Three port versus four port laparoscopic cholecystectomy: a prospective comparative clinical study

Background: Although, traditional laparoscopic cholecystectomy is performed using four-port technique, various modifications were made to further enhance the advantages of laparoscopic cholecystectomy. Aim of the study is to compare the results of three-port and four-port laparoscopic cholecystectomy at single center in terms of technical feasibility, safety of the procedure, operative time, intra-operative complications, postoperative pain and post-operative analgesia requirementMethods: It was a  prospective comparative study conducted  in the department of surgery Skims Medical college Srinagar, India from July 2015 to March 2017. The study was performed on all adult patients with ultrasound documented cholelithiasis and gall bladder Polyposis. The total number of patients studied was 100 which were divided into two groups of 50 each.Results: The average operative time in three port group was 29.2 minutes (range, 15-37) compared to 30.66 minutes (range, 15-42) in four port group, which was statistically insignificant. The final visual analog scores for pain in the postoperative period was 2.30 vs 2.86 in three port and four port group respectively, with a P value=0.008, which was statistically significant.Conclusions: The three-port technique is as safe as the standard four-port technique and can be a viable alternative to four port cholecystectomy with an advantage of less pain and less analgesic requirement and better cosmetic results

Health Care Seeking Behavior: Factors for The Utilization of Indigenous Medical Treatment in District Sheikhupura

The current study was conducted in the area of medical sociology. This study's primary goal was to determine the prevalence of different health attitudes and how those beliefs affected the usage of healthcare services. The role of family on its members to choose Indigenous medical treatment and the effect of Socio-economic status in the utilization of Indigenous medical treatment are the research questions formulated for the present research. The study was conducted within the framework of Talcott Parsons theory of Doctor Patient Relationship. The sample of the present study consisted of 10 married and 10 unmarried female respondents of Mohallah Dar Ul Islam in Farooqabad and 10 married and 10 unmarried female respondents from Mohallah Siddique Haidery in Farooqabad district Sheikhupura which is a rural area. The sample of these 40 respondents was drawn through convenient sampling procedure. The interview was conducted for data collection. Interviews with survey participants yielded personal data and information about their responses to health and medical services. The ensuing data was then examined to ascertain the variables influencing medical services and health-related behaviors in the research regions. Variables such, Gender, Education, Income and Family backgrounds took into consideration in the present research. Thematic analysis showed mix responses. Indigenous medical ideas and their utilization have been supported by a number of factors, including the negative impacts of allopathic medicine, the beliefs of one's forefathers, low socioeconomic level, social distance from modern medical professionals, and a strong link with indigenous caregivers. The term used for Indigenous care provider was as traditional healer and indigenous practitioner whereas, for the modern care provider, the terms as medical doctors, medical professional and surgeon were used. It was concluded that rural people strongly depend on indigenous medical treatment due to their concrete beliefs, lack of education, lack of allocation of modern medical resources in their area, poor socio-economic status and the fulfillment of expectations to high regard from indigenous treatment that resultantly leads to low utilization of modern medical treatment. Key Words: Healthcare services, Behaviour, Sheikhupura, Patients, Medical Sociology, Medical Treatment, Attitude, Therapy, &nbsp

IoMT-Enabled Computer-Aided Diagnosis of Pulmonary Embolism from Computed Tomography Scans Using Deep Learning

The Internet of Medical Things (IoMT) has revolutionized Ambient Assisted Living (AAL) by interconnecting smart medical devices. These devices generate a large amount of data without human intervention. Learning-based sophisticated models are required to extract meaningful information from this massive surge of data. In this context, Deep Neural Network (DNN) has been proven to be a powerful tool for disease detection. Pulmonary Embolism (PE) is considered the leading cause of death disease, with a death toll of 180,000 per year in the US alone. It appears due to a blood clot in pulmonary arteries, which blocks the blood supply to the lungs or a part of the lung. An early diagnosis and treatment of PE could reduce the mortality rate. Doctors and radiologists prefer Computed Tomography (CT) scans as a first-hand tool, which contain 200 to 300 images of a single study for diagnosis. Most of the time, it becomes difficult for a doctor and radiologist to maintain concentration going through all the scans and giving the correct diagnosis, resulting in a misdiagnosis or false diagnosis. Given this, there is a need for an automatic Computer-Aided Diagnosis (CAD) system to assist doctors and radiologists in decision-making. To develop such a system, in this paper, we proposed a deep learning framework based on DenseNet201 to classify PE into nine classes in CT scans. We utilized DenseNet201 as a feature extractor and customized fully connected decision-making layers. The model was trained on the Radiological Society of North America (RSNA)-Pulmonary Embolism Detection Challenge (2020) Kaggle dataset and achieved promising results of 88%, 88%, 89%, and 90% in terms of the accuracy, sensitivity, specificity, and Area Under the Curve (AUC), respectively