Transcranial Ultrasonographic Image Analysis System for Decision Support in Parkinson Disease

Attention-enhanced dilated convolution for Parkinson's disease detection using transcranial sonography

BACKGROUND

Transcranial sonography (TCS) plays a crucial role in diagnosing Parkinson's disease. However, the intricate nature of TCS pathological features, the lack of consistent diagnostic criteria, and the dependence on physicians' expertise can hinder accurate diagnosis. Current TCS-based diagnostic methods, which rely on machine learning, often involve complex feature engineering and may struggle to capture deep image features. While deep learning offers advantages in image processing, it has not been tailored to address specific TCS and movement disorder considerations. Consequently, there is a scarcity of research on deep learning algorithms for TCS-based PD diagnosis.

METHODS

This study introduces a deep learning residual network model, augmented with attention mechanisms and multi-scale feature extraction, termed AMSNet, to assist in accurate diagnosis. Initially, a multi-scale feature extraction module is implemented to robustly handle the irregular morphological features and significant area information present in TCS images. This module effectively mitigates the effects of artifacts and noise. When combined with a convolutional attention module, it enhances the model's ability to learn features of lesion areas. Subsequently, a residual network architecture, integrated with channel attention, is utilized to capture hierarchical and detailed textures within the images, further enhancing the model's feature representation capabilities.

RESULTS

The study compiled TCS images and personal data from 1109 participants. Experiments conducted on this dataset demonstrated that AMSNet achieved remarkable classification accuracy (92.79%), precision (95.42%), and specificity (93.1%). It surpassed the performance of previously employed machine learning algorithms in this domain, as well as current general-purpose deep learning models.

CONCLUSION

The AMSNet proposed in this study deviates from traditional machine learning approaches that necessitate intricate feature engineering. It is capable of automatically extracting and learning deep pathological features, and has the capacity to comprehend and articulate complex data. This underscores the substantial potential of deep learning methods in the application of TCS images for the diagnosis of movement disorders.

Segmentation of Substantia Nigra in Brain Parenchyma Sonographic Images Using Deep Learning

Currently, Parkinson's Disease (PD) is diagnosed primarily based on symptoms by experts clinicians. Neuroimaging exams represent an important tool to confirm the clinical diagnosis. Among them, Brain Parenchyma Sonography (BPS) is used to evaluate the hyperechogenicity of Substantia Nigra (SN), found in more than 90% of PD patients. In this article, we exploit a new dataset of BPS images to investigate an automatic segmentation approach for SN that can increase the accuracy of the exam and its practicability in clinical routine. This study achieves state-of-the-art performance in SN segmentation of BPS images. Indeed, it is found that the modified U-Net network scores a Dice coefficient of 0.859 ± 0.037. The results presented in this study demonstrate the feasibility and usefulness of SN automatic segmentation in BPS medical images, to the point that this study can be considered as the first stage of the development of an end-to-end CAD (Computer Aided Detection) system. Furthermore, the used dataset, which will be further enriched in the future, has proven to be very effective in supporting the training of CNNs and may pave the way for future studies in the field of CAD applied to PD.

Ultrasonic Assessment of the Medial Temporal Lobe Tissue Displacements in Alzheimer’s Disease

We aim to estimate brain tissue displacements in the medial temporal lobe (MTL) using backscattered ultrasound radiofrequency (US RF) signals, and to assess the diagnostic ability of brain tissue displacement parameters for the differentiation of patients with Alzheimer’s disease (AD) from healthy controls (HC). Standard neuropsychological evaluation and transcranial sonography (TCS) for endogenous brain tissue motion data collection are performed for 20 patients with AD and for 20 age- and sex-matched HC in a prospective manner. Essential modifications of our previous method in US waveform parametrization, raising the confidence of micrometer-range displacement signals in the presence of noise, are done. Four logistic regression models are constructed, and receiver operating characteristic (ROC) curve analyses are applied. All models have cut-offs from 61.0 to 68.5% and separate AD patients from HC with a sensitivity of 89.5% and a specificity of 100%. The area under a ROC curve of predicted probability in all models is excellent (from 95.2 to 95.7%). According to our models, AD patients can be differentiated from HC by a sharper morphology of some individual MTL spatial point displacements (i.e., by spreading the spectrum of displacements to the high-end frequencies with higher variability across spatial points within a region), by lower displacement amplitude differences between adjacent spatial points (i.e., lower strain), and by a higher interaction of these attributes.