Figure 4 - uploaded by Claudia Chien
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(A) Mean intensity values from cropped MRIs of the lateral ventricle regions between low and high disease activity patient groups at baseline MRI. (B) Image of the highest mean intensity cropped raw FLAIR (from the high disease activity group), showing low-intensity values in the lateral ventricle CSF and surrounding white and grey matter, however, the choroid plexus has high intensities. The colour bar in part (B) indicates the intensity measures in the raw cropped-FLAIR image. CSF: cerebrospinal fluid; CP: choroid plexus.
Source publication
Background
Lack of easy-to-interpret disease activity prediction methods in early MS can lead to worse patient prognosis.
Objectives
Using machine learning (multiple kernel learning – MKL) models, we assessed the prognostic value of various clinical and MRI measures for disease activity.
Methods
Early MS patients ( n = 148) with at least two asso...
Contexts in source publication
Context 1
... it can be seen that in our cohort 50% of patients risk failing NEDA-3 Criteria around six months after baseline. We investigated further if the baseline mean intensities from the cropped MRIs around the LV showed differences between low and high disease activity patient groups using a Welch's two-sample t-test (Figure 4). Figure 4B shows the hyperintense CP of a high disease activity patient with the highest mean image intensity shown in Figure 4A. ...
Context 2
... investigated further if the baseline mean intensities from the cropped MRIs around the LV showed differences between low and high disease activity patient groups using a Welch's two-sample t-test (Figure 4). Figure 4B shows the hyperintense CP of a high disease activity patient with the highest mean image intensity shown in Figure 4A. Although N4-bias correction (14) of the FLAIR MRIs was performed during the MRI preprocessing stage, there are higher intensities from this LV region in patients with high disease activity (t = −64.1, ...
Context 3
... it can be seen that in our cohort 50% of patients risk failing NEDA-3 Criteria around six months after baseline. We investigated further if the baseline mean intensities from the cropped MRIs around the LV showed differences between low and high disease activity patient groups using a Welch's two-sample t-test (Figure 4). Figure 4B shows the hyperintense CP of a high disease activity patient with the highest mean image intensity shown in Figure 4A. Although N4-bias correction (14) of the FLAIR MRIs was performed during the MRI preprocessing stage, there are higher intensities from this LV region in patients with high disease activity (t = −64.1, ...
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Citations
... Data were retrospectively collected from the early MS cohort of Berlin, Germany (32 people diagnosed with clinically isolated syndrome (CIS) and 93 pwMS) [25,26] and people with clinically definite MS from the Amsterdam MS cohort, the Netherlands (330 pwMS) [27,28]. All included subjects were over the age of 18 and had a clinical assessment and a structural MRI examination available at baseline. ...
... itksn ap. org) by two expert MRI technicians [26]. In Amsterdam, lesions were automatically segmented using a k-nearest neighbor algorithm and visually checked [28][29][30]. ...
Background
Robust predictive models of clinical impairment and worsening in multiple sclerosis (MS) are needed to identify patients at risk and optimize treatment strategies.
Objective
To evaluate whether machine learning (ML) methods can classify clinical impairment and predict worsening in people with MS (pwMS) and, if so, which combination of clinical and magnetic resonance imaging (MRI) features and ML algorithm is optimal.
Methods
We used baseline clinical and structural MRI data from two MS cohorts (Berlin: n = 125, Amsterdam: n = 330) to evaluate the capability of five ML models in classifying clinical impairment at baseline and predicting future clinical worsening over a follow-up of 2 and 5 years. Clinical worsening was defined by increases in the Expanded Disability Status Scale (EDSS), Timed 25-Foot Walk Test (T25FW), 9-Hole Peg Test (9HPT), or Symbol Digit Modalities Test (SDMT). Different combinations of clinical and volumetric MRI measures were systematically assessed in predicting clinical outcomes. ML models were evaluated using Monte Carlo cross-validation, area under the curve (AUC), and permutation testing to assess significance.
Results
The ML models significantly determined clinical impairment at baseline for the Amsterdam cohort, but did not reach significance for predicting clinical worsening over a follow-up of 2 and 5 years. High disability (EDSS ≥ 4) was best determined by a support vector machine (SVM) classifier using clinical and global MRI volumes (AUC = 0.83 ± 0.07, p = 0.015). Impaired cognition (SDMT Z-score ≤ −1.5) was best determined by a SVM using regional MRI volumes (thalamus, ventricles, lesions, and hippocampus), reaching an AUC of 0.73 ± 0.04 (p = 0.008).
Conclusion
ML models could aid in classifying pwMS with clinical impairment and identify relevant biomarkers, but prediction of clinical worsening is an unmet need.
... In their study, Chien, Seiler, Eitel, Schmitz-Hübsch, Paul, and Ritter [10] have suggested MKL (Multiple Kernel Learning) framework as a diagnostic tool for Alzheimer's disease (AD), which involves the integration of diverse data sources. The authors propose a methodology for generating multimodal indicators of Alzheimer's disease (AD) by utilizing diffusion tensor imaging (DTI) to extract biomarkers from adjacent images. ...
This article aims to provide a concise overview of diverse methodologies employed at different stages of a pattern recognition system, highlighting contemporary research challenges and applications in this dynamic field. The integration of machine learning techniques has played a pivotal role in converging pattern recognition frameworks in academic literature. The process relies heavily on supervised or unsupervised categorization methods to achieve its objectives, with a notable focus on statistical approaches. More recently, there is a growing emphasis on incorporating neural network methodologies and insights from statistical learning theory. Designing an effective recognition system necessitates careful consideration of various factors, including pattern representation, pattern class definition, feature extraction, sensing environment, feature selection, classifier learning and design, cluster analysis, test and training sample selection, and performance assessment.
... 14 However, their analysis included features commonly not available within de-identified structured EHR datasets, such as the EDSS and functional sub-scores. Other work focused on predicting vision impairment using genetic markers 15,16 and imaging data.17,18 EHRs are widely available, and the rapid development of predictive models is facilitated by the use of structured data collected and readily available within the EHR. ...
Objectives
Multiple sclerosis (MS) is a complex autoimmune neurological disorder that frequently impacts vision. One of the most frequent initial presentations of MS is acute vision loss due to optic neuritis, an acute disorder caused by MS involvement with the optic nerve. While vision impairment is often the first sign of MS, it can occur or recur at any time during the patient’s course. In this study, we aim to develop and evaluate machine learning models to predict vision impairment in patients with MS, both at the time of first MS diagnosis and throughout their course of care. Early awareness and intervention in patients likely to have vision loss can help preserve patient quality of life.
Materials and Methods
Using the Epic Cosmos de-identified electronic health record (EHR) dataset, we queried 213+ million patients to extract our MS cohort. Cases were defined as MS patients with vision impairment or optic neuritis (VI) following their first MS diagnosis, while controls were MS patients without VI. We trained logistic regression (LR), light gradient boosting machine (LGBM), and recurrent neural network (RNN) models to predict future VI in MS patients. The models were evaluated for two distinct clinical tasks: prediction of VI at the time of the first MS diagnosis and prediction of VI at the most recent visit. Similarly, we trained the models on different segments of the patient trajectory including up until the first MS diagnosis (MS-First Diagnosis), or until the most recent visit before developing the outcome (MS-Progress) as well as the combination of both (MS-General). Finally, we trained a survival model with the goal of predicting patient likelihood of vision loss over time. We compared the models’ performance using AUROC, AUPRC, and Brier scores.
Results
We extracted a cohort of 377,097 patients with MS, including 42,281 VI cases. Our trained models achieved ∼80% AUROC, with RNN-based models outperforming LGBM and LR (79.6% vs 72.8% and 68.6%, respectively) when considering the full patient trajectory. The MS-General RNN model had the highest AUROC (64.4%) for predicting VI at the first MS diagnosis. The MS-Progress survival model achieved a 75% concordance index on the full trajectory, while the more clinically relevant MS-First Diagnosis model achieved 63.1% at initial diagnosis.
Discussion and Conclusion
The MS-Progress and MS-General RNN models performed best in both prediction scenarios. While MS-General achieved the best performance at the time of first MS diagnosis with around 1% AUROC increase compared to the MS-First Diagnosis model, it showed around 1% AUROC decrease on the MS progress scenario. All RNN survival models performed the best when they were trained on data corresponding to the evaluation use-case scenarios. RNN based models showed promising performance that demonstrates that they can be useful clinical tools to predict risk of future VI events in patients with MS. Further development of these models will focus on expanding to predict other comorbidities associated with MS relapse or progression.
... Anirban et al. [21] proposed a multiple kernel learning embedded multi-objective swarm intelligence technique to identify the candidate biomarker genes from the transcriptomic profile of arsenicosis samples. Using multiple kernel learning MKL models, Chien et al. [22] assessed the predictive value of various clinical and MRI measures for disease activity. Jiang et al. [23] proposed a high-order norm-product regularized multiple kernel learning framework to optimize the discrimination performance. ...
Multi-kernel learning methods are essential kernel learning methods. Still, the base kernel functions in most multi-kernel learning methods only with select kernel functions with shallow structures, which are weak for large-scale uneven data. We propose two types of acceleration models from a multidimensional perspective of the data: the neural tangent kernel (NTK)-based multi-kernel learning method is proposed, where the NTK kernel regressor is shown to be equivalent to an infinitely wide neural network predictor, and the NTK with deep structure is used as the base kernel function to enhance the learning ability of multi-kernel models; and a parallel computing kernel model based on data partitioning techniques. An RBF, POLY-based multi-kernel model is also proposed. All models use historical memory-based PSO (HMPSO) for efficient search of parameters within the model. Since NTK has a multi-layer structure and thus has a significant computational complexity, the use of a Monotone Disjunctive Kernel (MDK) to store and train Boolean features in binary achieves a 15–60% training time compression of NTK models in different datasets while obtaining a 1–25% accuracy improvement.
Magnetic resonance imaging (MRI) of the brain is commonly used to detect where chronic and active lesions are in multiple sclerosis (MS). MRI is also extensively used as a tool to calculate and extrapolate brain health by way of volumetric analysis or advanced imaging techniques. In MS patients, psychiatric symptoms are common comorbidities, with depression being the main one. Even though these symptoms are a major determinant of quality of life in MS, they are often overlooked and undertreated. There has been evidence of bidirectional interactions between the course of MS and comorbid psychiatric symptoms. In order to mitigate disability progression in MS, treating psychiatric comorbidities should be investigated and optimized. New research for the prediction of disease states or phenotypes of disability have advanced, primarily due to new technologies and a better understanding of the aging brain.
