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. 2022 Aug 4;5:109. doi: 10.1038/s41746-022-00638-1

Table 6.

Diagnostic and predictive models built using mixed variables.

AI methods used Authors [ref.] Stage of endometriosis Type of endometriosis Sample size Inputs used Evaluation
Metric
Logistic Regression Guo et al.58 All stages of endometriosis and stage 3/4 endometriosis NR 1016 infertile patients for any-stage endometriosis nomogram: BMI, Cycle length, parity, palpable nodularity, endometrioma diagnosed on TVS, tubal pathology; for stage 3–4 endometriosis nomogram: pain, palpable nodularity, endometrioma diagnosed on TVS

SE = NR

SP = NR

Logistic Regression Chattot et al.57 Not specified NR 119 patients (47 endometriosis with rectosigmoid involvement, 72 endometriosis without rectosigmoid involvement) Palpation of a posterior nodule on digital examination, UBESS score of 3 on ultrasonography, rectosigmoid involvement in endometriosis infiltration on MRI, presence of blood in the stools during menstruation

SE = NR

SP = NR

Logistic Regression Nnoaham et al.27 Stage 3 and 4 endometriosis NR 1396 symptomatic women Ultrasound evidence, menstrual dyschezia, ethnicity, history of benign ovarian cysts

SE = 82.6%

SP = 75.8%

NR not reported, BMI body mass index, TVS transvaginal ultrasound, UBESS ultrasound-based endometriosis staging system, MRI magnetic resonance imaging, SE sensitivity, SP specificity.