Machine Learning in Medicine

Semester: 
Spring

Algorithms in Medicine

Semester: 
Fall
Mousa, D., N. Zayed, and I. A. Yassine, "Correlation transfer function analysis as a biomarker for Alzheimer brain plasticity using longitudinal resting-state fMRI data.", Scientific reports, vol. 13, issue 1, pp. 21559, 2023. Abstract

Neural plasticity is the ability of the brain to alter itself functionally and structurally as a result of its experience. However, longitudinal changes in functional connectivity of the brain are still unrevealed in Alzheimer's disease (AD). This study aims to discover the significant connections (SCs) between brain regions for AD stages longitudinally using correlation transfer function (CorrTF) as a new biomarker for the disease progression. The dataset consists of: 29 normal controls (NC), and 23, 24, and 23 for early, late mild cognitive impairments (EMCI, LMCI), and ADs, respectively, along three distant visits. The brain was divided into 116 regions using the automated anatomical labeling atlas, where the intensity time series is calculated, and the CorrTF connections are extracted for each region. Finally, the standard t-test and ANOVA test were employed to investigate the SCs for each subject's visit. No SCs, along three visits, were found For NC subjects. The most SCs were mainly directed from cerebellum in case of EMCI and LMCI. Furthermore, the hippocampus connectivity increased in LMCI compared to EMCI whereas missed in AD. Additionally, the patterns of longitudinal changes among the different AD stages compared to Pearson Correlation were similar, for SMC, VC, DMN, and Cereb networks, while differed for EAN and SN networks. Our findings define how brain changes over time, which could help detect functional changes linked to each AD stage and better understand the disease behavior.

Bibars, M., P. E. Salah, A. Eldeib, M. A. Elattar, and I. A. Yassine, "Cross-Modality Deep Transfer Learning: Application to Liver Segmentation in CT and MRI", Annual Conference on Medical Image Understanding and Analysis, UK, pp. 96-110, June, 2023.
Salah, P. E., M. Bibars, A. Eldeib, A. M. Ghanem, A. M. Gharib, K. Z. Abd-Elmoniem, M. A. Elattar, and I. A. Yassine, "Iterative Refinement Algorithm for Liver Segmentation Ground-Truth Generation Using Fine-Tuning Weak Labels for CT and Structural MRI", Annual Conference on Medical Image Understanding and Analysis, UK, pp. 33-47, 2023.
Khder, S. M., E. A. H. Mohamed, and I. A. Yassine, "A Clustering- based Fusion System for Blastomere Localization", Biomedical Engineering: Applications, Basis and Communications, vol. 34, issue 4, pp. 2250021, 2022.
Koko, R. R. Z., I. A. Yassine, M. A. Wahed, J. K. Madete, and M. A. Rushdi, "Dynamic Construction of Outlier Detector Ensembles With Bisecting K-Means Clustering", EEE Access, vol. 11, pp. 24431-24447, 2023.
Morsy, S. E., N. Zayed, and I. A. Yassine, "Hierarchical based classification method based on fusion of Gaussian map descriptors for Alzheimer diagnosis using T-weighted magnetic resonance imaging.", Scientific reports, vol. 13, issue 1, pp. 13734, 2023. Abstract

Alzheimer's disease (AD) is considered one of the most spouting elderly diseases. In 2015, AD is reported the US's sixth cause of death. Substantially, non-invasive imaging is widely employed to provide biomarkers supporting AD screening, diagnosis, and progression. In this study, Gaussian descriptors-based features are proposed to be efficient new biomarkers using Magnetic Resonance Imaging (MRI) T-weighted images to differentiate between Alzheimer's disease (AD), Mild Cognitive Impairment (MCI), and Normal controls (NC). Several Gaussian map-based features are extracted such as Gaussian shape operator, Gaussian curvature, and mean curvature. The aforementioned features are then introduced to the Support Vector Machine (SVM). They were, first, calculated separately for the Hippocampus and Amygdala. Followed by the fusion of the features. Moreover, Fusion of the regions before feature extraction was also employed. Alzheimer's disease Neuroimaging Initiative (ADNI) dataset, formed of 45, 55, and 65 cases for AD, MCI, and NC respectively, is appointed in this study. The shape operator feature outperformed the other features, with 74.6%, and 98.9% accuracy in the case of normal vs. abnormal, and AD vs. MCI classification respectively.

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