Effective Identification of Endometrial Tuberculosis for Infertility using Machine Learning - V, Garg Varsha
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Tuberculosis is a dreaded disease caused by Mycobacterium tuberculosis and India alone has 27% of the population suffering from Tuberculosis. Female Genital Tuberculosis (FGTB) is found to be a major reason for infertility; but is currently an under-researched medical condition. Among all, Endometrial TB (ETB) has a major impact on female fertility but it can be effectively treated on timely diagnosis. ETB accounts for 70% of infertile FGTB infected patients. An early, objective and reliable ETB identification causing infertility is a need of the hour in a populous country like India. As per…mehr

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Tuberculosis is a dreaded disease caused by Mycobacterium tuberculosis and India alone has 27% of the population suffering from Tuberculosis. Female Genital Tuberculosis (FGTB) is found to be a major reason for infertility; but is currently an under-researched medical condition. Among all, Endometrial TB (ETB) has a major impact on female fertility but it can be effectively treated on timely diagnosis. ETB accounts for 70% of infertile FGTB infected patients. An early, objective and reliable ETB identification causing infertility is a need of the hour in a populous country like India. As per our literature study, there is little discussion on computational methods available for the same till date. In order to identify the cause of infertility, women have to undergo several invasive and expensive tests. A varied and asymptomatic symptomatology, under-reporting and lack of sensitive reliable diagnostics are roadblocks to incidence estimation. For confirmed diagnosis, a high grade suspicion, detailed documentation of patient history, examination and series of discriminatory diagnostic and imaging test are required. Tubercular pathology is identified when a women presents with problem of infertility. Ultrasound remains the initial imaging modality in the evaluation of abdominal and pelvic pain of unknown etiology. Therefore, this thesis aims at developing a computational framework that comprises of efficient computing methods for identification of Endometrial Tuberculosis from TVUS images of uterus that will assist gynecologists and radiologists in diagnosis. Since, Transvaginal Ultrasound (TVUS) imaging is a non-invasive, primary and first line investigative technique; aim of this work is to develop an effective method for diagnosis of ETB from TVUS images. The prime objective of the research thesis is to develop a computational method with the aim of providing assistance to the medical fraternity in ETB identification which is a prime cause of Infertility in developing and underdeveloped countries. India leads with the highest TB incidence rate and a high infertility rate. To conduct the research, real time labelled 80 TVUS images have been collected from medical centers in Delhi and Ghaziabad and in consultation with medical experts from a leading Hospital in Delhi, India to generate a labelled, robust and invariant dataset.