A Pathophysiological Approach to Understanding Pulmonary Hypertension in Cardiac Surgery

Nguyen, Anne Quynh-Nhu; Deschamps, Alain; Perrault, Louis P.; Denault, André

doi:10.5772/27744

Cited by 1 publication

(2 citation statements)

References 91 publications

(88 reference statements)

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“…When comparing the performance of Nguyens et al's method to Chilamkurthy et al's work, it was observed that Nguyens et al achieved higher detection performance in terms of AUC for the equivalent-size dataset of 25k images, but lower AUC was obtained with the smaller CQ500 dataset. 11 19…”

Section: Discussionmentioning

confidence: 99%

“…7 MLS is defined as the displacement of the brain from its assumed center line, and it can be quantified by measuring the perpendicular distance between the IML and the shifted SP, and a common practice is to measure the maximum distance from the midline formed by the anterior falx and posterior falx to the SP at the axial level of the foramen of Monro. 8 9 11…”

Section: Introductionmentioning

confidence: 99%

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Automated Midline Shift Detection and Quantification in Traumatic Brain Injury: A Comprehensive Review

Agrawal,

Joshi,

Poonamallee

2024

Indian Journal of Neurotrauma

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Traumatic brain injury (TBI) often results in midline shift (MLS) that is a critical indicator of the severity and prognosis of head injuries. Automated analysis of MLS from head computed tomography (CT) scans using artificial intelligence (AI) techniques has gained much attention in the past decade and has shown promise in improving diagnostic efficiency and accuracy. This review aims to summarize the current state of research on AI-based approaches for MLS analysis in TBI cases, identify the methodologies employed, evaluate the performance of the algorithms, and draw conclusions regarding their potential clinical applicability. A comprehensive literature search was conducted, identifying 15 distinctive publications. The identified articles were analyzed for their focus on MLS detection and quantification using AI techniques, including their choice of AI algorithms, dataset characteristics, and methodology. The reviewed articles covered various aspects related to MLS detection and quantification, employing deep neural networks trained on two-dimensional or three-dimensional CT imaging datasets. The dataset sizes ranged from 11 patients' CT scans to 25,000 CT images. The performance of the AI algorithms exhibited variations in accuracy, sensitivity, and specificity, with sensitivity ranging from 70 to 100%, and specificity ranging from 73 to 97.4%. AI-based approaches utilizing deep neural networks have demonstrated potential in the automated detection and quantification of MLS in TBI cases. However, different researchers have used different techniques; hence, critical comparison is difficult. Further research and standardization of evaluation protocols are needed to establish the reliability and generalizability of these AI algorithms for MLS detection and quantification in clinical practice. The findings highlight the importance of AI techniques in improving MLS diagnosis and guiding clinical decision-making in TBI management.

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Section: Discussionmentioning

confidence: 99%