Deep Learning-Based Identification of Common Complication Features of Surgical Incisions
ObjectiveIn recent years, due to the development of accelerated recovery after surgery and day surgery in the field of surgery, the average length-of-stay of patients has been shortened and patients stay at home for post-surgical recovery and healing of the surgical incisions. In order to identify,...
Gespeichert in:
| Veröffentlicht in: | Sichuan da xue xue bao. Journal of Sichuan University. Yi xue ban 2023-09, Vol.54 (5), p.923-929 |
|---|---|
| Hauptverfasser: | , , , , , , , , , , |
| Format: | Artikel |
| Sprache: | chi |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 929 |
|---|---|
| container_issue | 5 |
| container_start_page | 923 |
| container_title | Sichuan da xue xue bao. Journal of Sichuan University. Yi xue ban |
| container_volume | 54 |
| creator | Zhao, Chunlin Hu, Shiqi He, Tingting Yuan, Linyan Yang, Xue Wang, Jing Chen, Xiao Liang, Zhimin Guo, Yuchen Li, Ping Li, Lingli |
| description | ObjectiveIn recent years, due to the development of accelerated recovery after surgery and day surgery in the field of surgery, the average length-of-stay of patients has been shortened and patients stay at home for post-surgical recovery and healing of the surgical incisions. In order to identify, in a timely manner, the problems that may appear at the incision site and help patients prevent or reduce the anxiety they may experience after discharge, we used deep learning method in this study to classify the features of common complications of surgical incisions, hoping to realize patient-directed early identification of complications common to surgical incisions. MethodsA total of 1 224 postoperative photographs of patients' surgical incisions were taken and collected at a tertiary-care hospital between June 2021 and March 2022. The photographs were collated and categorized according to different features of complications of the surgical incisions. Then, the photographs were divided into training, validation |
| doi_str_mv | 10.12182/20230960303 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest</sourceid><recordid>TN_cdi_proquest_miscellaneous_2880821263</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2880821263</sourcerecordid><originalsourceid>FETCH-proquest_miscellaneous_28808212633</originalsourceid><addsrcrecordid>eNqVirEOgjAURTtoolE2P6CjC_raJlBXUSOJmw5spOLDNCkt8uD_xUQ_wOncnHMZWwnYCCm03EqQCnYJKFATNhdJKmORqmLGIiJ7BxAC1E6nc1YcEFt-QdN565_x3hA-eP5A39vaVqa3wfNQ8yw0zbhGtO6nT2j6oUP69OvQPUfveO4rS2OlJZvWxhFGXy7Y-nS8Zee47cJrQOrLxlKFzhmPYaBSag1aCpko9cf1DTHISjw</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2880821263</pqid></control><display><type>article</type><title>Deep Learning-Based Identification of Common Complication Features of Surgical Incisions</title><source>PubMed Central</source><source>Alma/SFX Local Collection</source><creator>Zhao, Chunlin ; Hu, Shiqi ; He, Tingting ; Yuan, Linyan ; Yang, Xue ; Wang, Jing ; Chen, Xiao ; Liang, Zhimin ; Guo, Yuchen ; Li, Ping ; Li, Lingli</creator><creatorcontrib>Zhao, Chunlin ; Hu, Shiqi ; He, Tingting ; Yuan, Linyan ; Yang, Xue ; Wang, Jing ; Chen, Xiao ; Liang, Zhimin ; Guo, Yuchen ; Li, Ping ; Li, Lingli</creatorcontrib><description>ObjectiveIn recent years, due to the development of accelerated recovery after surgery and day surgery in the field of surgery, the average length-of-stay of patients has been shortened and patients stay at home for post-surgical recovery and healing of the surgical incisions. In order to identify, in a timely manner, the problems that may appear at the incision site and help patients prevent or reduce the anxiety they may experience after discharge, we used deep learning method in this study to classify the features of common complications of surgical incisions, hoping to realize patient-directed early identification of complications common to surgical incisions. MethodsA total of 1 224 postoperative photographs of patients' surgical incisions were taken and collected at a tertiary-care hospital between June 2021 and March 2022. The photographs were collated and categorized according to different features of complications of the surgical incisions. Then, the photographs were divided into training, validation</description><identifier>ISSN: 1672-173X</identifier><identifier>DOI: 10.12182/20230960303</identifier><language>chi</language><ispartof>Sichuan da xue xue bao. Journal of Sichuan University. Yi xue ban, 2023-09, Vol.54 (5), p.923-929</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Zhao, Chunlin</creatorcontrib><creatorcontrib>Hu, Shiqi</creatorcontrib><creatorcontrib>He, Tingting</creatorcontrib><creatorcontrib>Yuan, Linyan</creatorcontrib><creatorcontrib>Yang, Xue</creatorcontrib><creatorcontrib>Wang, Jing</creatorcontrib><creatorcontrib>Chen, Xiao</creatorcontrib><creatorcontrib>Liang, Zhimin</creatorcontrib><creatorcontrib>Guo, Yuchen</creatorcontrib><creatorcontrib>Li, Ping</creatorcontrib><creatorcontrib>Li, Lingli</creatorcontrib><title>Deep Learning-Based Identification of Common Complication Features of Surgical Incisions</title><title>Sichuan da xue xue bao. Journal of Sichuan University. Yi xue ban</title><description>ObjectiveIn recent years, due to the development of accelerated recovery after surgery and day surgery in the field of surgery, the average length-of-stay of patients has been shortened and patients stay at home for post-surgical recovery and healing of the surgical incisions. In order to identify, in a timely manner, the problems that may appear at the incision site and help patients prevent or reduce the anxiety they may experience after discharge, we used deep learning method in this study to classify the features of common complications of surgical incisions, hoping to realize patient-directed early identification of complications common to surgical incisions. MethodsA total of 1 224 postoperative photographs of patients' surgical incisions were taken and collected at a tertiary-care hospital between June 2021 and March 2022. The photographs were collated and categorized according to different features of complications of the surgical incisions. Then, the photographs were divided into training, validation</description><issn>1672-173X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNqVirEOgjAURTtoolE2P6CjC_raJlBXUSOJmw5spOLDNCkt8uD_xUQ_wOncnHMZWwnYCCm03EqQCnYJKFATNhdJKmORqmLGIiJ7BxAC1E6nc1YcEFt-QdN565_x3hA-eP5A39vaVqa3wfNQ8yw0zbhGtO6nT2j6oUP69OvQPUfveO4rS2OlJZvWxhFGXy7Y-nS8Zee47cJrQOrLxlKFzhmPYaBSag1aCpko9cf1DTHISjw</recordid><startdate>20230901</startdate><enddate>20230901</enddate><creator>Zhao, Chunlin</creator><creator>Hu, Shiqi</creator><creator>He, Tingting</creator><creator>Yuan, Linyan</creator><creator>Yang, Xue</creator><creator>Wang, Jing</creator><creator>Chen, Xiao</creator><creator>Liang, Zhimin</creator><creator>Guo, Yuchen</creator><creator>Li, Ping</creator><creator>Li, Lingli</creator><scope>7X8</scope></search><sort><creationdate>20230901</creationdate><title>Deep Learning-Based Identification of Common Complication Features of Surgical Incisions</title><author>Zhao, Chunlin ; Hu, Shiqi ; He, Tingting ; Yuan, Linyan ; Yang, Xue ; Wang, Jing ; Chen, Xiao ; Liang, Zhimin ; Guo, Yuchen ; Li, Ping ; Li, Lingli</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-proquest_miscellaneous_28808212633</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>chi</language><creationdate>2023</creationdate><toplevel>online_resources</toplevel><creatorcontrib>Zhao, Chunlin</creatorcontrib><creatorcontrib>Hu, Shiqi</creatorcontrib><creatorcontrib>He, Tingting</creatorcontrib><creatorcontrib>Yuan, Linyan</creatorcontrib><creatorcontrib>Yang, Xue</creatorcontrib><creatorcontrib>Wang, Jing</creatorcontrib><creatorcontrib>Chen, Xiao</creatorcontrib><creatorcontrib>Liang, Zhimin</creatorcontrib><creatorcontrib>Guo, Yuchen</creatorcontrib><creatorcontrib>Li, Ping</creatorcontrib><creatorcontrib>Li, Lingli</creatorcontrib><collection>MEDLINE - Academic</collection><jtitle>Sichuan da xue xue bao. Journal of Sichuan University. Yi xue ban</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhao, Chunlin</au><au>Hu, Shiqi</au><au>He, Tingting</au><au>Yuan, Linyan</au><au>Yang, Xue</au><au>Wang, Jing</au><au>Chen, Xiao</au><au>Liang, Zhimin</au><au>Guo, Yuchen</au><au>Li, Ping</au><au>Li, Lingli</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Deep Learning-Based Identification of Common Complication Features of Surgical Incisions</atitle><jtitle>Sichuan da xue xue bao. Journal of Sichuan University. Yi xue ban</jtitle><date>2023-09-01</date><risdate>2023</risdate><volume>54</volume><issue>5</issue><spage>923</spage><epage>929</epage><pages>923-929</pages><issn>1672-173X</issn><abstract>ObjectiveIn recent years, due to the development of accelerated recovery after surgery and day surgery in the field of surgery, the average length-of-stay of patients has been shortened and patients stay at home for post-surgical recovery and healing of the surgical incisions. In order to identify, in a timely manner, the problems that may appear at the incision site and help patients prevent or reduce the anxiety they may experience after discharge, we used deep learning method in this study to classify the features of common complications of surgical incisions, hoping to realize patient-directed early identification of complications common to surgical incisions. MethodsA total of 1 224 postoperative photographs of patients' surgical incisions were taken and collected at a tertiary-care hospital between June 2021 and March 2022. The photographs were collated and categorized according to different features of complications of the surgical incisions. Then, the photographs were divided into training, validation</abstract><doi>10.12182/20230960303</doi></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1672-173X |
| ispartof | Sichuan da xue xue bao. Journal of Sichuan University. Yi xue ban, 2023-09, Vol.54 (5), p.923-929 |
| issn | 1672-173X |
| language | chi |
| recordid | cdi_proquest_miscellaneous_2880821263 |
| source | PubMed Central; Alma/SFX Local Collection |
| title | Deep Learning-Based Identification of Common Complication Features of Surgical Incisions |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-06T02%3A50%3A07IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Deep%20Learning-Based%20Identification%20of%20Common%20Complication%20Features%20of%20Surgical%20Incisions&rft.jtitle=Sichuan%20da%20xue%20xue%20bao.%20Journal%20of%20Sichuan%20University.%20Yi%20xue%20ban&rft.au=Zhao,%20Chunlin&rft.date=2023-09-01&rft.volume=54&rft.issue=5&rft.spage=923&rft.epage=929&rft.pages=923-929&rft.issn=1672-173X&rft_id=info:doi/10.12182/20230960303&rft_dat=%3Cproquest%3E2880821263%3C/proquest%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2880821263&rft_id=info:pmid/&rfr_iscdi=true |