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Article

Gemini-Assisted Deep Learning Classification Model for Automated Diagnosis of High-Resolution Esophageal Manometry Images

by
Stefan Lucian Popa
1,
Teodora Surdea-Blaga
1,*,
Dan Lucian Dumitrascu
1,
Andrei Vasile Pop
1,
Abdulrahman Ismaiel
1,
Liliana David
1,
Vlad Dumitru Brata
2,
Daria Claudia Turtoi
2,
Giuseppe Chiarioni
3,4,
Edoardo Vincenzo Savarino
5,
Imre Zsigmond
6,
Zoltan Czako
7 and
Daniel Corneliu Leucuta
8
1
Second Medical Department, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400006 Cluj-Napoca, Romania
2
Faculty of Medicine, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania
3
Il Cerchio Med Global Healthcare, Verona Center, 37100 Verona, Italy
4
UNC Center for Functional GI and Motility Disorders, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
5
Gastroenterology Unit, Department of Surgery, Oncology and Gastroenterology, University of Padua, 35128 Padova, Italy
6
Faculty of Mathematics and Computer Science, Babes-Bolyai University, 400347 Cluj-Napoca, Romania
7
Computer Science Department, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania
8
Department of Medical Informatics and Biostatistics, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400349 Cluj-Napoca, Romania
*
Author to whom correspondence should be addressed.
Medicina 2024, 60(9), 1493; https://doi.org/10.3390/medicina60091493
Submission received: 19 July 2024 / Revised: 12 August 2024 / Accepted: 9 September 2024 / Published: 13 September 2024
(This article belongs to the Special Issue Gastroesophageal Reflux Disease and Esophageal Motility Disorders)
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Abstract

Background/Objectives: To develop a deep learning model for esophageal motility disorder diagnosis using high-resolution manometry images with the aid of Gemini. Methods: Gemini assisted in developing this model by aiding in code writing, preprocessing, model optimization, and troubleshooting. Results: The model demonstrated an overall precision of 0.89 on the testing set, with an accuracy of 0.88, a recall of 0.88, and an F1-score of 0.885. It presented better results for multiple categories, particularly in the panesophageal pressurization category, with precision = 0.99 and recall = 0.99, yielding a balanced F1-score of 0.99. Conclusions: This study demonstrates the potential of artificial intelligence, particularly Gemini, in aiding the creation of robust deep learning models for medical image analysis, solving not just simple binary classification problems but more complex, multi-class image classification tasks.
Keywords: Gemini; deep learning; esophageal motility disorder diagnosis; image classification; artificial intelligence; HREM Gemini; deep learning; esophageal motility disorder diagnosis; image classification; artificial intelligence; HREM

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MDPI and ACS Style

Popa, S.L.; Surdea-Blaga, T.; Dumitrascu, D.L.; Pop, A.V.; Ismaiel, A.; David, L.; Brata, V.D.; Turtoi, D.C.; Chiarioni, G.; Savarino, E.V.; et al. Gemini-Assisted Deep Learning Classification Model for Automated Diagnosis of High-Resolution Esophageal Manometry Images. Medicina 2024, 60, 1493. https://doi.org/10.3390/medicina60091493

AMA Style

Popa SL, Surdea-Blaga T, Dumitrascu DL, Pop AV, Ismaiel A, David L, Brata VD, Turtoi DC, Chiarioni G, Savarino EV, et al. Gemini-Assisted Deep Learning Classification Model for Automated Diagnosis of High-Resolution Esophageal Manometry Images. Medicina. 2024; 60(9):1493. https://doi.org/10.3390/medicina60091493

Chicago/Turabian Style

Popa, Stefan Lucian, Teodora Surdea-Blaga, Dan Lucian Dumitrascu, Andrei Vasile Pop, Abdulrahman Ismaiel, Liliana David, Vlad Dumitru Brata, Daria Claudia Turtoi, Giuseppe Chiarioni, Edoardo Vincenzo Savarino, and et al. 2024. "Gemini-Assisted Deep Learning Classification Model for Automated Diagnosis of High-Resolution Esophageal Manometry Images" Medicina 60, no. 9: 1493. https://doi.org/10.3390/medicina60091493

APA Style

Popa, S. L., Surdea-Blaga, T., Dumitrascu, D. L., Pop, A. V., Ismaiel, A., David, L., Brata, V. D., Turtoi, D. C., Chiarioni, G., Savarino, E. V., Zsigmond, I., Czako, Z., & Leucuta, D. C. (2024). Gemini-Assisted Deep Learning Classification Model for Automated Diagnosis of High-Resolution Esophageal Manometry Images. Medicina, 60(9), 1493. https://doi.org/10.3390/medicina60091493

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