A Comprehensive Review on Machine Learning Based Models for Healthcare Applications

At present, there has been significant progress concerning AI and machine learning, specifically in medical sector. Artificial intelligence refers to computing programmes that replicate and simulate human intelligence, such as an individual's problem-solving capabilities or their capacity for learning. Moreover, machine learning can be considered as a subfield within the broader domain of artificial intelligence. The process automatically identifies and analyses patterns within unprocessed data. The objective of this work is to facilitate researchers in acquiring an extensive knowledge of machine learning and its utilisation within the healthcare domain. This research commences by providing a categorization of machine learning-based methodologies concerning healthcare. In accordance with the taxonomy, we have put forth, machine learning approaches in the healthcare domain are classified according to various factors. These factors include the methods employed for the process of preparing data for analysis, which includes activities such as data cleansing and data compression techniques. Additionally, the strategies for learning are utilised, such as reinforcement learning, semi-supervised learning, supervised learning, and unsupervised learning. are considered. Also, the evaluation approaches employed encompass simulation-based evaluation as well as evaluation of actual use in everyday situations. Lastly, the applications of these ML-based methods in medicine pertain towards diagnosis and treatment. Based on the classification we have put forward; we proceed to examine a selection of research that have been presented in the framework of machine learning applications within the healthcare domain. This review paper serves as a valuable resource for researchers seeking to gain familiarity with the latest research on ML applications concerning medicine. It aids towards the recognition for obstacles and limitations associated with ML in this domain, while also facilitating the identification of potential future research directions

Assessment of the outcomes of open side-to-side choledochoduodenostomy in the management of choledocholithiasis

Background: Gallstone disease is one of the most common digestive diseases leading to frequent hospital visits and its prevalence shows ethnic variability, with rates of approximately 10-15% in the United States and Europe. The present study aims to prospectively assess the outcomes of open side-to-side choledochoduodenostomy in the management of choledocholithiasis. Methods: This hospital-based prospective observational study was conducted in the Department of Surgery, Tezpur medical College and Hospital, Tezpur, over one year period, from July 2021 to June 2022. The study includes twenty-four patients admitted to the surgery department for bile duct stone operations. After intraoperative confirmation of the criteria, these patients underwent choledochoduodenostomy. The patients were followed for 2 months postoperatively after discharge. Results: Only a few patients had immediate postoperative complications which were managed conservatively. No patient had any evidence of retained stone, nor did they have any symptoms of cholangitis, features suggestive of the development of Sump syndrome, or any other follow-up postoperative complications. Conclusion: Open side-to-side choledochoduodenostomy should be considered a method of choice in remote areas where endoscopic facilities are lacking and in patients where cost is a factor in deciding the choice of procedure, with reduced postoperative complications like retained stones and a shorter duration of hospital stay in expert surgical hands

Role of Computed Tomography Scan in the Assessment and Management of Blunt Splenic Trauma in a Tertiary Care Hospital, Assam, India

Introduction: Trauma is the most common cause of mortality and morbidity in young individuals. Penetrating splenic injuries are more common than blunt injuries. The management of blunt splenic trauma has substantially evolved over the last few decades, moving from routine splenectomy to preserving the spleen wherever feasible. Aim: To determine the role of Multidetector Computed Tomography (MDCT) in the diagnosis and treatment of blunt splenic trauma. Materials and Methods: This hospital-based retrospective study was conducted in Department of General Surgery, Tezpur Medical College and Hospital, Tezpur, Assam, India. During the study period, there were 132 cases of blunt trauma abdomen. Among them, 122 patients had undergone MDCT of the abdomen. Clinical details of those 122 patients who did MDCT for possible blunt trauma of the abdomen were traced and were admitted to the Department of General Surgery, Tezpur Medical College and Hospital, Tezpur, Assam, India during the period from 1st October 2021 to 31st August 2022 retrospectively. The clinical data of these 122 patients were recorded. Of these 122 patients who underwent MDCT, 21 had splenic injuries. The patients who were treated conservatively were traced and the outcome of the treatment on follow-up, from the clinical notes. The preliminary MDCT findings of the patients were correlated with the final diagnosis and treatment. Fisher’s-exact tests and Chi-square were used for statistical analysis. Results: The 21 splenic injuries in this study were classified based on the American Association for the Surgery of Trauma (AAST) grading scales for organ injury, and 14 (66.67%) had Non Operative Management (NOM). Of the four patients with Contrast Material Extravasation (CME), all of them had undergone laparotomy related to the spleen (100%) and demonstrated active bleeding during surgery, but only three of the remaining 17 patients without CME (17.65%) required laparotomy related to the spleen; the difference was statistically found to be significant (p<0.01). Conclusion: The accurate diagnosis provided by MDCT evaluation of blunt splenic injuries helps in formulating the right approach for better management