Deep Learning Based Junctional Zone Quantification using 3D Transvaginal Ultrasound in Assisted Reproduction

A Deep Learning Fusion Approach to Diagnosis the Polycystic Ovary Syndrome (PCOS)

One of the leading causes of female infertility is PCOS, which is a hormonal disorder affecting women of childbearing age. The common symptoms of PCOS include increased acne, irregular period, increase in body hair, and overweight. Early diagnosis of PCOS is essential to manage the symptoms and reduce the associated health risks. Nonetheless, the diagnosis is based on Rotterdam criteria, including a high level of androgen hormones, ovulation failure, and polycystic ovaries on the ultrasound image (PCOM). At present, doctors and radiologists manually perform PCOM detection using ovary ultrasound by counting the number of follicles and determining their volume in the ovaries, which is one of the challenging PCOS diagnostic criteria. Moreover, such physicians require more tests and checks for biochemical/clinical signs in addition to the patient’s symptoms in order to decide the PCOS diagnosis. Furthermore, clinicians do not utilize a single diagnostic test or specific method to examine patients. This paper introduces the data set that includes the ultrasound image of the ovary with clinical data related to the patient that has been classified as PCOS and non-PCOS. Next, we proposed a deep learning model that can diagnose the PCOM based on the ultrasound image, which achieved 84.81% accuracy using the Inception model. Then, we proposed a fusion model that includes the ultrasound image with clinical data to diagnose the patient if they have PCOS or not. The best model that has been developed achieved 82.46% accuracy by extracting the image features using MobileNet architecture and combine with clinical features.

Access to infertility care and ART treatment in India: A clinician's perspective

Infertility is a worldwide problem that is increasing with time. The cause of infertility can be due to either male or female factors or both. The food, environmental, occupational and psychological factors do contribute to infertility. The infertility and the present scenario of assisted reproductive technology (ART) in India with regard to the clinics, clinicians and regulatory mechanisms in vogue are discussed. The potential of India as a reproductive tourist destination and surrogacy issues is included. The social, economic, and family problems arising as a consequence of infertility are discussed. The status of ART in India is getting improved and the gap between the West and India is expected to be minimized with the influence of regulatory mechanisms introduced through ART Act in India. The salient features that have a bearing on the infertility treatment outcomes, which are being neglected, or recent findings of research are included with special reference to possible future developments in the field of ART.

Strengths and limitations of diagnostic tools for endometriosis and relevance in diagnostic test accuracy research

Endometriosis is a chronic systemic disease that can cause pain, infertility and reduced quality of life. Diagnosing endometriosis remains challenging, which yields diagnostic delays for patients. Research on diagnostic test accuracy in endometriosis can be difficult due to verification bias, as not all patients with endometriosis undergo definitive diagnostic testing. The purpose of this State-of-the-Art Review is to provide a comprehensive update on the strengths and limitations of the diagnostic modalities used in endometriosis and discuss the relevance of diagnostic test accuracy research pertaining to each. We performed a comprehensive literature review of the following methods: clinical assessment including history and physical examination, biomarkers, diagnostic imaging, surgical diagnosis and histopathology. Our review suggests that, although non-invasive diagnostic methods, such as clinical assessment, ultrasound and magnetic resonance imaging, do not yet qualify formally as replacement tests for surgery in diagnosing all subtypes of endometriosis, they are likely to be appropriate for advanced stages of endometriosis. We also demonstrate in our review that all methods have strengths and limitations, leading to our conclusion that there should not be a single gold-standard diagnostic method for endometriosis, but rather, multiple accepted diagnostic methods appropriate for different circumstances. © 2022 International Society of Ultrasound in Obstetrics and Gynecology.

Artificial intelligence-the future is now

The pros and cons of artificial intelligence in assisted reproductive technology are presented.