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

<i>Background/Objectives:</i> To develop a deep learning model for esophageal motility disorder diagnosis using high-resolution manometry images with the aid of Gemini. <i>Methods:</i> Gemini assisted in developing this model by aiding in code writing, preprocessing, model optimization, and troubleshooting. <i>Results:</i> 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. <i>Conclusions:</i> 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.

Authors

Institutions

Publication Details

Journal
Medicina
Published
2024-09-13
DOI
https://doi.org/10.3390/medicina60091493
Citations
12
Primary Topic
Esophageal Cancer Research and Treatment
Type
article
Field-Weighted Citation Impact
6.87
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Vlad Dumitru Brata, Andrei Pop, Daniel‐Corneliu Leucuta, Teodora Surdea‐Blaga et al.
12 citations
Medicina
Esophageal Cancer Research and Treatment
6.87
article

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

Vlad Dumitru Brata, Andrei Pop, Daniel‐Corneliu Leucuta, Teodora Surdea‐Blaga, Zoltan Czako, Imre Zsigmond, Dan L. Dumitraşcu, Abdulrahman Ismaiel, Liliana David, Edoardo Vincenzo Savarino, Giuseppe Chiarioni, Stefan‐Lucian Popa, Daria Claudia Turtoi
article en
12 citations

Abstract

<i>Background/Objectives:</i> To develop a deep learning model for esophageal motility disorder diagnosis using high-resolution manometry images with the aid of Gemini. <i>Methods:</i> Gemini assisted in developing this model by aiding in code writing, preprocessing, model optimization, and troubleshooting. <i>Results:</i> 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. <i>Conclusions:</i> 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.

MedicinaVol. 60(9)
University of North Carolina at Chapel Hill (US), University of Verona (IT), University of Padua (IT), Technical University of Cluj-Napoca (RO), Iuliu Hațieganu University of Medicine and Pharmacy (RO), Babeș-Bolyai University (RO)
Quality Education
Openalex Percentile: Top 2%
Esophageal Cancer Research and Treatment
6.87
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.