Digital health-enabled life-course management of endometriosis: from early recognition and assisted diagnosis to symptom monitoring and self-management.
- 1Jiangxi University of Chinese Medicine, Nanchang, Jiangxi, China.
- 2Affiliated Hospital of Jiangxi University of Chinese Medicine, Nanchang, Jiangxi, China.
Abstract
Endometriosis is a chronic inflammatory disease that often requires longitudinal care across recognition, diagnostic assessment, symptom control, fertility planning, treatment transitions, and recurrence reassessment. The information required for these decisions is frequently fragmented across patient experience, clinical encounters, imaging, treatment history, and health-system records. This narrative Review synthesizes digital-health applications in endometriosis according to clinical function, data source, and translational maturity. Literature was identified through structured searches of major biomedical databases and targeted reference screening, with emphasis on clinically relevant validation, prospective evaluation, and implementation evidence. The most mature applications include structured electronic symptom capture, electronic patient-reported outcomes, selected app- or telehealth-based support, and digital pain interventions. AI/ML approaches for imaging, risk estimation, molecular classification, fertility outcomes, and recurrence prediction are technically active but are commonly limited by internal validation, uncertain calibration and action thresholds, and sparse prospective clinical-utility evidence. We propose a clinician-supervised life-course framework in which patient-expressed data, clinical and imaging information, treatment history, fertility goals, and follow-up data are integrated to support early recognition, assisted diagnosis, monitoring, shared decisions, and reassessment. The target state is clinical coherence rather than maximal data capture, supported by explicit institutional responsibility for review, escalation, feedback, privacy, equity, and human oversight. Progress toward routine use will depend on external validation, interoperable data architecture, low-burden measurement, prospective pathway evaluation, and accountable implementation.
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BibTeX
@article{deng2026digital,
title = {{Digital health-enabled life-course management of endometriosis: from early recognition and assisted diagnosis to symptom monitoring and self-management.}},
author = {QinQin Deng and ZiChen Shao and Min Yang},
journal = {Frontiers in medicine},
year = {2026},
month = oct,
doi = {10.3389/fmed.2026.1951037},
url = {https://europepmc.org/article/MED/42921513},
note = {CC BY},
}
RIS
TY - JOUR
TI - Digital health-enabled life-course management of endometriosis: from early recognition and assisted diagnosis to symptom monitoring and self-management.
AU - QinQin Deng
AU - ZiChen Shao
AU - Min Yang
PY - 2026
DA - 2026/10/06
JO - Frontiers in medicine
DO - 10.3389/fmed.2026.1951037
UR - https://europepmc.org/article/MED/42921513
AB - Endometriosis is a chronic inflammatory disease that often requires longitudinal care across recognition, diagnostic assessment, symptom control, fertility planning, treatment transitions, and recurrence reassessment. The information required for these decisions is frequently fragmented across patient experience, clinical encounters, imaging, treatment history, and health-system records. This narrative Review synthesizes digital-health applications in endometriosis according to clinical function, data source, and translational maturity. Literature was identified through structured searches of major biomedical databases and targeted reference screening, with emphasis on clinically relevant validation, prospective evaluation, and implementation evidence. The most mature applications include structured electronic symptom capture, electronic patient-reported outcomes, selected app- or telehealth-based support, and digital pain interventions. AI/ML approaches for imaging, risk estimation, molecular classification, fertility outcomes, and recurrence prediction are technically active but are commonly limited by internal validation, uncertain calibration and action thresholds, and sparse prospective clinical-utility evidence. We propose a clinician-supervised life-course framework in which patient-expressed data, clinical and imaging information, treatment history, fertility goals, and follow-up data are integrated to support early recognition, assisted diagnosis, monitoring, shared decisions, and reassessment. The target state is clinical coherence rather than maximal data capture, supported by explicit institutional responsibility for review, escalation, feedback, privacy, equity, and human oversight. Progress toward routine use will depend on external validation, interoperable data architecture, low-burden measurement, prospective pathway evaluation, and accountable implementation.
SN - 2296-858X
ER -