A Systematic Review of Diagnosis and Prognostication Techniques for Brain Ventriculomegaly Using Deep Learning

A disorder known as ventriculomegaly causes the cerebral ventricles in the developing fetal brain to expand. For proper therapy and prognostication of afflicted fetuses, prompt diagnosis and precise ventriculomegaly classification are essential. Recent developments in algorithms that employ deep learning have proven assure in a variety of medical imaging applications, such as the analysis of embryonic brain abnormalities. The research question driving this review is: "How effectively have deep learning methods been utilized for the diagnosis and prediction of brain ventriculomegaly, and what are the key findings, limitations, and trends in this area?"A thorough search strategy was used to find pertinent articles for this systematic review that were published in peer-reviewed journals, conference proceedings, and other pertinent sources. Deep learning, brain ventriculomegaly, diagnosis, and prognostication-related keywords were utilized to find papers. Select research that concentrated on using deep learning methods to diagnose brain ventriculomegaly and forecast its prognosis were subjected to inclusion and exclusion criteria.22 studies in total that satisfied the inclusion criteria were found during the review. Collectively, this research showed that there is considerable interest in using deep learning techniques to the diagnosis and prognosis of brain ventriculomegaly. The chosen research used a variety of deep learning architectures, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and attention mechanisms. These research' findings revealed promising diagnostic precision and prognostic skills. Deep learning models demonstrated the capacity to precisely identify brain ventriculomegaly from MRI and CT scan data. Some studies also emphasized the potential of deep learning methods for forecasting the development of ventriculomegaly and related outcomes.Collectively, the research under evaluation indicate that deep learning algorithms are capable of efficiently analyzing complicated medical imaging data and supporting the early and precise diagnosis of ventriculomegaly. Additionally, the possibility for ventriculomegaly progression and prognosis prediction utilizing these approaches provides useful information for individualized patient care and treatment planning. However, it's critical to recognize the restrictions found in all of the research under consideration, including difficulties with data accessibility, model interpretability, and generalizability. To fully achieve the clinical potential of deep learning in the context of brain ventriculomegaly diagnosis and prognostication, overcoming these constraints and doing additional research will be essential as the area of deep learning continues to develop.