5cb40a4b22646bfdc2ad378 |
| institution | OA Journals |
| issn | 2450-3118 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nicolaus Copernicus University in Toruń |
| record_format | Article |
| series | Quality in Sport |
| spelling | doaj-art-d60c04afc5cb40a4b22646bfdc2ad3782025-08-20T02:15:33ZengNicolaus Copernicus University in ToruńQuality in Sport2450-31182025-05-014110.12775/QS.2025.41.60070Automated Recognition of Abnormalities in Gastrointestinal Endoscopic Images – Evaluation of an AI Tool for Identifying Polyps and Other IrregularitiesWeronika Jarych0https://orcid.org/0009-0009-1335-8072Elżbieta Tokarczyk1https://orcid.org/0009-0003-9683-7699Patryk Iglewski2https://orcid.org/0009-0004-6611-2168Daria Ziemińska3https://orcid.org/0009-0001-8240-2593Karina Motolko4https://orcid.org/0009-0001-9971-6687Rafał Burczyk5https://orcid.org/0000-0002-1650-1534Konrad Duszyński6https://orcid.org/0009-0006-5524-8857Michał Kociński7https://orcid.org/0009-0007-7651-7929Jan Reinald Wendt8https://orcid.org/0009-0009-6163-7041Szpital Morski im. PCK w Gdyni, Powstania Styczniowego 1, 81-519 Gdynia, PolandSzpital św. Wincentego a Paulo w Gdyni, ul. Wójta Radtkego 1, 81-348 GdyniaWojewódzki Szpital Zespolony im. L. Rydygiera w ToruniuSzpital Św. Wincentego a Paulo w Gdyni, ul. Wójta Radtkego 1, 81-348 GdyniaSpecjalistyczny Szpital Miejski im. M. Kopernika w Toruniu, ul. Stefana Batorego 17/19, 87-100 ToruńSzpital Uniwersytecki nr 2 im. dr J. Biziela, ul. Ujejskiego 75, 85-168 BydgoszczStudenckie Koło Naukowe Okulistyki, Gdański Uniwersytet Medyczny, ul. Marii Skłodowskiej-Curie 3a, 80-210 GdańskCollegium Medicum in Bydgoszcz: Bydgoszcz, Kujawsko-Pomorskie, PLSzpital Św. Wincentego a Paulo w Gdyni, ul. Wójta Radtkego 1, 81-348 Gdynia This study investigates the application of artificial intelligence (AI) for the automatic detection of pathological abnormalities in gastrointestinal endoscopic images. Specifically, it evaluates the performance of an AI tool in identifying and classifying lesions such as polyps and other irregularities, including inflammatory changes, within real-time endoscopic procedures. The primary objective is to assess the tool's diagnostic accuracy and its potential to improve lesion detection, thereby reducing the likelihood of overlooked abnormalities. Leveraging advanced machine learning techniques, particularly convolutional neural networks (CNNs), the AI system aims to enhance diagnostic precision and support clinicians in making prompt, evidence-based decisions. Key advantages of AI integration in endoscopy include improved sensitivity, minimized detection errors, and the potential to optimize clinical workflow efficiency. However, the study also addresses significant challenges, including the necessity for large, heterogeneous datasets for model validation, the need for standardized AI applications, and the ethical implications of AI-assisted clinical decision-making. Additionally, the potential benefits of combining AI with complementary imaging technologies, such as fluorescence imaging and spectroscopy, are explored to further enhance diagnostic capabilities. In conclusion, the study highlights the promising role of AI in gastrointestinal endoscopy while underscoring the importance of continued research, algorithmic refinement, and the establishment of regulatory frameworks to fully harness its clinical potential. https://apcz.umk.pl/QS/article/view/60070Artificial intelligenceendoscopygastrointestinal imagingpolyps detectioncomputer-aided detectionconvolutional neural networks |
| spellingShingle | Weronika Jarych Elżbieta Tokarczyk Patryk Iglewski Daria Ziemińska Karina Motolko Rafał Burczyk Konrad Duszyński Michał Kociński Jan Reinald Wendt Automated Recognition of Abnormalities in Gastrointestinal Endoscopic Images – Evaluation of an AI Tool for Identifying Polyps and Other Irregularities Quality in Sport Artificial intelligence endoscopy gastrointestinal imaging polyps detection computer-aided detection convolutional neural networks |
| title | Automated Recognition of Abnormalities in Gastrointestinal Endoscopic Images – Evaluation of an AI Tool for Identifying Polyps and Other Irregularities |
| title_full | Automated Recognition of Abnormalities in Gastrointestinal Endoscopic Images – Evaluation of an AI Tool for Identifying Polyps and Other Irregularities |
| title_fullStr | Automated Recognition of Abnormalities in Gastrointestinal Endoscopic Images – Evaluation of an AI Tool for Identifying Polyps and Other Irregularities |
| title_full_unstemmed | Automated Recognition of Abnormalities in Gastrointestinal Endoscopic Images – Evaluation of an AI Tool for Identifying Polyps and Other Irregularities |
| title_short | Automated Recognition of Abnormalities in Gastrointestinal Endoscopic Images – Evaluation of an AI Tool for Identifying Polyps and Other Irregularities |
| title_sort | automated recognition of abnormalities in gastrointestinal endoscopic images evaluation of an ai tool for identifying polyps and other irregularities |
| topic | Artificial intelligence endoscopy gastrointestinal imaging polyps detection computer-aided detection convolutional neural networks |
| url | https://apcz.umk.pl/QS/article/view/60070 |
| work_keys_str_mv | AT weronikajarych automatedrecognitionofabnormalitiesingastrointestinalendoscopicimagesevaluationofanaitoolforidentifyingpolypsandotherirregularities AT elzbietatokarczyk automatedrecognitionofabnormalitiesingastrointestinalendoscopicimagesevaluationofanaitoolforidentifyingpolypsandotherirregularities AT patrykiglewski automatedrecognitionofabnormalitiesingastrointestinalendoscopicimagesevaluationofanaitoolforidentifyingpolypsandotherirregularities AT dariazieminska automatedrecognitionofabnormalitiesingastrointestinalendoscopicimagesevaluationofanaitoolforidentifyingpolypsandotherirregularities AT karinamotolko automatedrecognitionofabnormalitiesingastrointestinalendoscopicimagesevaluationofanaitoolforidentifyingpolypsandotherirregularities AT rafałburczyk automatedrecognitionofabnormalitiesingastrointestinalendoscopicimagesevaluationofanaitoolforidentifyingpolypsandotherirregularities AT konradduszynski automatedrecognitionofabnormalitiesingastrointestinalendoscopicimagesevaluationofanaitoolforidentifyingpolypsandotherirregularities AT michałkocinski automatedrecognitionofabnormalitiesingastrointestinalendoscopicimagesevaluationofanaitoolforidentifyingpolypsandotherirregularities AT janreinaldwendt automatedrecognitionofabnormalitiesingastrointestinalendoscopicimagesevaluationofanaitoolforidentifyingpolypsandotherirregularities |