Artificial Intelligence-Enabled Medical Analysis for Intracranial Cerebral Hemorrhage Detection and Classification

Abstract

Intracranial hemorrhage (ICH) becomes a crucial healthcare emergency, which requires earlier detection and accurate assessment. Owing to the increased death rate (around 40%), the earlier recognition and classification of disease using computed tomography (CT) images are necessary to ensure a favourable prediction and restrain the existence of neurologic deficits. Since the manual diagnosis approach is time-consuming, automated ICH detection and classification models using artificial intelligence (AI) models are required. With this motivation, this study introduces an AI-enabled medical analysis tool for ICH detection and classification (AIMA-ICHDC) using CT images. The proposed AIMA-ICHDC technique aims at identifying the presence of ICH and identifying the different grades. In addition, the AIMA-ICHDC technique involves the design of glowworm swarm optimization with fuzzy entropy clustering (GSO-FEC) technique for the segmentation process. Besides, the VGG-19 model was executed for generating a collection of feature vectors and the optimal mixed-kernel-based extreme learning machine (OMKELM) model is utilized as a classifier. To optimally select the weight parameter of the MKELM technique, the coyote optimization algorithm (COA) was utilized. A wide range of simulation analyses are carried out under varying aspects. As part of the AIMA-ICHDC method, ICH can be detected and graded using a single sample. For segmentation, the AIMA-ICHDC technique uses the GSO-FEC method, which is the design of glowworm swarm optimization (GSO). The comparative outcomes highlighted the betterment of the AIMA-ICHDC technique compared to the recent state-of-the-art ICH classification approaches in terms of several measures.

The signs of computer tomography combined with artificial intelligence can indicate the correlation between status of consciousness and primary brainstem hemorrhage of patients

“…Brainstem hemorrhage is a common serious disease and a common cause of acute or chronic DOC ( 2 ), with poor prognosis. In recent years, advances in CT signs combined with AI have made the diagnosis of ICH more accurate ( 18 ). Here, the CT signs of PBH patients are analyzed retrospectively by combining radiologists and AI in the hope of finding simple CT signs that indicate the correlation between conscious state and PBH and provide references for clinical diagnosis, therapy and scientific research.…”

“…In recent years, with rapid advances of AI techniques, image omics and machine learning, various prediction models have emerged with ICH patients ( 18 , 37 ). Here, the CT signs of PBH patients were initially analyzed to provide a reference for an accurate indicative model, that should include hemorrhage volume and whether hemorrhage involving the ventricular system.…”

“…However, this is an observational and qualitative judgment based on CT signs in ICH patients, and quantitative diagnosis based on CT signs is now difficult. But with the progress of artificial intelligence (AI) technique, the traditional CT scan has developed new possibilities in diagnosing ICH ( 18 ). The application of AI technique in ICH mainly includes four aspects ( 19 ): etiological diagnosis, division of ICH, predicting the progression and prognosis of ICH.…”

Retracted: Artificial Intelligence-Enabled Medical Analysis for Intracranial Cerebral Hemorrhage Detection and Classification

“…Journal of Healthcare Engineering has retracted the article titled "Artifcial Intelligence-Enabled Medical Analysis for Intracranial Cerebral Hemorrhage Detection and Classifcation" [1] due to concerns that the peer review process has been compromised. Following an investigation conducted by the Hindawi Research Integrity team [2], signifcant concerns were identifed with the peer reviewers assigned to this article; the investigation has concluded that the peer review process was compromised.…”

“…Journal of Healthcare Engineering has retracted the article titled “Artificial Intelligence-Enabled Medical Analysis for Intracranial Cerebral Hemorrhage Detection and Classification” [ 1 ] due to concerns that the peer review process has been compromised.…”