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Improving Patient Safety in the X-ray Inspection Process with EfficientNet-Based Medical Assistance System
Patient safety is a paramount concern in the medical field, and advancements in deep learning and Artificial Intelligence (AI) have opened up new possibilities for improving healthcare practices. While AI has shown promise in assisting doctors with early symptom detection from medical images, there...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10379294/ https://www.ncbi.nlm.nih.gov/pubmed/37510509 http://dx.doi.org/10.3390/healthcare11142068 |
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author | Chen, Shyh-Wei Chen, Jyun-Kai Hsieh, Yu-Heng Chen, Wen-Hsien Liao, Ying-Hsiang Lin, You-Cheng Chen, Ming-Chih Tsai, Ching-Tsorng Chai, Jyh-Wen Yuan, Shyan-Ming |
author_facet | Chen, Shyh-Wei Chen, Jyun-Kai Hsieh, Yu-Heng Chen, Wen-Hsien Liao, Ying-Hsiang Lin, You-Cheng Chen, Ming-Chih Tsai, Ching-Tsorng Chai, Jyh-Wen Yuan, Shyan-Ming |
author_sort | Chen, Shyh-Wei |
collection | PubMed |
description | Patient safety is a paramount concern in the medical field, and advancements in deep learning and Artificial Intelligence (AI) have opened up new possibilities for improving healthcare practices. While AI has shown promise in assisting doctors with early symptom detection from medical images, there is a critical need to prioritize patient safety by enhancing existing processes. To enhance patient safety, this study focuses on improving the medical operation process during X-ray examinations. In this study, we utilize EfficientNet for classifying the 49 categories of pre-X-ray images. To enhance the accuracy even further, we introduce two novel Neural Network architectures. The classification results are then compared with the doctor’s order to ensure consistency and minimize discrepancies. To evaluate the effectiveness of the proposed models, a comprehensive dataset comprising 49 different categories and over 12,000 training and testing sheets was collected from Taichung Veterans General Hospital. The research demonstrates a significant improvement in accuracy, surpassing a 4% enhancement compared to previous studies. |
format | Online Article Text |
id | pubmed-10379294 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-103792942023-07-29 Improving Patient Safety in the X-ray Inspection Process with EfficientNet-Based Medical Assistance System Chen, Shyh-Wei Chen, Jyun-Kai Hsieh, Yu-Heng Chen, Wen-Hsien Liao, Ying-Hsiang Lin, You-Cheng Chen, Ming-Chih Tsai, Ching-Tsorng Chai, Jyh-Wen Yuan, Shyan-Ming Healthcare (Basel) Article Patient safety is a paramount concern in the medical field, and advancements in deep learning and Artificial Intelligence (AI) have opened up new possibilities for improving healthcare practices. While AI has shown promise in assisting doctors with early symptom detection from medical images, there is a critical need to prioritize patient safety by enhancing existing processes. To enhance patient safety, this study focuses on improving the medical operation process during X-ray examinations. In this study, we utilize EfficientNet for classifying the 49 categories of pre-X-ray images. To enhance the accuracy even further, we introduce two novel Neural Network architectures. The classification results are then compared with the doctor’s order to ensure consistency and minimize discrepancies. To evaluate the effectiveness of the proposed models, a comprehensive dataset comprising 49 different categories and over 12,000 training and testing sheets was collected from Taichung Veterans General Hospital. The research demonstrates a significant improvement in accuracy, surpassing a 4% enhancement compared to previous studies. MDPI 2023-07-19 /pmc/articles/PMC10379294/ /pubmed/37510509 http://dx.doi.org/10.3390/healthcare11142068 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Chen, Shyh-Wei Chen, Jyun-Kai Hsieh, Yu-Heng Chen, Wen-Hsien Liao, Ying-Hsiang Lin, You-Cheng Chen, Ming-Chih Tsai, Ching-Tsorng Chai, Jyh-Wen Yuan, Shyan-Ming Improving Patient Safety in the X-ray Inspection Process with EfficientNet-Based Medical Assistance System |
title | Improving Patient Safety in the X-ray Inspection Process with EfficientNet-Based Medical Assistance System |
title_full | Improving Patient Safety in the X-ray Inspection Process with EfficientNet-Based Medical Assistance System |
title_fullStr | Improving Patient Safety in the X-ray Inspection Process with EfficientNet-Based Medical Assistance System |
title_full_unstemmed | Improving Patient Safety in the X-ray Inspection Process with EfficientNet-Based Medical Assistance System |
title_short | Improving Patient Safety in the X-ray Inspection Process with EfficientNet-Based Medical Assistance System |
title_sort | improving patient safety in the x-ray inspection process with efficientnet-based medical assistance system |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10379294/ https://www.ncbi.nlm.nih.gov/pubmed/37510509 http://dx.doi.org/10.3390/healthcare11142068 |
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