Advertisement

Artificial intelligence (AI) in the detection of rectosigmoid deep endometriosis

      Abstract

      Objectives

      The aim of this study was to compare the accuracy of seven classical Machine Learning (ML) models trained with ultrasound (US) soft markers to raise suspicion of endometriotic bowel involvement.

      Materials and Methods

      Input data to the models was retrieved from a database of a previously published study on bowel endometriosis performed on 333 patients. The following models have been tested: k-nearest neighbors algorithm (k-NN), Naive Bayes, Neural Networks (NNET-neuralnet), Support Vector Machine (SVM), Decision Tree, Random Forest, and Logistic Regression. The data driven strategy has been to split randomly the complete dataset in two different datasets. The training dataset and the test dataset with a 67 % and 33 % of the original cases respectively. All models were trained on the training dataset and the predictions have been evaluated using the test dataset. The best model was chosen based on the accuracy demonstrated on the test dataset. The information used in all the models were: age; presence of US signs of uterine adenomyosis; presence of an endometrioma; adhesions of the ovary to the uterus; presence of “kissing ovaries”; absence of sliding sign. All models have been trained using CARET package in R with ten repeated 10-fold cross-validation. Accuracy, Sensitivity, Specificity, positive (PPV) and negative (NPV) predictive value were calculated using a 50 % threshold. Presence of intestinal involvement was defined in all cases in the test dataset with an estimated probability greater than 0.5.

      Results

      In our previous study from where the inputs were retrieved, 106 women had a final expert US diagnosis of rectosigmoid endometriosis. In term of diagnostic accuracy the best model was the Neural Net (Accuracy, 0.73; sensitivity, 0.72; specificity 0.73; PPV 0.52; and NPV 0.86) but without significant difference with the others.

      Conclusions

      The accuracy of ultrasound soft markers in raising suspicion of rectosigmoid endometriosis using Artificial Intelligence (AI) models showed similar results to the logistic model.

      Keywords

      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      References

        • Bellman R.
        An introduction to artificial intelligence: can computers think?.
        Boyd & Fraser Pub Co, San Francisco1978
        • Drukker L.
        • Noble J.A.
        • Papageorghiou A.T.
        Introduction to artificial intelligence in ultrasound imaging in obstetrics and gynecology.
        Ultrasound Obstet Gynecol. 2020; 56 (PMID: 32530098; PMCID: PMC7702141): 498-505https://doi.org/10.1002/uog.22122
        • Recht M.P.
        • Dewey M.
        • Dreyer K.
        • Langlotz C.
        • Niessen W.
        • Prainsack B.
        • et al.
        Integrating artificial intelligence into the clinical practice of radiology: challenges and recommendations.
        Eur Radiol. 2020; 30: 3576-3584
        • Martín Noguerol T.
        • Paulano-Godino F.
        • Martín-Valdivia M.T.
        • Menias C.O.
        • Luna A.
        Strengths, weaknesses, opportunities, and threats analysis of artificial intelligence and machine learning applications in radiology.
        J Am Coll Radiol. 2019; 16: 1239-1247
        • Singhal N.
        • Kudavelly S.
        • Ramaraju G.A.
        Deep learning based junctional zone quantification using 3D transvaginal ultrasound in assisted reproduction.
        Conf Proc IEEE Eng Med Biol Soc. 2020; 2020: 2133-2136
        • Timmerman D.
        • Verrelst H.
        • Bourne T.H.
        • De Moor B.
        • Collins W.P.
        • Vergote I.
        • et al.
        Artificial neural network models for the preoperative discrimination between malignant and benign adnexal masses.
        Ultrasound Obstet Gynecol. 1999; 13: 17-25
        • Acharya U.R.
        • Molinari F.
        • Sree S.V.
        • Swapna G.
        • Saba L.
        • Guerriero S.
        • et al.
        Ovarian tissue characterization in ultrasound: a review.
        Technol Cancer Res Treat. 2015; 14: 251-261
        • Acharya U.R.
        • Sree V.S.
        • Saba L.
        • Molinari F.
        • Guerriero S.
        • Suri J.S.
        Ovarian tumor characterization and classification: a class of GyneScanTM systems.
        Conf Proc IEEE Eng Med Biol Soc. 2012; 2012: 4446-4449
        • Acharya U.R.
        • Mookiah M.R.
        • Vinitha Sree S.
        • Yanti R.
        • Martis R.J.
        • Saba L.
        • et al.
        Evolutionary algorithm-based classifier parameter tuning for automatic ovarian cancer tissue characterization and classification.
        Ultraschall Med. 2014; 35: 237-245
        • Acharya U.R.
        • Sree S.V.
        • Saba L.
        • Molinari F.
        • Guerriero S.
        • Suri J.S.
        Ovarian tumor characterization and classification using ultrasound-a new online paradigm.
        J Digit Imaging. 2013; 26: 544-553
        • Acharya U.R.
        • Sree S.V.
        • Krishnan M.M.
        • Saba L.
        • Molinari F.
        • Guerriero S.
        • et al.
        Ovarian tumor characterization using 3D ultrasound.
        Technol Cancer Res Treat. 2012; 11: 543-552
        • Grigore M.
        • Popovici R.M.
        • Gafitanu D.
        • Himiniuc L.
        • Murarasu M.
        • Micu R.
        Logistic models and artificial intelligence in the sonographic assessment of adnexal masses - a systematic review of the literature.
        Med Ultrason. 2020; 22 (Epub 2020 Jun 29. PMID: 32905566): 469-475https://doi.org/10.11152/mu-2538
        • Hastie T.
        • Tibshirani R.
        • Friedman J.
        The elements of statistical learning.
        Springer Series in Statistics Springer New York Inc., New York, NY, USA2001
        • Guerriero S.
        • Ajossa S.
        • Pascual M.A.
        • Rodriguez I.
        • Piras A.
        • Perniciano M.
        • et al.
        Ultrasonographic soft markers for detection of rectosigmoid deep endometriosis.
        Ultrasound Obstet Gynecol. 2020; 55: 269-273
        • Max Kuhn
        Caret: classification and Regression Training. R package version 6.0-85.
        2020
        • R Core Team
        R: a language and environment for statistical computing.
        (URL) R Foundation for Statistical Computing, Vienna, Austria2019
        • Robin X.
        • Turck N.
        • Hainard A.
        • Tiberti N.
        • Lisacek F.
        • Sanchez J.C.
        • et al.
        pROC: an open-source package for R and S+ to analyze and compare ROC curves.
        BMC Bioinformatics. 2011; 12: 77
        • Emin E.I.
        • Emin E.I.
        • Papalois A.
        • Willmott F.
        • Clarke S.
        • Sideris M.
        Artificial intelligence in obstetrics and gynaecology: is this the way forward?.
        In Vivo. 2019; 33: 1547-1551
        • Iftikhar P.
        • Kuijpers M.V.
        • Khayyat A.
        • Iftikhar A.
        • DeGouvia De Sa M.
        Artificial intelligence: a new paradigm in obstetrics and gynecology research and clinical practice.
        Cureus. 2020; 12: e7124
        • Balayla J.
        • Shrem G.
        Use of artificial intelligence (AI) in the interpretation of intrapartum fetal heart rate (FHR) tracings: a systematic review and meta-analysis.
        Arch Gynecol Obstet. 2019; 300: 7-14
        • Lipschuetz M.
        • Guedalia J.
        • Rottenstreich A.
        • Novoselsky Persky M.
        • Cohen S.M.
        • Kabiri D.
        • et al.
        Prediction of vaginal birth after cesarean deliveries using machine learning.
        Am J Obstet Gynecol. 2020; 222: 613.e1-613.e12
        • Young R.C.
        Machine learning and statistical models to predict postpartum hemorrhage.
        Obstet Gynecol. 2020; 136 (PMID: 32590708): 194-195https://doi.org/10.1097/AOG.0000000000003980
        • Babayev E.
        Choosing the best embryo with the help of artificial intelligence.
        Fertil Steril. 2020; 114: 1171
        • Zaninovic N.
        • Rosenwaks Z.
        Artificial intelligence in human in vitro fertilization and embryology.
        Fertil Steril. 2020; 114: 914-920
        • Bao H.
        • Bi H.
        • Zhang X.
        • Zhao Y.
        • Dong Y.
        • Luo X.
        • et al.
        Artificial intelligence-assisted cytology for detection of cervical intraepithelial neoplasia or invasive cancer: a multicenter, clinical-based, observational study.
        Gynecol Oncol. 2020; 159: 171-178
        • Pergialiotis V.
        • Pouliakis A.
        • Parthenis C.
        • Damaskou V.
        • Chrelias C.
        • Papantoniou N.
        • et al.
        The utility of artificial neural networks and classification and regression trees for the prediction of endometrial cancer in postmenopausal women.
        Public Health. 2018; 164: 1-6
        • Akazawa M.
        • Hashimoto K.
        Artificial intelligence in ovarian Cancer diagnosis.
        Anticancer Res. 2020; 40: 4795-4800
        • Kotarska M.
        • Smoleń A.
        • Stachowicz N.
        • Kotarski J.
        Application of neuron networks in the diagnostics of endometrial pathologies.
        Ginekol Pol. 2011; 82 (PMID: 21851032): 344-349
        • Lucidarme O.
        • Akakpo J.P.
        • Granberg S.
        • Sideri M.
        • Levavi H.
        • Schneider A.
        • et al.
        Ovarian HistoScanning Clinical Study Group. A new computer-aided diagnostic tool for non-invasive characterisation of malignant ovarian masses: results of a multicentre validation study.
        Eur Radiol. 2010; 20: 1822-1830
        • Khazendar S.
        • Sayasneh A.
        • Al-Assam H.
        • Du H.
        • Kaijser J.
        • Ferrara L.
        • et al.
        Automated characterisation of ultrasound images of ovarian tumours: the diagnostic accuracy of a support vector machine and image processing with a local binary pattern operator.
        Facts Views Vis Obgyn. 2015; 7: 7-15
        • Aramendía-Vidaurreta V.
        • Cabeza R.
        • Villanueva A.
        • Navallas J.
        • Alcázar J.L.
        Ultrasound image discrimination between benign and malignant adnexal masses based on a neural network approach.
        Ultrasound Med Biol. 2016; 42: 742-752
        • Martínez-Más J.
        • Bueno-Crespo A.
        • Khazendar S.
        • Remezal-Solano M.
        • Martínez-Cendán J.P.
        • Jassim S.
        • et al.
        Evaluation of machine learning methods with Fourier Transform features for classifying ovarian tumors based on ultrasound images.
        PLoS One. 2019; 14e0219388
        • Biagiotti R.
        • Desii C.
        • Vanzi E.
        • Gacci G.
        Predicting ovarian malignancy: application of artificial neural networks to transvaginal and color Doppler flow US.
        Radiology. 1999; 210: 399-403
        • Clayton R.D.
        • Snowden S.
        • Weston M.J.
        • Mogensen O.
        • Eastaugh J.
        • Lane G.
        Neural networks in the diagnosis of malignant ovarian tumours.
        Br J Obstet Gynaecol. 1999; 106: 1078-1082
        • Tailor A.
        • Jurkovic D.
        • Bourne T.H.
        • Collins W.P.
        • Campbell S.
        Sonographic prediction of malignancy in adnexal masses using an artificial neural network.
        Br J Obstet Gynaecol. 1999; 106: 21-30
        • Smoleń A.
        • Czekierdowski A.
        • Stachowicz N.
        • Kotarski J.
        [Use of multilayer perception artificial neutral networks for the prediction of the probability of malignancy in adnexal tumors].
        Ginekol Pol. 2003; 74: 855-862
        • Szpurek D.
        • Moszynski R.
        • Smolen A.
        • Sajdak S.
        Artificial neural network computer prediction of ovarian malignancy in women with adnexal masses.
        Int J Gynaecol Obstet. 2005; 89: 108-113
        • Van Holsbeke C.
        • Van Calster B.
        • Valentin L.
        • Testa A.C.
        • Ferrazzi E.
        • Dimou I.
        • et al.
        International Ovarian Tumor Analysis Group. External validation of mathematical models to distinguish between benign and malignant adnexal tumors: a multicenter study by the International Ovarian Tumor Analysis Group.
        Clin Cancer Res. 2007; 13: 4440-4447
        • Zhang L.
        • Huang J.
        • Liu L.
        Improved deep learning network based in combination with cost-sensitive learning for early detection of ovarian Cancer in color ultrasound detecting system.
        J Med Syst. 2019; 43: 251
        • Christiansen F.
        • Epstein E.L.
        • Smedberg E.
        • Åkerlund M.
        • Smith K.
        • Epstein E.L.
        Ultrasound image analysis using deep neural networks for discriminating between benign and malignant ovarian tumors: comparison with expert subjective assessment.
        Ultrasound Obstet Gynecol. 2021; 57: 155-163
        • van den Noort F.
        • van der Vaart C.H.
        • Grob A.T.M.
        • van de Waarsenburg M.K.
        • Slump C.H.
        • van Stralen M.
        Deep learning enables automatic quantitative assessment of puborectalis muscle and urogenital hiatus in plane of minimal hiatal dimensions.
        Ultrasound Obstet Gynecol. 2019; 54: 270-275
        • Kabir M.
        Does artificial intelligence (AI) constitute an opportunity or a threat to the future of medicine as we know it?.
        Future Healthc J. 2019; 6: 190-191
        • Efron B.
        Prediction, estimation, and attribution.
        Int Stat Rev. 2020; 88: S28-S59
        • Tu J.V.
        Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes.
        J Clin Epidemiol. 1996; 49 (PMID: 8892489): 1225-1231https://doi.org/10.1016/s0895-4356(96)00002-9
        • Hashimoto D.A.
        • Rosman G.
        • Rus D.
        • Meireles O.R.
        Artificial intelligence in surgery: promises and perils.
        Ann Surg. 2018; 268: 70-76
        • Guerriero S.
        • Saba L.
        • Pascual M.A.
        • Ajossa S.
        • Rodriguez I.
        • Mais V.
        • et al.
        Transvaginal ultrasound vs magnetic resonance imaging for diagnosing deep infiltrating endometriosis: systematic review and meta-analysis.
        Ultrasound Obstet Gynecol. 2018; 51 (PMID: 29154402): 586-595https://doi.org/10.1002/uog.18961
        • Urushibara A.
        • Saida T.
        • Mori K.
        • Ishiguro T.
        • Sakai M.
        • Masuoka S.
        • et al.
        Diagnosing uterine cervical cancer on a single T2-weighted image: comparison between deep learning versus radiologists.
        Eur J Radiol. 2021; 135109471
        • Wang M.
        • Perucho J.A.U.
        • Tse K.Y.
        • Chu M.M.Y.
        • Ip P.
        • Lee E.Y.P.
        MRI texture features differentiate clinicopathological characteristics of cervical carcinoma.
        Eur Radiol. 2020; 30: 5384-5391
        • Dong H.C.
        • Dong H.K.
        • Yu M.H.
        • Lin Y.H.
        • Chang C.C.
        Using deep learning with convolutional neural network approach to identify the invasion depth of endometrial Cancer in myometrium using MR images: a pilot study.
        Int J Environ Res Public Health. 2020; 17: 5993https://doi.org/10.3390/ijerph17165993
        • Chen X.
        • Wang Y.
        • Shen M.
        • Yang B.
        • Zhou Q.
        • Yi Y.
        • et al.
        Deep learning for the determination of myometrial invasion depth and automatic lesion identification in endometrial cancer MR imaging: a preliminary study in a single institution.
        Eur Radiol. 2020; 30: 4985-4994