Automated Detection and Screening of Traumatic Brain Injury (TBI) Using Computed Tomography Images: A Comprehensive Review and Future Perspectives

Vidhya,; Gudigar, Anjan; Raghavendra, U.; Hegde, Ajay; Menon, Girish; Molinari, Filippo; Ciaccio, Edward J.; Acharya, U. Rajendra

doi:10.3390/ijerph18126499

Cited by 40 publications

(24 citation statements)

References 127 publications

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“…From our extensive literature review, it was observed that many of the CAD tools in the area of several medical fields have used accuracy, sensitivity or recall, specificity, positive predictive value (PPV) or precision, F-measure or F-score, and area under the curve (AUC) to evaluate the performance of the system [ 274 , 275 , 276 ]. Similarly, the performance of the CAD tool for the identification of COVID-19 was also evaluated using the same performance parameters as mentioned above.…”

Section: Resultsmentioning

confidence: 99%

Role of Artificial Intelligence in COVID-19 Detection

Gudigar

Raghavendra

Nayak

et al. 2021

Sensors

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The global pandemic of coronavirus disease (COVID-19) has caused millions of deaths and affected the livelihood of many more people. Early and rapid detection of COVID-19 is a challenging task for the medical community, but it is also crucial in stopping the spread of the SARS-CoV-2 virus. Prior substantiation of artificial intelligence (AI) in various fields of science has encouraged researchers to further address this problem. Various medical imaging modalities including X-ray, computed tomography (CT) and ultrasound (US) using AI techniques have greatly helped to curb the COVID-19 outbreak by assisting with early diagnosis. We carried out a systematic review on state-of-the-art AI techniques applied with X-ray, CT, and US images to detect COVID-19. In this paper, we discuss approaches used by various authors and the significance of these research efforts, the potential challenges, and future trends related to the implementation of an AI system for disease detection during the COVID-19 pandemic.

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Section: Resultsmentioning

confidence: 99%

Role of Artificial Intelligence in COVID-19 Detection

Gudigar

Raghavendra

Nayak

et al. 2021

Sensors

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“…Machine Learning Techniques ML uses various algorithms for the following key steps: preprocessing and segmentation; feature extraction; dimensionality reduction or feature ranking; and classification. Image preprocessing is a crucial preliminary step as it enhances the image quality or resolution using appropriate filters [74][75][76]. Extraction of the correct region of interest (ROI) is the necessary initial step for coronary plaque detection before further characterization.…”

Section: And DL Techniques In Cadmentioning

confidence: 99%

Recent Trends in Artificial Intelligence-Assisted Coronary Atherosclerotic Plaque Characterization

Gudigar

Nayak

Samanth

et al. 2021

IJERPH

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Coronary artery disease is a major cause of morbidity and mortality worldwide. Its underlying histopathology is the atherosclerotic plaque, which comprises lipid, fibrous and—when chronic—calcium components. Intravascular ultrasound (IVUS) and intravascular optical coherence tomography (IVOCT) performed during invasive coronary angiography are reference standards for characterizing the atherosclerotic plaque. Fine image spatial resolution attainable with contemporary coronary computed tomographic angiography (CCTA) has enabled noninvasive plaque assessment, including identifying features associated with vulnerable plaques known to presage acute coronary events. Manual interpretation of IVUS, IVOCT and CCTA images demands scarce physician expertise and high time cost. This has motivated recent research into and development of artificial intelligence (AI)-assisted methods for image processing, feature extraction, plaque identification and characterization. We performed parallel searches of the medical and technical literature from 1995 to 2021 focusing respectively on human plaque characterization using various imaging modalities and the use of AI-assisted computer aided diagnosis (CAD) to detect and classify atherosclerotic plaques, including their composition and the presence of high-risk features denoting vulnerable plaques. A total of 122 publications were selected for evaluation and the analysis was summarized in terms of data sources, methods—machine versus deep learning—and performance metrics. Trends in AI-assisted plaque characterization are detailed and prospective research challenges discussed. Future directions for the development of accurate and efficient CAD systems to characterize plaque noninvasively using CCTA are proposed.

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“…Typically, automated detection systems utilize a combination of image processing techniques, including preprocessing, segmentation, feature extraction, feature selection, and classification. 2 Numerous ML and DL techniques have been employed for detecting and segmenting brain structures to enable volumetric analysis (such as NEUROShield), 11 as well as screening ICH and other related pathologies. [12][13][14][15] Advanced image processing techniques have been devised to detect ICH, potentially enhancing the speed and precision of ICH detection, thereby improving the patients' prognosis.…”

Section: Introductionmentioning

confidence: 99%

Automated Detection of Intracranial Hemorrhage from Head CT Scans Applying Deep Learning Techniques in Traumatic Brain Injuries: A Comparative Review

Agrawal

Poonamallee

Joshi³

2023

Indian Journal of Neurotrauma

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Traumatic brain injury (TBI) is not only an acute condition but also a chronic disease with long-term consequences. Intracranial hematomas are considered the primary consequences that occur in TBI and may have devastating effects that may lead to mass effect on the brain and eventually cause secondary brain injury. Emergent detection of hematoma in computed tomography (CT) scans and assessment of three major determinants, namely, location, volume, and size, is crucial for prognosis and decision-making, and artificial intelligence (AI) using deep learning techniques, such as convolutional neural networks (CNN) has received extended attention after demonstrations that it could perform at least as well as humans in imaging classification tasks. This article conducts a comparative review of medical and technological literature to update and establish evidence as to how technology can be utilized rightly for increasing the efficiency of the clinical workflow in emergency cases. A systematic and comprehensive literature search was conducted in the electronic database of PubMed and Google Scholar from 2013 to 2023 to identify studies related to the automated detection of intracranial hemorrhage (ICH). Inclusion and exclusion criteria were set to filter out the most relevant articles. We identified 15 studies on the development and validation of computer-assisted screening and analysis algorithms that used head CT scans. Our review shows that AI algorithms can prioritize radiology worklists to reduce time to screen for ICH in the head scans sufficiently and may also identify subtle ICH overlooked by radiologists, and that automated ICH detection tool holds promise for introduction into routine clinical practice.

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Automated Detection and Screening of Traumatic Brain Injury (TBI) Using Computed Tomography Images: A Comprehensive Review and Future Perspectives

Cited by 40 publications

References 127 publications

Role of Artificial Intelligence in COVID-19 Detection

Role of Artificial Intelligence in COVID-19 Detection

Recent Trends in Artificial Intelligence-Assisted Coronary Atherosclerotic Plaque Characterization

Automated Detection of Intracranial Hemorrhage from Head CT Scans Applying Deep Learning Techniques in Traumatic Brain Injuries: A Comparative Review

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