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Quantization of extraoral free flap monitoring for venous congestion with deep learning integrated iOS applications on smartphones: a diagnostic study
Free flap monitoring is essential for postmicrosurgical management and outcomes but traditionally relies on human observers; the process is subjective and qualitative and imposes a heavy burden on staffing. To scientifically monitor and quantify the condition of free flaps in a clinical scenario, we...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Lippincott Williams & Wilkins
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10389505/ https://www.ncbi.nlm.nih.gov/pubmed/37055021 http://dx.doi.org/10.1097/JS9.0000000000000391 |
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author | Hsu, Shao-Yun Chen, Li-Wei Huang, Ren-Wen Tsai, Tzong Yueh Hung, Shao-Yu Cheong, David Chon-Fok Lu, Johnny Chuieng-Yi Chang, Tommy Nai-Jen Huang, Jung-Ju Tsao, Chung-Kan Lin, Chih-Hung Chuang, David Chwei-Chin Wei, Fu-Chan Kao, Huang-Kai |
author_facet | Hsu, Shao-Yun Chen, Li-Wei Huang, Ren-Wen Tsai, Tzong Yueh Hung, Shao-Yu Cheong, David Chon-Fok Lu, Johnny Chuieng-Yi Chang, Tommy Nai-Jen Huang, Jung-Ju Tsao, Chung-Kan Lin, Chih-Hung Chuang, David Chwei-Chin Wei, Fu-Chan Kao, Huang-Kai |
author_sort | Hsu, Shao-Yun |
collection | PubMed |
description | Free flap monitoring is essential for postmicrosurgical management and outcomes but traditionally relies on human observers; the process is subjective and qualitative and imposes a heavy burden on staffing. To scientifically monitor and quantify the condition of free flaps in a clinical scenario, we developed and validated a successful clinical transitional deep learning (DL) model integrated application. MATERIAL AND METHODS: Patients from a single microsurgical intensive care unit between 1 April 2021 and 31 March 2022, were retrospectively analyzed for DL model development, validation, clinical transition, and quantification of free flap monitoring. An iOS application that predicted the probability of flap congestion based on computer vision was developed. The application calculated probability distribution that indicates the flap congestion risks. Accuracy, discrimination, and calibration tests were assessed for model performance evaluations. RESULTS: From a total of 1761 photographs of 642 patients, 122 patients were included during the clinical application period. Development (photographs =328), external validation (photographs =512), and clinical application (photographs =921) cohorts were assigned to corresponding time periods. The performance measurements of the DL model indicate a 92.2% training and a 92.3% validation accuracy. The discrimination (area under the receiver operating characteristic curve) was 0.99 (95% CI: 0.98–1.0) during internal validation and 0.98 (95% CI: 0.97–0.99) under external validation. Among clinical application periods, the application demonstrates 95.3% accuracy, 95.2% sensitivity, and 95.3% specificity. The probabilities of flap congestion were significantly higher in the congested group than in the normal group (78.3 (17.1)% versus 13.2 (18.1)%; 0.8%; 95% CI, P<0.001). CONCLUSION: The DL integrated smartphone application can accurately reflect and quantify flap condition; it is a convenient, accurate, and economical device that can improve patient safety and management and assist in monitoring flap physiology. |
format | Online Article Text |
id | pubmed-10389505 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Lippincott Williams & Wilkins |
record_format | MEDLINE/PubMed |
spelling | pubmed-103895052023-08-01 Quantization of extraoral free flap monitoring for venous congestion with deep learning integrated iOS applications on smartphones: a diagnostic study Hsu, Shao-Yun Chen, Li-Wei Huang, Ren-Wen Tsai, Tzong Yueh Hung, Shao-Yu Cheong, David Chon-Fok Lu, Johnny Chuieng-Yi Chang, Tommy Nai-Jen Huang, Jung-Ju Tsao, Chung-Kan Lin, Chih-Hung Chuang, David Chwei-Chin Wei, Fu-Chan Kao, Huang-Kai Int J Surg Original Research Free flap monitoring is essential for postmicrosurgical management and outcomes but traditionally relies on human observers; the process is subjective and qualitative and imposes a heavy burden on staffing. To scientifically monitor and quantify the condition of free flaps in a clinical scenario, we developed and validated a successful clinical transitional deep learning (DL) model integrated application. MATERIAL AND METHODS: Patients from a single microsurgical intensive care unit between 1 April 2021 and 31 March 2022, were retrospectively analyzed for DL model development, validation, clinical transition, and quantification of free flap monitoring. An iOS application that predicted the probability of flap congestion based on computer vision was developed. The application calculated probability distribution that indicates the flap congestion risks. Accuracy, discrimination, and calibration tests were assessed for model performance evaluations. RESULTS: From a total of 1761 photographs of 642 patients, 122 patients were included during the clinical application period. Development (photographs =328), external validation (photographs =512), and clinical application (photographs =921) cohorts were assigned to corresponding time periods. The performance measurements of the DL model indicate a 92.2% training and a 92.3% validation accuracy. The discrimination (area under the receiver operating characteristic curve) was 0.99 (95% CI: 0.98–1.0) during internal validation and 0.98 (95% CI: 0.97–0.99) under external validation. Among clinical application periods, the application demonstrates 95.3% accuracy, 95.2% sensitivity, and 95.3% specificity. The probabilities of flap congestion were significantly higher in the congested group than in the normal group (78.3 (17.1)% versus 13.2 (18.1)%; 0.8%; 95% CI, P<0.001). CONCLUSION: The DL integrated smartphone application can accurately reflect and quantify flap condition; it is a convenient, accurate, and economical device that can improve patient safety and management and assist in monitoring flap physiology. Lippincott Williams & Wilkins 2023-04-14 /pmc/articles/PMC10389505/ /pubmed/37055021 http://dx.doi.org/10.1097/JS9.0000000000000391 Text en Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) |
spellingShingle | Original Research Hsu, Shao-Yun Chen, Li-Wei Huang, Ren-Wen Tsai, Tzong Yueh Hung, Shao-Yu Cheong, David Chon-Fok Lu, Johnny Chuieng-Yi Chang, Tommy Nai-Jen Huang, Jung-Ju Tsao, Chung-Kan Lin, Chih-Hung Chuang, David Chwei-Chin Wei, Fu-Chan Kao, Huang-Kai Quantization of extraoral free flap monitoring for venous congestion with deep learning integrated iOS applications on smartphones: a diagnostic study |
title | Quantization of extraoral free flap monitoring for venous congestion with deep learning integrated iOS applications on smartphones: a diagnostic study |
title_full | Quantization of extraoral free flap monitoring for venous congestion with deep learning integrated iOS applications on smartphones: a diagnostic study |
title_fullStr | Quantization of extraoral free flap monitoring for venous congestion with deep learning integrated iOS applications on smartphones: a diagnostic study |
title_full_unstemmed | Quantization of extraoral free flap monitoring for venous congestion with deep learning integrated iOS applications on smartphones: a diagnostic study |
title_short | Quantization of extraoral free flap monitoring for venous congestion with deep learning integrated iOS applications on smartphones: a diagnostic study |
title_sort | quantization of extraoral free flap monitoring for venous congestion with deep learning integrated ios applications on smartphones: a diagnostic study |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10389505/ https://www.ncbi.nlm.nih.gov/pubmed/37055021 http://dx.doi.org/10.1097/JS9.0000000000000391 |
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