Xuan Vinh To | Searcharxiv

arXiv2024

Neuroradiological features of traumatic encephalopathy syndrome using MRI and FDG-PET imaging: a case series in Australia

Objectives: This study examined whether currently existing clinical structural magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (18FDG-PET) capabilities and board-certified radiologists' reports and interpretations can assist with traumatic encephalopathy syndrome (TES) diagnosis. Design: retrospective case series. Setting: this study assessed six patients with TES criteria recruited from the Sydney area, Australia Main outcomes: patients' clinical history and clinical presentation along TES diagnostic criteria, board-certified radiologist reports of structural MRI and 18FDG-PET. Results: one patient was classified as possible CTE, and the others were classified as probable CTE with significant RHI exposure history and a spectrum of cognitive deficits and other neuropsychiatric disturbances consistent with TES diagnostic criteria. Most common radiological features included atrophy of posterior superior parietal region and Evans Index > 0.25. FDG-PET's pattern of common regions of hypometabolism and differing hypometabolism across participants suggested that FDG-PET and structural MRI have the potential for stratifying different TES stages. Conclusions: this study highlights the potential of a combined clinical and radiological approach using solely current capabilities to improve TES diagnosis and suggests a larger study. Such expanded investigation is crucial for advancing the ante-mortem diagnosis and management of CTE.

q-bio.NC↗

arXiv2023

Cascaded Multi-Modal Mixing Transformers for Alzheimer's Disease Classification with Incomplete Data

Accurate medical classification requires a large number of multi-modal data, and in many cases, different feature types. Previous studies have shown promising results when using multi-modal data, outperforming single-modality models when classifying diseases such as Alzheimer's Disease (AD). However, those models are usually not flexible enough to handle missing modalities. Currently, the most common workaround is discarding samples with missing modalities which leads to considerable data under-utilization. Adding to the fact that labeled medical images are already scarce, the performance of data-driven methods like deep learning can be severely hampered. Therefore, a multi-modal method that can handle missing data in various clinical settings is highly desirable. In this paper, we present Multi-Modal Mixing Transformer (3MAT), a disease classification transformer that not only leverages multi-modal data but also handles missing data scenarios. In this work, we test 3MT for AD and Cognitively normal (CN) classification and mild cognitive impairment (MCI) conversion prediction to progressive MCI (pMCI) or stable MCI (sMCI) using clinical and neuroimaging data. The model uses a novel Cascaded Modality Transformer architecture with cross-attention to incorporate multi-modal information for more informed predictions. We propose a novel modality dropout mechanism to ensure an unprecedented level of modality independence and robustness to handle missing data scenarios. The result is a versatile network that enables the mixing of arbitrary numbers of modalities with different feature types and also ensures full data utilization missing data scenarios. The model is trained and evaluated on the ADNI dataset with the SOTRA performance and further evaluated with the AIBL dataset with missing data.

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