{"title":"增强临床实践:Endoscore应用程序用于自动手术数据采集和子宫内膜异位症评分。","authors":"Arrigo Fruscalzo, Georgia Theodorou, Ambrogio Pietro Londero, Benedetta Guani, Jean-Marc Ayoubi, Anis Feki, Carolin Marti","doi":"10.52054/FVVO.2025.36","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>There is a growing unmet need to digitalise the management of clinical data in medicine. Web-based scoring applications for endometriosis align with this trend.</p><p><strong>Objectives: </strong>This study aimed to evaluate a web-based application that automatically calculates endometriosis staging scores [revised American Society for Reproductive Medicine classification (r-ASRM), the revised Enzian classification (#Enzian), Endometriosis Fertility Index (EFI)] and compare it to manual scoring in a proof-of-concept study.</p><p><strong>Methods: </strong>20 endometriosis cases operated on in 2022 were retrospectively selected. Six experienced gynaecologists were randomly allocated to either the conventional paper-based method or the digital application for staging of disease.</p><p><strong>Main outcome measures: </strong>Completion time, score consistency among examiners and methods, and user satisfaction were recorded using a Likert scale and a subjective mental effort questionnaire (SMEQ).</p><p><strong>Results: </strong>In comparison to the paper-based method, the web-based tool reduced scoring time by 25.1 seconds (128.0 vs. 153.1, <i>P</i><0.05), was perceived as easier to use (higher Likert scale scores), and was associated with low-to-moderate mental effort on the SMEQ. The agreement between electronic and paper forms was rated as very good to excellent for r-ASRM [intraclass correlation coefficient (ICC): 0.93] and #Enzian (ICC: 0.84), while it was moderate for EFI (ICC: 0.67). Interrater agreement utilising the electronic form demonstrated high levels, yielding very good to excellent results for r-ASRM (ICC: 0.93) and EFI (ICC: 0.82) while showing moderate agreement for #Enzian (ICC: 0.63).</p><p><strong>Conclusions: </strong>The application facilitates sequential data entry for users and automatically calculates r-ASRM, #Enzian, and EFI scores. It decreases scoring duration, strongly aligns with the paper-based method, and enhances user satisfaction.</p><p><strong>What is new?: </strong>This tool can potentially improve clinical efficiency, accuracy, and consistency in the staging of endometriosis.</p>","PeriodicalId":46400,"journal":{"name":"Facts Views and Vision in ObGyn","volume":"17 3","pages":"253-262"},"PeriodicalIF":1.4000,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12489274/pdf/","citationCount":"0","resultStr":"{\"title\":\"Enhancing clinical practice: the Endoscore app for automated surgical data capture and endometriosis scoring.\",\"authors\":\"Arrigo Fruscalzo, Georgia Theodorou, Ambrogio Pietro Londero, Benedetta Guani, Jean-Marc Ayoubi, Anis Feki, Carolin Marti\",\"doi\":\"10.52054/FVVO.2025.36\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>There is a growing unmet need to digitalise the management of clinical data in medicine. Web-based scoring applications for endometriosis align with this trend.</p><p><strong>Objectives: </strong>This study aimed to evaluate a web-based application that automatically calculates endometriosis staging scores [revised American Society for Reproductive Medicine classification (r-ASRM), the revised Enzian classification (#Enzian), Endometriosis Fertility Index (EFI)] and compare it to manual scoring in a proof-of-concept study.</p><p><strong>Methods: </strong>20 endometriosis cases operated on in 2022 were retrospectively selected. Six experienced gynaecologists were randomly allocated to either the conventional paper-based method or the digital application for staging of disease.</p><p><strong>Main outcome measures: </strong>Completion time, score consistency among examiners and methods, and user satisfaction were recorded using a Likert scale and a subjective mental effort questionnaire (SMEQ).</p><p><strong>Results: </strong>In comparison to the paper-based method, the web-based tool reduced scoring time by 25.1 seconds (128.0 vs. 153.1, <i>P</i><0.05), was perceived as easier to use (higher Likert scale scores), and was associated with low-to-moderate mental effort on the SMEQ. The agreement between electronic and paper forms was rated as very good to excellent for r-ASRM [intraclass correlation coefficient (ICC): 0.93] and #Enzian (ICC: 0.84), while it was moderate for EFI (ICC: 0.67). Interrater agreement utilising the electronic form demonstrated high levels, yielding very good to excellent results for r-ASRM (ICC: 0.93) and EFI (ICC: 0.82) while showing moderate agreement for #Enzian (ICC: 0.63).</p><p><strong>Conclusions: </strong>The application facilitates sequential data entry for users and automatically calculates r-ASRM, #Enzian, and EFI scores. It decreases scoring duration, strongly aligns with the paper-based method, and enhances user satisfaction.</p><p><strong>What is new?: </strong>This tool can potentially improve clinical efficiency, accuracy, and consistency in the staging of endometriosis.</p>\",\"PeriodicalId\":46400,\"journal\":{\"name\":\"Facts Views and Vision in ObGyn\",\"volume\":\"17 3\",\"pages\":\"253-262\"},\"PeriodicalIF\":1.4000,\"publicationDate\":\"2025-09-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12489274/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Facts Views and Vision in ObGyn\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.52054/FVVO.2025.36\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"OBSTETRICS & GYNECOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Facts Views and Vision in ObGyn","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.52054/FVVO.2025.36","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"OBSTETRICS & GYNECOLOGY","Score":null,"Total":0}
Enhancing clinical practice: the Endoscore app for automated surgical data capture and endometriosis scoring.
Background: There is a growing unmet need to digitalise the management of clinical data in medicine. Web-based scoring applications for endometriosis align with this trend.
Objectives: This study aimed to evaluate a web-based application that automatically calculates endometriosis staging scores [revised American Society for Reproductive Medicine classification (r-ASRM), the revised Enzian classification (#Enzian), Endometriosis Fertility Index (EFI)] and compare it to manual scoring in a proof-of-concept study.
Methods: 20 endometriosis cases operated on in 2022 were retrospectively selected. Six experienced gynaecologists were randomly allocated to either the conventional paper-based method or the digital application for staging of disease.
Main outcome measures: Completion time, score consistency among examiners and methods, and user satisfaction were recorded using a Likert scale and a subjective mental effort questionnaire (SMEQ).
Results: In comparison to the paper-based method, the web-based tool reduced scoring time by 25.1 seconds (128.0 vs. 153.1, P<0.05), was perceived as easier to use (higher Likert scale scores), and was associated with low-to-moderate mental effort on the SMEQ. The agreement between electronic and paper forms was rated as very good to excellent for r-ASRM [intraclass correlation coefficient (ICC): 0.93] and #Enzian (ICC: 0.84), while it was moderate for EFI (ICC: 0.67). Interrater agreement utilising the electronic form demonstrated high levels, yielding very good to excellent results for r-ASRM (ICC: 0.93) and EFI (ICC: 0.82) while showing moderate agreement for #Enzian (ICC: 0.63).
Conclusions: The application facilitates sequential data entry for users and automatically calculates r-ASRM, #Enzian, and EFI scores. It decreases scoring duration, strongly aligns with the paper-based method, and enhances user satisfaction.
What is new?: This tool can potentially improve clinical efficiency, accuracy, and consistency in the staging of endometriosis.