@Article{info:doi/10.2196/57723,
author="Iratni, Maya
and Abdullah, Amira
and Aldhaheri, Mariam
and Elharrouss, Omar
and Abd-alrazaq, Alaa
and Rustamov, Zahiriddin
and Zaki, Nazar
and Damseh, Rafat",
title="Transformers for Neuroimage Segmentation: Scoping Review",
journal="J Med Internet Res",
year="2025",
month="Jan",
day="29",
volume="27",
pages="e57723",
keywords="3D segmentation",
keywords="brain tumor segmentation",
keywords="deep learning",
keywords="neuroimaging",
keywords="transformer",
abstract="Background: Neuroimaging segmentation is increasingly important for diagnosing and planning treatments for neurological diseases. Manual segmentation is time-consuming, apart from being prone to human error and variability. Transformers are a promising deep learning approach for automated medical image segmentation. Objective: This scoping review will synthesize current literature and assess the use of various transformer models for neuroimaging segmentation. Methods: A systematic search in major databases, including Scopus, IEEE Xplore, PubMed, and ACM Digital Library, was carried out for studies applying transformers to neuroimaging segmentation problems from 2019 through 2023. The inclusion criteria allow only for peer-reviewed journal papers and conference papers focused on transformer-based segmentation of human brain imaging data. Excluded are the studies dealing with nonneuroimaging data or raw brain signals and electroencephalogram data. Data extraction was performed to identify key study details, including image modalities, datasets, neurological conditions, transformer models, and evaluation metrics. Results were synthesized using a narrative approach. Results: Of the 1246 publications identified, 67 (5.38\%) met the inclusion criteria. Half of all included studies were published in 2022, and more than two-thirds used transformers for segmenting brain tumors. The most common imaging modality was magnetic resonance imaging (n=59, 88.06\%), while the most frequently used dataset was brain tumor segmentation dataset (n=39, 58.21\%). 3D transformer models (n=42, 62.69\%) were more prevalent than their 2D counterparts. The most developed were those of hybrid convolutional neural network-transformer architectures (n=57, 85.07\%), where the vision transformer is the most frequently used type of transformer (n=37, 55.22\%). The most frequent evaluation metric was the Dice score (n=63, 94.03\%). Studies generally reported increased segmentation accuracy and the ability to model both local and global features in brain images. Conclusions: This review represents the recent increase in the adoption of transformers for neuroimaging segmentation, particularly for brain tumor detection. Currently, hybrid convolutional neural network-transformer architectures achieve state-of-the-art performances on benchmark datasets over standalone models. Nevertheless, their applicability remains highly limited by high computational costs and potential overfitting on small datasets. The heavy reliance of the field on the brain tumor segmentation dataset hints at the use of a more diverse set of datasets to validate the performances of models on a variety of neurological diseases. Further research is needed to define the optimal transformer architectures and training methods for clinical applications. Continuing development may make transformers the state-of-the-art for fast, accurate, and reliable brain magnetic resonance imaging segmentation, which could lead to improved clinical tools for diagnosing and evaluating neurological disorders. ",
doi="10.2196/57723",
url="https://www.jmir.org/2025/1/e57723"
}


@Article{info:doi/10.2196/51822,
author="Lefkovitz, Ilana
and Walsh, Samantha
and Blank, J. Leah
and Jett{\'e}, Nathalie
and Kummer, R. Benjamin",
title="Direct Clinical Applications of Natural Language Processing in Common Neurological Disorders: Scoping Review",
journal="JMIR Neurotech",
year="2024",
month="May",
day="22",
volume="3",
pages="e51822",
keywords="natural language processing",
keywords="NLP",
keywords="unstructured",
keywords="text",
keywords="machine learning",
keywords="deep learning",
keywords="neurology",
keywords="headache disorders",
keywords="migraine",
keywords="Parkinson disease",
keywords="cerebrovascular disease",
keywords="stroke",
keywords="transient ischemic attack",
keywords="epilepsy",
keywords="multiple sclerosis",
keywords="cardiovascular",
keywords="artificial intelligence",
keywords="Parkinson",
keywords="neurological",
keywords="neurological disorder",
keywords="scoping review",
keywords="diagnosis",
keywords="treatment",
keywords="prediction",
abstract="Background: Natural language processing (NLP), a branch of artificial intelligence that analyzes unstructured language, is being increasingly used in health care. However, the extent to which NLP has been formally studied in neurological disorders remains unclear. Objective: We sought to characterize studies that applied NLP to the diagnosis, prediction, or treatment of common neurological disorders. Methods: This review followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) standards. The search was conducted using MEDLINE and Embase on May 11, 2022. Studies of NLP use in migraine, Parkinson disease, Alzheimer disease, stroke and transient ischemic attack, epilepsy, or multiple sclerosis were included. We excluded conference abstracts, review papers, as well as studies involving heterogeneous clinical populations or indirect clinical uses of NLP. Study characteristics were extracted and analyzed using descriptive statistics. We did not aggregate measurements of performance in our review due to the high variability in study outcomes, which is the main limitation of the study. Results: In total, 916 studies were identified, of which 41 (4.5\%) met all eligibility criteria and were included in the final review. Of the 41 included studies, the most frequently represented disorders were stroke and transient ischemic attack (n=20, 49\%), followed by epilepsy (n=10, 24\%), Alzheimer disease (n=6, 15\%), and multiple sclerosis (n=5, 12\%). We found no studies of NLP use in migraine or Parkinson disease that met our eligibility criteria. The main objective of NLP was diagnosis (n=20, 49\%), followed by disease phenotyping (n=17, 41\%), prognostication (n=9, 22\%), and treatment (n=4, 10\%). In total, 18 (44\%) studies used only machine learning approaches, 6 (15\%) used only rule-based methods, and 17 (41\%) used both. Conclusions: We found that NLP was most commonly applied for diagnosis, implying a potential role for NLP in augmenting diagnostic accuracy in settings with limited access to neurological expertise. We also found several gaps in neurological NLP research, with few to no studies addressing certain disorders, which may suggest additional areas of inquiry. Trial Registration: Prospective Register of Systematic Reviews (PROSPERO) CRD42021228703; https://www.crd.york.ac.uk/PROSPERO/display\_record.php?RecordID=228703 ",
doi="10.2196/51822",
url="https://neuro.jmir.org/2024/1/e51822"
}


@Article{info:doi/10.2196/51245,
author="Chew, Ann Kimberly
and Ponsford, Jennie
and Gould, Rachel Kate",
title="Addressing Cyberscams and Acquired Brain Injury (``I Desperately Need to Know What to Do''): Qualitative Exploration of Clinicians' and Service Providers' Perspectives",
journal="J Med Internet Res",
year="2024",
month="Jan",
day="29",
volume="26",
pages="e51245",
keywords="cyberscam",
keywords="cyberscams",
keywords="fraud",
keywords="cybercrime",
keywords="cybersafety",
keywords="brain injury",
keywords="disability",
keywords="neurorehabilitation",
keywords="interventions",
keywords="treatment",
keywords="qualitative",
abstract="Background: People with acquired brain injury (ABI) may be more susceptible to scams owing to postinjury cognitive and psychosocial consequences. Cyberscams result in financial loss and debilitating psychological impacts such as shame and mistrust, interference with neurorehabilitation, and reduced independence. Despite these significant consequences, there are no psychological treatments to support cyberscam survivors. There is limited evidence regarding how the current workforce is addressing post-ABI cyberscams. Objective: This study aims to understand the perspectives and needs of clinicians and service providers in addressing post-ABI cyberscams. Methods: Overall, 20 multidisciplinary clinicians and service providers were recruited through purposive sampling across Australia. Semistructured interviews explored post-ABI scam experiences and vulnerabilities, treatments and their efficacy, and recommendations for future cybersafety recovery interventions. Reflexive thematic analysis was used. Results: In total, 8 themes encompassing a biopsychosocial understanding of scam vulnerabilities and impacts were identified: ``genuine lack of awareness: cognitive-executive difficulties''; ``not coping with the loss of it all''; ``needing trust and connection''; ``strong reactions of trusted others''; ``nothing structured to do''; ``financial stress and independence''; ``cyberability''; and ``scammer persuasion.'' Each theme informed clinical recommendations including the need to provide psychological and cognitive support, enhance financial and cybersafety skills, promote meaningful social engagement, and foster collaboration between families and clinical support teams. Conclusions: The multifaceted range of scam vulnerabilities and impacts highlighted the need for individualized, comprehensive, and targeted treatments using a biopsychosocial approach to enable cyberscam recovery among people with ABI. These findings will guide the development of a co-designed intervention. ",
doi="10.2196/51245",
url="https://www.jmir.org/2024/1/e51245",
url="http://www.ncbi.nlm.nih.gov/pubmed/38285489"
}


@Article{info:doi/10.2196/46427,
author="Jing, Yu
and Qin, Peinuan
and Fan, Xiangmin
and Qiang, Wei
and Wencheng, Zhu
and Sun, Wei
and Tian, Feng
and Wang, Dakuo",
title="Deep Learning--Assisted Gait Parameter Assessment for Neurodegenerative Diseases: Model Development and Validation",
journal="J Med Internet Res",
year="2023",
month="Jul",
day="5",
volume="25",
pages="e46427",
keywords="deep learning",
keywords="neurodegenerative disease",
keywords="auxiliary medical care",
keywords="gait parameter assessment",
abstract="Background: Neurodegenerative diseases (NDDs) are prevalent among older adults worldwide. Early diagnosis of NDD is challenging yet crucial. Gait status has been identified as an indicator of early-stage NDD changes and can play a significant role in diagnosis, treatment, and rehabilitation. Historically, gait assessment has relied on intricate but imprecise scales by trained professionals or required patients to wear additional equipment, causing discomfort. Advancements in artificial intelligence may completely transform this and offer a novel approach to gait evaluation. Objective: This study aimed to use cutting-edge machine learning techniques to offer patients a noninvasive, entirely contactless gait assessment and provide health care professionals with precise gait assessment results covering all common gait-related parameters to assist in diagnosis and rehabilitation planning. Methods: Data collection involved motion data from 41 different participants aged 25 to 85 (mean 57.51, SD 12.93) years captured in motion sequences using the Azure Kinect (Microsoft Corp; a 3D camera with a 30-Hz sampling frequency). Support vector machine (SVM) and bidirectional long short-term memory (Bi-LSTM) classifiers trained using spatiotemporal features extracted from raw data were used to identify gait types in each walking frame. Gait semantics could then be obtained from the frame labels, and all the gait parameters could be calculated accordingly. For optimal generalization performance of the model, the classifiers were trained using a 10-fold cross-validation strategy. The proposed algorithm was also compared with the previous best heuristic method. Qualitative and quantitative feedback from medical staff and patients in actual medical scenarios was extensively collected for usability analysis. Results: The evaluations comprised 3 aspects. Regarding the classification results from the 2 classifiers, Bi-LSTM achieved an average precision, recall, and F1-score of 90.54\%, 90.41\%, and 90.38\%, respectively, whereas these metrics were 86.99\%, 86.62\%, and 86.67\%, respectively, for SVM. Moreover, the Bi-LSTM--based method attained 93.2\% accuracy in gait segmentation evaluation (tolerance set to 2), whereas that of the SVM-based method achieved only 77.5\% accuracy. For the final gait parameter calculation result, the average error rate of the heuristic method, SVM, and Bi-LSTM was 20.91\% (SD 24.69\%), 5.85\% (SD 5.45\%), and 3.17\% (SD 2.75\%), respectively. Conclusions: This study demonstrated that the Bi-LSTM--based approach can effectively support accurate gait parameter assessment, assisting medical professionals in making early diagnoses and reasonable rehabilitation plans for patients with NDD. ",
doi="10.2196/46427",
url="https://www.jmir.org/2023/1/e46427",
url="http://www.ncbi.nlm.nih.gov/pubmed/37405831"
}


@Article{info:doi/10.2196/31106,
author="Cheah, Wen-Ting
and Hwang, Jwu-Jia
and Hong, Sheng-Yi
and Fu, Li-Chen
and Chang, Yu-Ling
and Chen, Ta-Fu
and Chen, I-An
and Chou, Chun-Chen",
title="A Digital Screening System for Alzheimer Disease Based on a Neuropsychological Test and a Convolutional Neural Network:  System Development and Validation",
journal="JMIR Med Inform",
year="2022",
month="Mar",
day="9",
volume="10",
number="3",
pages="e31106",
keywords="Alzheimer disease",
keywords="mild cognitive impairment",
keywords="screening system",
keywords="convolutional neural network",
keywords="Rey-Osterrieth Complex Figure",
abstract="Background: Alzheimer disease (AD) and other types of dementia are now considered one of the world's most pressing health problems for aging people worldwide. It was the seventh-leading cause of death, globally, in 2019. With a growing number of patients with dementia and increasing costs for treatment and care, early detection of the disease at the stage of mild cognitive impairment (MCI) will prevent the rapid progression of dementia. In addition to reducing the physical and psychological stress of patients' caregivers in the long term, it will also improve the everyday quality of life of patients. Objective: The aim of this study was to design a digital screening system to discriminate between patients with MCI and AD and healthy controls (HCs), based on the Rey-Osterrieth Complex Figure (ROCF) neuropsychological test. Methods: The study took place at National Taiwan University between 2018 and 2019. In order to develop the system, pretraining was performed using, and features were extracted from, an open sketch data set using a data-driven deep learning approach through a convolutional neural network. Later, the learned features were transferred to our collected data set to further train the classifier. The first data set was collected using pen and paper for the traditional method. The second data set used a tablet and smart pen for data collection. The system's performance was then evaluated using the data sets. Results: The performance of the designed system when using the data set that was collected using the traditional pen and paper method resulted in a mean area under the receiver operating characteristic curve (AUROC) of 0.913 (SD 0.004) when distinguishing between patients with MCI and HCs. On the other hand, when discriminating between patients with AD and HCs,  the mean AUROC was 0.950 (SD 0.003) when using the data set that was collected using the digitalized method. Conclusions: The automatic ROCF test scoring system that we designed showed satisfying results for differentiating between patients with AD and MCI and HCs. Comparatively, our proposed network architecture provided better performance than our previous work, which did not include data augmentation and dropout techniques. In addition, it also performed better than other existing network architectures, such as AlexNet and Sketch-a-Net, with transfer learning techniques. The proposed system can be incorporated with other tests to assist clinicians in the early diagnosis of AD and to reduce the physical and mental burden on patients' family and friends. ",
doi="10.2196/31106",
url="https://medinform.jmir.org/2022/3/e31106",
url="http://www.ncbi.nlm.nih.gov/pubmed/35262497"
}


@Article{info:doi/10.2196/25913,
author="Verdonck, Micha{\"e}l
and Carvalho, Hugo
and Berghmans, Johan
and Forget, Patrice
and Poelaert, Jan",
title="Exploratory Outlier Detection for Acceleromyographic Neuromuscular Monitoring: Machine Learning Approach",
journal="J Med Internet Res",
year="2021",
month="Jun",
day="21",
volume="23",
number="6",
pages="e25913",
keywords="neuromuscular monitoring",
keywords="outlier analysis",
keywords="acceleromyography",
keywords="postoperative residual curarization",
keywords="train-of-four",
keywords="monitoring devices",
keywords="neuromuscular",
keywords="machine learning",
keywords="monitors",
keywords="anesthesiology",
abstract="Background: Perioperative quantitative monitoring of neuromuscular function in patients receiving neuromuscular blockers hasbecome internationally recognized as an absolute and core necessity in modern anesthesia care. Because of their kinetic nature, artifactual recordings of acceleromyography-based neuromuscular monitoring devices are not unusual. These generate a great deal of cynicism among anesthesiologists, constituting an obstacle toward their widespread adoption. Through outlier analysis techniques, monitoring devices can learn to detect and flag signal abnormalities. Outlier analysis (or anomaly detection) refers to the problem of finding patterns in data that do not conform to expected behavior. Objective: This study was motivated by the development of a smartphone app intended for neuromuscular monitoring based on combined accelerometric and angular hand movement data. During the paired comparison stage of this app against existing acceleromyography monitoring devices, it was noted that the results from both devices did not always concur.  This study aims to engineer a set of features that enable the detection of outliers in the form of erroneous train-of-four (TOF) measurements from an acceleromyographic-based device. These features are tested for their potential in the detection of erroneous TOF measurements by developing an outlier detection algorithm. Methods: A data set encompassing 533 high-sensitivity TOF measurements from 35 patients was created based on a multicentric open label trial of a purpose-built accelero- and gyroscopic-based neuromuscular monitoring app. A basic set of features was extracted based on raw data while a second set of features was purpose engineered based on TOF pattern characteristics. Two cost-sensitive logistic regression (CSLR) models were deployed to evaluate the performance of these features. The final output of the developed models was a binary classification, indicating if a TOF measurement was an outlier or not. Results: A total of 7 basic features were extracted based on raw data, while another 8 features were engineered based on TOF pattern characteristics. The model training and testing were based on separate data sets: one with 319 measurements (18 outliers) and a second with 214 measurements (12 outliers). The F1 score (95\% CI) was 0.86 (0.48-0.97) for the CSLR model with engineered features, significantly larger than the CSLR model with the basic features (0.29 [0.17-0.53]; P<.001). Conclusions: The set of engineered features and their corresponding incorporation in an outlier detection algorithm have the potential to increase overall neuromuscular monitoring data consistency. Integrating outlier flagging algorithms within neuromuscular monitors could potentially reduce overall acceleromyography-based reliability issues. Trial Registration: ClinicalTrials.gov NCT03605225; https://clinicaltrials.gov/ct2/show/NCT03605225 ",
doi="10.2196/25913",
url="https://www.jmir.org/2021/6/e25913/",
url="http://www.ncbi.nlm.nih.gov/pubmed/34152273"
}