Table 1 Description of the studies.
From: Clinical use of artificial intelligence in endometriosis: a scoping review
Year | Author [ref.] | Study design | Intervention | Purpose | Objective | Sample size | AI accuracy for best model |
|---|---|---|---|---|---|---|---|
2022 | Bendifallah et al.50 | Retrospective | Logistic Regression, Random Forest, Decision Tree, eXtreme Gradient Boosting, Voting Classifier (soft/hard) | Prediction | Predict likelihood of endometriosis based on 16 essential clinical and symptom-based features related to patient history, demographics, endometriosis phenotype and treatment | 1126 endometriosis patients, 608 controls | SE = 93% SP = 92% |
2022 | Bendifallah et al.35 | Prospective | Logistic Regression, Random Forest eXtreme Gradient Boosting, AdaBoost | Diagnosis | Diagnosis of endometriosis using a blood-based mRNA diagnostic signature | 200 plasma samples (153 cases, 47 controls) | SE = 96.8% SP = 100% |
2021 | Maicus et al.61 | Prospective | Resnet (2 + 1)D | Diagnosis | Classification of the state of the Pouch of Douglas using the sliding sign test on ultrasound | 749 transvaginal ultrasound videos (414 training set, 139 validation set, 196 test set) | SE = 88.6% SP = 90% |
2021 | Guerriero et al.59 | Retrospective | K-Nearest Neighbor, Naïve Bayes, Neural Networks, SVM, Decision Tree, Random Forest, Logistic Regression | Prediction | Detection of endometriotic bowel involvement in rectosigmoid deep endometriosis | 333 patients | SE = 72% SP = 73% |
2021 | Li et al.52 | Retrospective | Deep Machine Learning Algorithm (NNET) | Diagnosis | Diagnosis of endometriosis based on genes | 213 patients | SE = 100% SP = 61.1% |
2020 | Matta et al.30 | Retrospective Case–Control | Logistic Regression, ANN, SVM, Adaptive Boosting, PLSDA | Research | Identify biomarkers of internal exposure in adipose tissue most associated with endometriosis | 99 women (44 controls, 55 cases) | SE = NR SP = NR |
2020 | Akter et al.53 | Retrospective | New Ensemble Machine Learning Classifier (GenomeForest) | Diagnosis | Classifying endometriosis versus control patients using RNAse and enrichment-based DNA-methylation datasets | 38 single-end RNA-sequence samples, 80 MBD-sequence DNA-methylation samples | Transcriptomics Data SE = 93.8% SP = 100% Methylomics Data SE = 92.9% SP = 88.6% |
2020 | Perrotta et al.54 | Prospective Observational Cross-Sectional Pilot | Random Forest-Based Machine Learning Classification Analysis | Diagnosis | Diagnosis of endometriosis using gut and/or vaginal microbiome profiles | 59 women (24 controls, 35 endometriosis patients) | SE = NR SP = NR |
2020 | Guo et al.58 | Retrospective Cohort | Logistic Regression | Prediction | Predict any-stage and stage 3/4 endometriosis before surgery in infertile women | 1016 patients (443 without endometriosis, 377 patients with stage 1/2 endometriosis, 196 patients with stage 3/4 endometriosis) | SE = NR SP = NR |
2021 | Vesale et al.45 | Retrospective | Logistic Regression | Prediction | Predict likelihood of voiding dysfunction after surgery for deep endometriosis | 789 patients | SE = NR SP = NR |
2019 | Benoit et al.46 | Retrospective | Logistic Regression | Prediction | Predict likelihood of a live birth after surgery followed by ART for patients with endometriosis-related infertility | 297 women | SE = NR SP = NR |
2019 | Lee et al.29 | Retrospective | Recommendation System | Research | Identify diseases associated with endometriosis | 1,730,562 controls, 11,273 cases | SE = NR SP = NR |
2019 | Braga et al.36 | Prospective Case–Control | PLSDA | Diagnosis | Develop an adjuvant tool for diagnosis of grades 3 and 4 endometriosis in infertile patients | 50 endometriosis serum samples, 50 control samples | SE = NR SP = NR |
2019 | Chattot et al.57 | Prospective Observational | Logistic Regression | Prediction | Predict rectosigmoid involvement in endometriosis using preoperative score | 119 women undergoing surgery for endometriosis | SE = NR SP = NR |
2019 | Knific et al.31 | Retrospective | Decision Tree, Linear Model, K-Nearest Neighbor, Random Forest | Diagnosis | Diagnosis of endometriosis based on plasma levels of proteins and patients’ clinical data | 210 patients | SE = 40% SP = 65% |
2019 | Parlatan et al.37 | Retrospective | K-Nearest Neighbor, SVM, PCA | Diagnosis | Diagnosis of endometriosis using non-invasive Raman spectroscopy-based classification model | 94 serum samples (49 endometriosis, 45 controls) | SE = 89.7% SP = 80.5% |
2019 | Akter et al.55 | Retrospective | Decision Tree, PLSDA, SVM, Random Forest | Diagnosis | Classify endometriosis versus control biopsy samples using transcriptomics or methylomics data | 38 samples in transcriptomics dataset, 77 samples in methylomics dataset | Transcriptomics Data SE = 81.3% SP = 95.5% Methylomics Data SE = 76.2% SP = 80% |
2018 | Bouaziz et al.28 | Retrospective | NLP | Research | Using NLP to extract data by text mining of the endometriosis-related genes in the PubMed database | 724 genes retrieved | SE = NR SP = NR |
2017 | Dominguez et al.33 | Prospective Case–Control | SVM | Diagnosis | Diagnosis of endometriosis using lipidomic profiling of endometrial fluid in patients with ovarian endometriosis | 12 endometriosis, 23 controls | SE = 58.3% SP = 100% |
2016 | Ghazi et al.38 | Prospective Cohort | PLSDA, Multi-Layer Feed Forward ANN, QDA | Prediction | Determine classifier metabolites for early prediction risk of disease | 31 infertile women with endometriosis, 15 controls | SE = NR SP = NR |
2015 | Reid et al.60 | Prospective Observational | Logistic Regression | Prediction | Use mathematical ultrasound models to determine whether a combination of transvaginal sonography markers could improve prediction of Pouch of Douglas obliteration | 189 women with suspected endometriosis | Model 1 SE = 88% SP = 97% Model 2 SE = 88% SP = 99% |
2014 | Lafay Pillet et al.47 | Prospective | Logistic Regression | Diagnosis | Diagnose DE before surgery for patients operated on for endometriomas | 164 patients with DIE, 162 with no DIE | SE = 51% SP = 94% |
2014 | Tamaresis et al.56 | Retrospective | Margin Tree Classification | Diagnosis | Detect and stage pelvic endometriosis using genomic data from endometrium | 148 endometrial samples | SE = NR SP = NR |
2014 | Wang et al.39 | Prospective Case–Control | Genetic Algorithm, Decision Tree Algorithm, Quick Classifier Algorithm | Diagnosis | Diagnosis of endometriosis and stage using peptide profiling | 122 patients | SE = 90.9% SP = 92.9% |
2013 | Wang et al.51 | Retrospective | Decision Tree | Prediction | Predict medical care decision rules for patients with recurrent pelvic cyst after surgical interventions | 178 case records | SE = NR SP = NR |
2012 | Ballester et al.48 | Prospective Longitudinal Study | Logistic Regression | Prediction | Prediction of clinical pregnancy rate in patients with endometriosis | 142 infertile patients with DIE | SE = 66.7% SP = 95.7% |
2012 | Fassbender et al.40 | Retrospective | LSSVM | Diagnosis | Diagnosis of endometriosis undetectable by ultrasonography | 254 plasma samples (89 controls, 165 endometriosis patients) | SE = 88% SP = 84% |
2012 | Fassbender et al.41 | Retrospective | LSSVM | Diagnosis | Diagnosis of endometriosis through mRNA expression profiles in luteal phase endometrium biopsies | 49 endometrial biopsies | SE = 91% SP = 80% |
2012 | Vodolazkaia et al.34 | Retrospective Cohort | Logistic Regression, LSSVM | Diagnosis | Diagnosis of endometriosis in symptomatic patients without U/S evidence of endometriosis | 121 controls, 232 endometriosis patients | SE = 81% SP = 81% |
2012 | Dutta et al.42 | Prospective | PLSDA | Prediction | Identification of predictive biomarkers in serum for early diagnosis of endometriosis in a minimally invasive manner | 22 endometriosis, 23 controls | SE = 81.8% SP = 91.3% |
2012 | Nnoaham et al.27 | Prospective Observational | Logistic Regression | Prediction | Predict any-stage endometriosis and stage 3 and 4 disease with a symptom-based model | 1396 symptomatic women | SE = 82.6% SP = 75.8% |
2010 | Wang et al.26 | Retrospective | ANN | Prediction | Screening for biomarkers of eutopic endometrium in endometriosis patients | 26 patients | SE = 91.7% SP = 90.9% |
2009 | Wolfler et al.43 | Prospective Exploratory Cohort | Genetic Algorithm | Prediction | Predict endometriosis before laparoscopy using patterns of serum proteins in symptomatic patients | 91 symptomatic patients | SE = 81.3% SP = 60.3% |
2009 | Stegmann et al.62 | Prospective Cohort | Logistic Regression | Prediction | Prediction of lesions that have high probability of containing histologically-confirmed endometriosis | 114 women with complete data on 487 lesions | SE = 88.4% SP = 24.6% |
2008 | Wang et al.44 | Retrospective | ANN | Diagnosis | Diagnostic model to correctly detect endometriosis and no endometriosis in serum samples using potential biomarkers of endometriosis | 66 serum samples | SE = 91.7% SP = 90% |
2005 | Chapron et al.49 | Prospective | Logistic Regression | Prediction | Predict presence of posterior deep endometriosis among women with chronic pelvic pain symptoms | 134 women scheduled for laparoscopy for chronic pelvic pain symptoms | SE = 68.6% SP = 77.1% |
