Increasing the impact of vertebrate scientific collections through 3D imaging: The openVertebrate (oVert) Thematic Collections Network

DC Blackburn, DM Boyer, JA Gray, J Winchester… - …, 2024 - academic.oup.com
The impact of preserved museum specimens is transforming and increasing by three-
dimensional (3D) imaging that creates high-fidelity online digital specimens. Through …

[HTML][HTML] AI-based decision support system for traumatic brain injury: a survey

F Rajaei, S Cheng, CA Williamson, E Wittrup… - Diagnostics, 2023 - mdpi.com
Traumatic brain injury (TBI) is one of the major causes of disability and mortality worldwide.
Rapid and precise clinical assessment and decision-making are essential to improve the …

[HTML][HTML] Multi-method diagnosis of CT images for rapid detection of intracranial hemorrhages based on deep and hybrid learning

BA Mohammed, EM Senan, ZG Al-Mekhlafi… - Electronics, 2022 - mdpi.com
Intracranial hemorrhaging is considered a type of disease that affects the brain and is very
dangerous, with high-mortality cases if there is no rapid diagnosis and prompt treatment. CT …

CHSNet: Automatic lesion segmentation network guided by CT image features for acute cerebral hemorrhage

B Xu, Y Fan, J Liu, G Zhang, Z Wang, Z Li… - Computers in Biology …, 2023 - Elsevier
Stroke is a cerebrovascular disease that can lead to severe sequelae such as hemiplegia
and mental retardation with a mortality rate of up to 40%. In this paper, we proposed an …

[HTML][HTML] A symmetric prior knowledge based deep learning model for intracerebral hemorrhage lesion segmentation

M Nijiati, A Tuersun, Y Zhang, Q Yuan, P Gong… - Frontiers in …, 2022 - frontiersin.org
Background: Accurate localization and classification of intracerebral hemorrhage (ICH)
lesions are of great significance for the treatment and prognosis of patients with ICH. The …

A comprehensive review and experimental comparison of deep learning methods for automated hemorrhage detection

AS Neethi, SK Kannath, AA Kumar, J Mathew… - … Applications of Artificial …, 2024 - Elsevier
Hemorrhagic stroke poses a critical medical emergency that necessitates prompt and
accurate diagnosis to prevent irreversible brain damage. The emergence of automated deep …

YOLOv5s-CAM: A deep learning model for automated detection and classification for types of intracranial hematoma in CT images

V Vidhya, U Raghavendra, A Gudigar, S Basak… - IEEE …, 2023 - ieeexplore.ieee.org
Intracranial hematoma due to traumatic brain injury is a serious health concern with rates of
morbidity and mortality that are increasing worldwide. Manual identification is slow, subject …

[HTML][HTML] Prediction of intraparenchymal hemorrhage progression and neurologic outcome in traumatic brain injury patients using radiomics score and clinical …

YJ Shih, YL Liu, JH Chen, CH Ho, CC Yang, TY Chen… - Diagnostics, 2022 - mdpi.com
(1) Background: Radiomics analysis of spontaneous intracerebral hemorrhages on
computed tomography (CT) images has been proven effective in predicting hematoma …

[HTML][HTML] Predicting vasospasm risk using first presentation aneurysmal subarachnoid hemorrhage volume: A semi-automated CT image segmentation analysis using …

JS Street, AS Pandit, AK Toma - PLoS One, 2023 - journals.plos.org
Purpose Cerebral vasospasm following aneurysmal subarachnoid hemorrhage (aSAH) is a
significant complication associated with poor neurological outcomes. We present a novel …

[HTML][HTML] Automated identification and quantification of traumatic brain injury from CT scans: Are we there yet?

A Hibi, M Jaberipour, MD Cusimano, A Bilbily… - Medicine, 2022 - journals.lww.com
Background: The purpose of this study was to conduct a systematic review for understanding
the availability and limitations of artificial intelligence (AI) approaches that could …