An Artificial Intelligence-Based Bio-Medical Stroke Prediction and Analytical System Using a Machine Learning Approach.

Stroke-related disabilities can have a major negative effect on the economic well-being of the person. When left untreated, a stroke can be fatal. According to the findings of this study, people who have had strokes generally have abnormal biosignals. Patients will be able to obtain prompt therapy in this manner if they are carefully monitored; their biosignals will be precisely assessed and real-time analysis will be performed. On the contrary, most stroke diagnosis and prediction systems rely on image analysis technologies such as CT or MRI, which are not only expensive but also hard to use. In this study, we develop a machine learning algorithm for the prediction of stroke in the brain, and this prediction is carried out from the real-time samples of electromyography (EMG) data. The study uses synthetic samples for training the support vector machine (SVM) classifier and then the testing is conducted in real-time samples. To improve the accuracy of prediction, the samples are generated using the data augmentation principle, which supports training with vast data. The simulation is conducted to test the efficacy of the model, and the results show that the proposed classifier achieves a higher rate of classification accuracy than the existing methods. Furthermore, it is seen that the rate of precision, recall, and f-measure is higher in the proposed SVM than in other methods.

Region Convolutional Neural Network for Brain Tumor Segmentation.

Gliomas are often difficult to find and distinguish using typical manual segmentation approaches because of their vast range of changes in size, shape, and appearance. Furthermore, the manual annotation of cancer tissue segmentation under the close supervision of a human professional is both time-consuming and exhausting to perform. It will be easier and faster in the future to get accurate and quick diagnoses and treatments thanks to automated segmentation and survival rate prediction models that can be used now. In this article, a segmentation model is designed using RCNN that enables automatic prognosis on brain tumors using MRI. The study adopts a U-Net encoder for capturing the features during the training of the model. The feature extraction extracts geometric features for the estimation of tumor size. It is seen that the shape, location, and size of a tumor are significant factors in the estimation of prognosis. The experimental methods are conducted to test the efficacy of the model, and the results of the simulation show that the proposed method achieves a reduced error rate with increased accuracy than other methods.

A Deep Learning Framework for Earlier Prediction of Diabetic Retinopathy from Fundus Photographs.

Diabetic patients can also be identified immediately utilizing retinopathy photos, but it is a challenging task. The blood veins visible in fundus photographs are used in several disease diagnosis approaches. We sought to replicate the findings published in implementation and verification of a deep learning approach for diabetic retinopathy identification in retinal fundus pictures. To address this issue, the suggested investigative study uses recurrent neural networks (RNN) to retrieve characteristics from deep networks. As a result, using computational approaches to identify certain disorders automatically might be a fantastic solution. We developed and tested several iterations of a deep learning framework to forecast the progression of diabetic retinopathy in diabetic individuals who have undergone teleretinal diabetic retinopathy assessment in a basic healthcare environment. A collection of one-field or three-field colour fundus pictures served as the input for both iterations. Utilizing the proposed DRNN methodology, advanced identification of the diabetic state was performed utilizing HE detected in an eye's blood vessel. This research demonstrates the difficulties in duplicating deep learning approach findings, as well as the necessity for more reproduction and replication research to verify deep learning techniques, particularly in the field of healthcare picture processing. This development investigates the utilization of several other Deep Neural Network Frameworks on photographs from the dataset after they have been treated to suitable image computation methods such as local average colour subtraction to assist in highlighting the germane characteristics from a fundoscopy, thus, also enhancing the identification and assessment procedure of diabetic retinopathy and serving as a skilled guidelines framework for practitioners all over the globe.

An Audit of Requests for Fresh Frozen Plasma in a Tertiary Care Center in South India.

AIM: To audit the fresh frozen plasma (FFP) usage with an insight into various guidelines. MATERIALS AND METHODS: The blood bank records pertaining to FFP usage in patients admitted in our medical college hospital were retrospectively reviewed for 2 years for usage of FFP in various departments and evaluated for appropriateness of usage based on various guidelines, which included the 2013 guidelines published by the National Health and Medical Research Council and the Australasian Society for Blood Transfusion. RESULTS: A total of 785 units of FFPs were transfused to 207 patients during the study period. The appropriate usage was found to be 59.3%, and the usage was most appropriate in massive transfusions. CONCLUSION: This study highlights the nonadherence to guidelines among clinicians which is mainly due to lack of knowledge of appropriate usage.

A rapid spectrophotometric method for the determination of carbon monoxide in ambient air.

A sensitive spectrophotometric method for the determination of carbon monoxide is described, based on the reduction of palladium(II) by carbon monoxide. The resulting elemental palladium is reacted with iodate in acidic medium in the presence of chloride, to produce an ICl(-)(n) species that is readily extracted as an ion-pair with Pyronine-G into benzene. Measurement of the absorbance of the extract at 535 nm permits the determination of carbon monoxide down to 1 mul/l. in air. The effect of interfering gases is discussed. The method is suitable for determination of carbon monoxide in motor vehicle exhaust gases and ambient air.