Background & Aims: The diagnosis of abdominal tuberculosis has always been a challenge to the physician. The clinical presentation is subtle with many vague symptoms and nonspecific signs. We did this study to find the various diagnostic findings in a case of chronic abdominal pain and find out the efficacy of laparoscopy in diagnosing Koch’s abdomen. Materials and Methods: A prospective observational study was conducted on patients attending surgery department from Nov-2020 to Nov-2021 having clinical and radiological diagnosis of abdominal tuberculosis. Total 59 patients of suspected abdominal tuberculosis underwent diagnostic laparoscopy and started on anti-tuberculosis treatment. Result: The most common presenting symptom was abdominal pain present in 35 patients (59.32%). In CT scan, 31 of them were suggestive of abdominal tuberculosis and seven were inconclusive. Only 37 of them had positive (62.71%) histology for tuberculosis and 22 were negative (37.28%). The PPV and NPV of CT scan was 77.42% (95% CI=60.19-88.61%) and 85.71%% respectively. Out of 27, 20 mesenteric lymph nodes had positive histology for tuberculosis. 29 patients had caseating granuloma and 12 had non-caseating granuloma. All 18 histology negative patients had nonspecific chronic inflammation with reactive lymph nodes. Peritoneal fluid was aspirated and sent for CBNAAT from 30 patients (50.84%). 10 were positive (33.33%) for tuberculosis gene and 20 (66.33%) were negative. Conclusions: Performing laparoscopy in the majority of patients with suspected abdominal tuberculosis is a clinically rewarding idea. It has a high yield to establish the diagnosis of abdominal tuberculosis (65.78%) by sampling macroscopically pathological tissues. Keywords: Koch’s abdomen, Diagnostic laparoscopy, Koch’s abdomen

Recently, chronic patients are taking multiple medications incorrectly and taking the wrong medications due to similarity of drugs.It is possible that taking the improper medication can result in hazardous interactions with other medications or they will counteract the intended benefits of the medications, resulting in extra severe repercussions such as acute complications. The conventional methods are failed to provide the maximum efficiency. Therefore, this article is focused on implementation of faster recurrent convolutional neural networks (FR-CNN), which is capable of extracting the features from images. FR-CNN mainly used to analyze the patterns of the medicines and extracts the deep features. Further, classification of medicines is carried out by comparing with ground truth labels. The simulation results shows that the proposed system resulted in superior performance as compared to state of art approaches.

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