The advent of computer vision technology and increased usage of video cameras in clinical settings have facilitated advancements in movement disorder analysis. This review investigated these advancements in terms of providing practical, low-cost solutions for the diagnosis and analysis of movement disorders, such as Parkinson's disease, ataxia, dyskinesia, and Tourette syndrome. Traditional diagnostic methods for movement disorders are typically reliant on the subjective assessment of motor symptoms, which poses inherent challenges. Furthermore, early symptoms are often overlooked, and overlapping symptoms across diseases can complicate early diagnosis. Consequently, deep learning has been used for the objective video-based analysis of movement disorders. This study systematically reviewed the latest advancements in automatic two-dimensional & three-dimensional video analysis using deep learning for movement disorders. We comprehensively analyzed the literature published until September 2023 by searching the Web of Science, PubMed, Scopus, and Embase databases. We identified 68 relevant studies and extracted information on their objectives, datasets, modalities, and methodologies. The study aimed to identify, catalogue, and present the most significant advancements, offering a consolidated knowledge base on the role of video analysis and deep learning in movement disorder analysis. First, the objectives, including specific PD symptom quantification, ataxia assessment, cerebral palsy assessment, gait disorder analysis, tremor assessment, tic detection (in the context of Tourette syndrome), dystonia assessment, and abnormal movement recognition were discussed. Thereafter, the datasets used in the study were examined. Subsequently, video modalities and deep learning methodologies related to the topic were investigated. Finally, the challenges and opportunities in terms of datasets, interpretability, evaluation methods, and home/remote monitoring were discussed.

Federated learning provides a privacy-preserving framework for medical image analysis but is also vulnerable to a unique category of adversarial attacks. This article presents an in-depth exploration of these vulnerabilities, emphasizing the potential for adversaries to execute attack transferability, a phenomenon where adversarial attacks developed on one model can be successfully applied to other models within the federated network. We delve into the specific risks associated with such attacks in the context of medical imaging, using domain-specific MRI tumor and pathology datasets. Our comprehensive evaluation assesses the efficacy of various known threat scenarios within a federated learning environment. The study demonstrates the system's susceptibility to multiple forms of attacks and highlights how domain-specific configurations can significantly elevate the success rate of these attacks. This analysis brings to light the need for defense mechanisms and advocates for a reevaluation of the current security protocols in federated medical image analysis systems.

Karyotyping is of importance for detecting chromosomal aberrations in human disease. However, chromosomes easily appear curved in microscopic images, which prevents cytogeneticists from analyzing chromosome types. To address this issue, we propose a framework for chromosome straightening, which comprises a preliminary processing algorithm and a generative model called masked conditional variational autoencoders (MC-VAE). The processing method utilizes patch rearrangement to address the difficulty in erasing low degrees of curvature, providing reasonable preliminary results for the MC-VAE. The MC-VAE further straightens the results by leveraging chromosome patches conditioned on their curvatures to learn the mapping between banding patterns and conditions. During model training, we apply a masking strategy with a high masking ratio to train the MC-VAE with eliminated redundancy. This yields a non-trivial reconstruction task, allowing the model to effectively preserve chromosome banding patterns and structure details in the reconstructed results. Extensive experiments on three public datasets with two stain styles show that our framework surpasses the performance of state-of-the-art methods in retaining banding patterns and structure details. Compared to using real-world bent chromosomes, the use of high-quality straightened chromosomes generated by our proposed method can improve the performance of various deep learning models for chromosome classification by a large margin. Such a straightening approach has the potential to be combined with other karyotyping systems to assist cytogeneticists in chromosome analysis.

Accurate pulmonary nodule detection is a crucial step in lung cancer screening. Computer-aided detection (CAD) systems are not routinely used by radiologists for pulmonary nodule detection in clinical practice despite their potential benefits. Maximum intensity projection (MIP) images improve the detection of pulmonary nodules in radiological evaluation with computed tomography (CT) scans. Inspired by the clinical methodology of radiologists, we aim to explore the feasibility of applying MIP images to improve the effectiveness of automatic lung nodule detection using convolutional neural networks (CNNs). We propose a CNN-based approach that takes MIP images of different slab thicknesses (5 mm, 10 mm, 15 mm) and 1 mm axial section slices as input. Such an approach augments the two-dimensional (2-D) CT slice images with more representative spatial information that helps discriminate nodules from vessels through their morphologies. Our proposed method achieves sensitivity of 92.7% with 1 false positive per scan and sensitivity of 94.2% with 2 false positives per scan for lung nodule detection on 888 scans in the LIDC-IDRI dataset. The use of thick MIP images helps the detection of small pulmonary nodules (3 mm-10 mm) and results in fewer false positives. Experimental results show that utilizing MIP images can increase the sensitivity and lower the number of false positives, which demonstrates the effectiveness and significance of the proposed MIP-based CNNs framework for automatic pulmonary nodule detection in CT scans. The proposed method also shows the potential that CNNs could gain benefits for nodule detection by combining the clinical procedure.

This study aimed to determine the most appropriate touch-based interaction technique for I2Vote, an image-based audience response system for radiology education in which users need to accurately mark a target on a medical image. Four plausible techniques were identified: land-on, take-off, zoom-pointing, and shift. The techniques were implemented in such a way that they could be used on any modern device. An empirical study was performed in which users marked a target on an image using all four techniques on either a smartphone or a tablet. The techniques were compared in terms of accuracy, efficiency, ease of use, intuitiveness, and compatibility with the different devices. The results showed that shift was the most accurate technique, but it was hampered by its high complexity and low intuitiveness. Land-on was the fastest technique but also the least accurate. Take-off and zoom-pointing provided the best trade-off between accuracy, efficiency, ease of use, and intuitiveness. We therefore conclude that both take-off and zoom-pointing are viable interaction techniques for I2Vote.

The case study describes a system that allows the use of interactive volume rendering for routine clinical diagnosis. In this setup, a SGI RealityStation acts like a remote rendering system which is controlled by a user interface that was added to an existing clinical system. The paper describes some implementation aspects, including several system optimizations that were carried out in order to optimize rendering speed. Initial results are very promising; the authors present three examples of clinical findings that were made using this system. Because of the setup, clinicians are now much more aware of the possibilities that modern hardware offers for interactive volume visualization.