Enhancing clinical practice: the Endoscore app for automated surgical data capture and endometriosis scoring

Arrigo Fruscalzo; Georgia Theodorou; Ambrogio Pietro Londero; Benedetta Guani; Jean-Marc Ayoubi; Anis Feki; Carolin Marti

doi:10.52054/FVVO.2025.36

Enhancing clinical practice: the Endoscore app for automated surgical data capture and endometriosis scoring

Facts Views Vis Obgyn. 2025 Sep 30;17(3):253-262. doi: 10.52054/FVVO.2025.36.

Authors

Arrigo Fruscalzo¹, Georgia Theodorou¹, Ambrogio Pietro Londero^{2

3}, Benedetta Guani¹, Jean-Marc Ayoubi⁴, Anis Feki¹, Carolin Marti¹

Affiliations

¹ Department of Gynaecology and Obstetrics, University Hospital of Fribourg, Fribourg, Switzerland.
² Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Infant Health, University of Genoa, Genoa, Italy.
³ Obstetrics and Gynaecology Unit, IRCCS Istituto Giannina Gaslini, Genova, Italy.
⁴ Department of Obstetrics and Gynaecology and Reproductive Medicine, Hopital Foch-Faculté de Médecine Paris, Suresnes, France.

Abstract

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.

Keywords: #Enzian; EFI; Endometriosis; application; classification; documentation; rASRM.