An artificial intelligence approach for investigating multifactorial pain-related features of endometriosis

Amber C. Kiser, Karen C Schliep, Edgar J. Hernández, Edgar Javier Hernandez, C Matthew Peterson, Mark Yandell, Karen Eilbeck
PloS one · 2024 · vol. 19(2) , pp. e0297998 · doi:10.1371/journal.pone.0297998 · PMID:38381710 · W4391994308
article OA: gold CC0 ⤵ 6 in-corpus citations
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⚙ AI-generated summary by claude@2026-06, 2026-06-10 ⓘ

An AI-based Bayesian network identified specific pain locations and types, such as chronic pelvic pain and dyspareunia, that significantly increase the relative risk of endometriosis.

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⚙ AI-generated deep summary by claude@2026-06, 2026-06-10 ⓘ

This study applied an AI approach using neighbor-joining clustering and a Bayesian network to analyze pain-related features, subfertility, and gynecologic diagnoses in 473 women undergoing laparoscopy or laparotomy for various surgical indications, with endometriosis determined by surgically visualized disease and severity staged by rASRM criteria. Pain reporting across 155 anatomical sites was clustered into 15 pain locations (15 clusters) and, after pruning, the final Bayesian network contained 18 nodes; querying the network showed that the presence of any pain-related feature increased the relative risk of endometriosis (p<0.001), with the combination of chronic pelvic pain, subfertility, and dyspareunia yielding the greatest relative risk increase. The authors report improved performance and sensitivity for Bayesian network analysis versus traditional statistical techniques, with the major caveat that the dataset was restricted to women undergoing surgery for other indications and had no missing data but was drawn from a specific operative cohort. This paper is centrally about endometriosis — it uses a Bayesian network to identify pain locations and pain-type constellations associated with the endometriosis diagnosis.

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Abstract

Endometriosis is a debilitating, chronic disease that is estimated to affect 11% of reproductive-age women. Diagnosis of endometriosis is difficult with diagnostic delays of up to 12 years reported. These delays can negatively impact health and quality of life. Vague, nonspecific symptoms, like pain, with multiple differential diagnoses contribute to the difficulty of diagnosis. By investigating previously imprecise symptoms of pain, we sought to clarify distinct pain symptoms indicative of endometriosis, using an artificial intelligence-based approach. We used data from 473 women undergoing laparoscopy or laparotomy for a variety of surgical indications. Multiple anatomical pain locations were clustered based on the associations across samples to increase the power in the probability calculations. A Bayesian network was developed using pain-related features, subfertility, and diagnoses. Univariable and multivariable analyses were performed by querying the network for the relative risk of a postoperative diagnosis, given the presence of different symptoms. Performance and sensitivity analyses demonstrated the advantages of Bayesian network analysis over traditional statistical techniques. Clustering grouped the 155 anatomical sites of pain into 15 pain locations. After pruning, the final Bayesian network included 18 nodes. The presence of any pain-related feature increased the relative risk of endometriosis (p-value < 0.001). The constellation of chronic pelvic pain, subfertility, and dyspareunia resulted in the greatest increase in the relative risk of endometriosis. The performance and sensitivity analyses demonstrated that the Bayesian network could identify and analyze more significant associations with endometriosis than traditional statistical techniques. Pelvic pain, frequently associated with endometriosis, is a common and vague symptom. Our Bayesian network for the study of pain-related features of endometriosis revealed specific pain locations and pain types that potentially forecast the diagnosis of endometriosis.

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Condition tags

mesh:D004715mesh:D017699endometriosischronic_pelvic_paindyspareunia

MeSH descriptors

Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis

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References (47)

Cited by (6)

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License: CC0 · commercial use OK