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2 Biocybernetics and Biomedical Engineering

2 2022

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Assessment of CT for the categorization of hemorrhagic stroke (HS) and cerebral amyloid angiopathy hemorrhage (CAAH): A review

Sudarshan Vidya K., Raghavendra U., Gudigar Anjan, Ciaccio Edward J., Vijayananthan Anushya, Sahathevan Ramesh, Acharya U. Rajendra

Biocybernetics and Biomedical Engineering

2022

Vol. 42, no. 3

888--901

The management of intracerebral hemorrhage (ICH) requires prompt diagnostic assessment and recognition. Accurate localization and categorization of ICH-type is crucial. There are two main categories of ICH: 1) hemorrhagic stroke (HS), which occurs in the deeper or subcortical regions of the brain, where the arterial network tapers to fine end-arteries, and, 2) cerebral amyloid angiopathy hemorrhage (CAAH), which occurs at the superficial or cortical-subcortical region of the grey and white matter junction. Computed tomography (CT) and magnetic resonance imaging (MRI) are the most used imaging tools in diagnosing ICH. However, availability, time, and cost often prevent emergent MRI use. Therefore, CT remains the primary tool in the diagnosis of ICH. The assessment of imaging studies is time-dependent, and a radiologist should do a detailed diagnostic evaluation. Human error can occur in a pressured clinical setting, even for highly trained medical professionals. Assisted or automated computer-aided analysis of CT/MRI may help to reduce the assessment time, improve the diagnostic accuracy, better differentiate between types of ICH, and reduce the risk of human errors. This review evaluates CT and MRI’s role in distinguishing between the two varieties of ICH-HS and CAAH. It focuses on how CT could be utilized as the preferred diagnostic tool. In addition, we discuss the role of automation using machine learning (ML) and the role or advantages of ML in the automated assessment of CT for the detection and classification of HS and CAAH. We have included our observations for future research and the requirements for further evaluation.

Applications of machine-learning algorithms for prediction of benign and malignant breast lesions using ultrasound radiomics signatures: A multi-center study

Homayoun Hassan, Chan Wai Yee, Kuzan Taha Yusuf, Leong Wai Ling, Altintoprak Kübra Murzoglu, Mohammadi Afshin, Vijayananthan Anushya, Rahmat Kartini, Leong Sook Sam, Mirza-Aghazadeh-Attari Mohammad, Ejtehadifar Sajjad, Faeghi Fariborz, Acharya U. Rajendra, Ardakani Ali Abbasian

Biocybernetics and Biomedical Engineering

2022

Vol. 42, no. 3

921--933

Artificial intelligence (AI) algorithms have an enormous potential to impact the field of radiology and diagnostic imaging, especially the field of cancer imaging. There have been efforts to use AI models to differentiate between benign and malignant breast lesions. However, most studies have been single-center studies without external validation. The present study examines the diagnostic efficacy of machine-learning algorithms in differentiating benign and malignant breast lesions using ultrasound images. Ultrasound images of 1259 solid non-cystic lesions from 3 different centers in 3 countries (Malaysia, Turkey, and Iran) were used for the machine-learning study. A total of 242 radiomics features were extracted from each breast lesion, and the robust features were considered for models’ development. Three machine-learning algorithms were used to carry out the classification task, namely, gradient boosting (XGBoost), random forest, and support vector machine. Sensitivity, specificity, accuracy, and area under the ROC curve (AUC) were determined to evaluate the models. Thirty-three robust features differed significantly between the two groups from all of the features. XGBoost, based on these robust features, showed the most favorable profile for all cohorts, as it achieved a sensitivity of 90.3%, specificity of 86.7%, the accuracy of 88.4%, and AUC of 0.890. The present study results show that incorporating selected robust radiomics features into well-curated machine-learning algorithms can generate high sensitivity, specificity, and accuracy in differentiating benign and malignant breast lesions. Furthermore, our results show that this optimal performance is preserved even in external validation datasets.