A Review on Computer Aided Diagnosis of Acute Brain Stroke

Abstract

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.

Automatic diagnosis of sleep apnea from biomedical signals using artificial intelligence techniques: Methods, challenges, and future works

“…In this section, the paper search is done based on the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) guidelines (Inamdar et al, 2021). The published papers search is performed between years 2016 and 2022 in the field of sleep apnea detection, where the general keywords like apnea, central sleep apnea, obstructive sleep apnea, and mixed sleep apnea, EOG, SpO2, EMG, ECG, EEG, PSG, respiration signals, Deep Learning, and Machine Learning have been used.…”

Artificial Intelligence and Medicine

“…With the advancement of medical imaging technology and the enhancement of clinical diagnosis precision, DL-based clinical diagnosis approaches have been intensively developed. A DL-based brain CT image processing system was introduced to achieve automatic identification of acute neurological events such as stroke (24,25). Zhu et al suggested an automatic diagnosis approach for ischemic stroke based on DL, with a sensitivity of 76.9%, a specificity of 84.0%, and an accuracy of 80.5%, which can offer doctors with acute ischemic stroke (26).…”

Deep Transfer Learning for Automatic Prediction of Hemorrhagic Stroke on CT Images

“…AI works on large datasets to detect useful patterns that helps in decision-making in disease diagnosis and hence treatment. Machine learning algorithms have been applied successfully for detecting and predicting hemorrhage stroke in NCCT brains [ 4 – 7 ]. Conventional image analysis techniques such as fuzzy C -means [ 8 ], level set [ 9 , 10 ], histogram analysis [ 11 ], region growing [ 12 ], thresholding [ 13 ], neural network [ 14 ], and random forest [ 15 ] have been used to successfully segment the brain hemorrhage.…”

“…In the thresholding technique, the hemorrhagic lesion is segmented into a region based on threshold of each pixel. Inamdar et al [ 7 ] presented a clustering algorithm using fuzzy C -mean and active contour methods to detect the brain hemorrhages. A fuzzy membership degree has been used to control the propagation parameters and to initialize the active contour of the desired object.…”