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Introduction Prompt recognition and aggressive management of acute intoxication due to organophosphorus poisoning are essential to minimize the morbidity and mortality. The present study was undertaken to know the prognosis and outcome of organophosphorus poisoning patients with the estimation of low-cost enzymes like the serum amylase level in a population with financial constraints. Methods In this cross-sectional study, we had enrolled 100 cases that had a history of exposure to organophosphorus compounds, and the serum amylase level was measured in all the patients. We assessed the outcome of all the patients in the form of discharge, need of ventilators, intensive care unit stay, and death. Results The mean serum amylase level in discharge patients was 335.40 ± 192.45, and in the patients who died it was 843.37 ± 22.60. It was significant to predict the outcome (t-value 7.07, p-value 0.0001, statistically significant). Conclusion Serum amylase level shows significant correlation with clinical outcomes in organophosphorus poisoning.

Swyer-James-MacLeod Syndrome (SJMS) appears as unilateral hyperlucency on chest imaging and is documented as a rare sequela of frequent paediatric respiratory infections. Due to its rarity, various causes of haemithoracic radio-opacity and radiolucency were studied while evaluating this case. In individuals with bronchiolitis obliterans, SJMS has been observed in about 4% of cases. The left lung is preferentially involved in most cases for unknown reasons. The syndrome usually develops following viral respiratory infection in infancy or early childhood, such as adenoviruses or Mycoplasma pneumoniae. It is predicted to have a prevalence of 0.01%. This case study focuses on an incidental finding of abnormal radiography identified during assessing a 54-year-old man with low back pain in Chronic Kidney Disease (CKD). Patient and relative should be explained about the Chest X-ray finding and the reason for SJMS to avoid unscrupulous investigations in the future as index patient had no respiratory symptoms.

Phonocardiogram (PCG) signals generated by the heart contain information about heart conditions. This review examines how PCG analysis identifies and diagnoses heart issues. We studied traditional signal processing and artificial intelligence techniques and provided a complete picture of the current state of this field. Adhering to the systematic review guidelines, our comprehensive review covers 103 studies from reputed journals. It includes Machine Learning (ML) and Deep Learning (DL) techniques used to develop the computer-aided diagnostic tools using PCG signals. This review evaluates the strengths and weaknesses of various ML and DL methods, emphasizing their effectiveness in diagnosing several abnormalities. Additionally, we examine the obstacles and challenges limiting the widespread adoption of PCG-based diagnostic systems in clinical settings. We outline a plan for future research to develop improved versions of PCG analysis models. These models will be more robust, precise, and user-friendly. They will improve cardiovascular care by enabling machines to screen for problems automatically and intelligently.

The current work proposes an efficient assessment of hypertension (HTN) using a Directional-Guided Motion Sensitive (DGMS) descriptor and Machine Learning (ML) algorithm. The main objective of the proposed work is to automate the detection of HTN using ultrasound (US) images. The four-chamber US images from 70 healthy subjects and 70 HTN patients are collected. A novel pipelined architecture has been developed in two stages with four phases: preprocessing, feature extraction using DGMS descriptor, feature ranking and selection, and classification using shallow K-Nearest Neighbor classifier. The proposed model has achieved a classification accuracy of 98% using a set of prominent features, predominating the performance attained by other approaches. This study suggests US contains predictive signals even when standard measures are normal and lays the groundwork for artificial intelligence-assisted cardiac assessment to aid quicker, more objective diagnosis and earlier treatment. If further validated on additional diverse patient data, the technology could be integrated into clinics to enhance HTN detection through automated, early discernment of subtle manifestations missed by human eyes and traditional metrics.

Amongst the most common causes of death globally, stroke is one of top three affecting over 100 million people worldwide annually. There are two classes of stroke, namely ischemic stroke (due to impairment of blood supply, accounting for ~70% of all strokes) and hemorrhagic stroke (due to bleeding), both of which can result, if untreated, in permanently damaged brain tissue. The discovery that the affected brain tissue (i.e., ‘ischemic penumbra’) can be salvaged from permanent damage and the bourgeoning growth in computer aided diagnosis has led to major advances in stroke management. Abiding to the Preferred Reporting Items for Systematic Review and Meta–Analyses (PRISMA) guidelines, we have surveyed a total of 177 research papers published between 2010 and 2021 to highlight the current status and challenges faced by computer aided diagnosis (CAD), machine learning (ML) and deep learning (DL) based techniques for CT and MRI as prime modalities for stroke detection and lesion region segmentation. This work concludes by showcasing the current requirement of this domain, the preferred modality, and prospective research areas.

One of the foremost causes of death in males worldwide is prostate cancer. The identification, detection and diagnosis of the same is very crucial in saving lives. In this paper, we present an efficient gland segmentation model using digital histopathology and deep learning. These methods have the potential to revolutionize medicine by identifying hidden patterns within the image. The recent improvements in data acquisition, processing and analysis of Deep Learning Models has made Artificial Intelligence driven healthcare a very lucrative area, in terms of data inference and delivering meaningful insights. This study presents an automated method for segmenting histopathological images of human prostate glands. The main focus is developing new methods for segmenting histopathological images of prostate gland using a multi-channel algorithm with an attention mechanism to detect important areas. We compare our results with a host of contemporary techniques and show that our method performs better at the segmentation task for histopathological imagery. Our method is able to delineate gland and background parts with an average Dice-coefficient of 0.9168. In this attention-based model we propose for semantic segmentation of prostate glands the potential to provide accurate segmentation versus tumor features, which has significant implications for medical screening applications.