Machine learning approach for enhancing the detection of endometriosis using ultrasound nano contract agents

By making the structures smaller and more sensitive, currents medical diagnostics is set to benefit from the technology with such inventions as nano contrast agents (NCAs). This study proposes a diagnostic approach that applies ultrasound NCAs in conjunction with machine learning (ML) to enhance the diagnosis of endometriosis, which, although is a common disease, is frequently misidentified. Because these contrast agents are at the nano-scale, the visualisation of the endometriotic lesions is improved and the distinction between them and other tissues with normal ultrasound technology is challenging. In addition, the deep learning algorithms utilized by the ML model for image and feature evaluation are more effective in identifying endometriotic tissue based on patterns generated by NCAs. Data shown prove that the performance of this approach enhances sensitivity and specificity and is far better than conventional ultrasound techniques. This new ML-derived approach which utilizes nano contrast agents in targeting the disease brings hope towards early detection of endometriosis thus assisting the clinicians in managing endometriosis afflicted patients adequately.