icobrain ms 5.1: Combining unsupervised and supervised approaches for improving the detection of multiple sclerosis lesions

Rakić, Mladen; Vercruyssen, Sophie; Eyndhoven, Simon Van; Rosa, Ezequiel de la; Jain, Saurabh; Huffel, Sabine Van; Maes, Frederik; Smeets, Dirk; Sima, Diana

doi:10.1016/j.nicl.2021.102707

Cited by 26 publications

(17 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Rakíc et al [ 105 ] aimed to develop an approach where two pipelines are utilized to classify MS lesions using MRI scans. This combined approach consisted of an unsupervised ML technique and a DL attention-gate 3D U-net network.…”

Section: Related Studiesmentioning

confidence: 99%

Multiple Sclerosis Diagnosis Using Machine Learning and Deep Learning: Challenges and Opportunities

Aslam

Khan

Bashamakh

et al. 2022

Sensors

View full text Add to dashboard Cite

Multiple Sclerosis (MS) is a disease that impacts the central nervous system (CNS), which can lead to brain, spinal cord, and optic nerve problems. A total of 2.8 million are estimated to suffer from MS. Globally, a new case of MS is reported every five minutes. In this review, we discuss the proposed approaches to diagnosing MS using machine learning (ML) published between 2011 and 2022. Numerous models have been developed using different types of data, including magnetic resonance imaging (MRI) and clinical data. We identified the methods that achieved the best results in diagnosing MS. The most implemented approaches are SVM, RF, and CNN. Moreover, we discussed the challenges and opportunities in MS diagnosis to improve AI systems to enable researchers and practitioners to enhance their approaches and improve the automated diagnosis of MS. The challenges faced by automated MS diagnosis include difficulty distinguishing the disease from other diseases showing similar symptoms, protecting the confidentiality of the patients’ data, achieving reliable ML models that are also easily understood by non-experts, and the difficulty of collecting a large reliable dataset. Moreover, we discussed several opportunities in the field such as the implementation of secure platforms, employing better AI solutions, developing better disease prognosis systems, combining more than one data type for better MS prediction and using OCT data for diagnosis, utilizing larger, multi-center datasets to improve the reliability of the developed models, and commercialization.

show abstract

Section: Related Studiesmentioning

confidence: 99%

Multiple Sclerosis Diagnosis Using Machine Learning and Deep Learning: Challenges and Opportunities

Aslam

Khan

Bashamakh

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…(1) An automated method for white matter lesion segmentation that uses 3D T1-weighted and FLAIR MR images in a probabilistic model. The accuracy and reproducibility of this software has been shown to be comparable to other well-established MS lesion segmentation algorithms (e.g., Lesion-TOADS, Lesion Segmentation Tool [ 17 , 18 ]); (2) An automated multi-atlas cortical and subcortical segmentation method, which showed similar accuracy and reproducibility to commonly used automatic segmentation tools [ 21 , 22 ]. Icobrain ms allowed extraction of normalized volumes for 34 anatomical regions, including cerebral gray matter regions ( n = 20), deep gray matter regions ( n = 7), brainstem ( n = 3), cerebellum ( n = 2), lateral ventricles ( n = 1), and cerebral white matter ( n = 1).…”

Section: Methodsmentioning

confidence: 85%

Neural correlates of digital measures shown by structural MRI: a post-hoc analysis of a smartphone-based remote assessment feasibility study in multiple sclerosis

et al. 2022

View full text Add to dashboard Cite

Background A study was undertaken to evaluate remote monitoring via smartphone sensor-based tests in people with multiple sclerosis (PwMS). This analysis aimed to explore regional neural correlates of digital measures derived from these tests. Methods In a 24-week, non-randomized, interventional, feasibility study (NCT02952911), sensor-based tests on the Floodlight Proof-of-Concept app were used to assess cognition (smartphone-based electronic Symbol Digit Modalities Test), upper extremity function (Draw a Shape Test, Pinching Test), and gait and balance (Static Balance Test, Two-Minute Walk Test, U-Turn Test). In this post-hoc analysis, digital measures and standard clinical measures (e.g., Nine-Hole Peg Test [9HPT]) were correlated against regional structural magnetic resonance imaging outcomes. Seventy-six PwMS aged 18–55 years with an Expanded Disability Status Scale score of 0.0–5.5 were enrolled from two different sites (USA and Spain). Sixty-two PwMS were included in this analysis. Results Worse performance on digital and clinical measures was associated with smaller regional brain volumes and larger ventricular volumes. Whereas digital and clinical measures had many neural correlates in common (e.g., putamen, globus pallidus, caudate nucleus, lateral occipital cortex), some were observed only for digital measures. For example, Draw a Shape Test and Pinching Test measures, but not 9HPT score, correlated with volume of the hippocampus (r = 0.37 [drawing accuracy over time on the Draw a Shape Test]/ − 0.45 [touching asynchrony on the Pinching Test]), thalamus (r = 0.38/ − 0.41), and pons (r = 0.35/ − 0.35). Conclusions Multiple neural correlates were identified for the digital measures in a cohort of people with early MS. Digital measures showed associations with brain regions that clinical measures were unable to demonstrate, thus providing potential novel information on functional ability compared with standard clinical assessments.

show abstract

“…Technical: icobrain ms has been tested for robustness to different input data [26,31,33,[72][73][74], reproducibility [26,31,72,74], repeatability [73,74], and consistency over time [19,73]. The tool has been compared to manual segmentation [19,21,26,31,74,75], LesionQuant [28], and automated established brain and lesion segmentation and atrophy quantification methods, such as SIENA(X) [28, 72-74, 76, 77], LST [26,31], Lesion-TOADS [26], FreeSurfer [77], and SPM [74,77] and has been included in a longitudinal MS lesion segmentation challenge [21]. Earlier or other versions of the tool have been compared to the current version [31,75].…”

Section: Icometrixmentioning

confidence: 99%

“…The tool has been compared to manual segmentation [19,21,26,31,74,75], LesionQuant [28], and automated established brain and lesion segmentation and atrophy quantification methods, such as SIENA(X) [28, 72-74, 76, 77], LST [26,31], Lesion-TOADS [26], FreeSurfer [77], and SPM [74,77] and has been included in a longitudinal MS lesion segmentation challenge [21]. Earlier or other versions of the tool have been compared to the current version [31,75]. An automated method to decrease the effect of interscanner variability on results has been tested [78,79].…”

Section: Icometrixmentioning

confidence: 99%

Commercial volumetric MRI reporting tools in multiple sclerosis: a systematic review of the evidence

et al. 2022

View full text Add to dashboard Cite

Purpose MRI is integral to the diagnosis of multiple sclerosis (MS) and is important for clinical prognostication. Quantitative volumetric reporting tools (QReports) can improve the accuracy and objectivity of MRI-based assessments. Several QReports are commercially available; however, validation can be difficult to establish and does not currently follow a common pathway. To aid evidence-based clinical decision-making, we performed a systematic review of commercial QReports for use in MS including technical details and published reports of validation and in-use evaluation. Methods We categorized studies into three types of testing: technical validation, for example, comparison to manual segmentation, clinical validation by clinicians or interpretation of results alongside clinician-rated variables, and in-use evaluation, such as health economic assessment. Results We identified 10 companies, which provide MS lesion and brain segmentation and volume quantification, and 38 relevant publications. Tools received regulatory approval between 2006 and 2020, contextualize results to normative reference populations, ranging from 620 to 8000 subjects, and require T1- and T2-FLAIR-weighted input sequences for longitudinal assessment of whole-brain volume and lesions. In MS, six QReports provided evidence of technical validation, four companies have conducted clinical validation by correlating results with clinical variables, only one has tested their QReport by clinician end-users, and one has performed a simulated in-use socioeconomic evaluation. Conclusion We conclude that there is limited evidence in the literature regarding clinical validation and in-use evaluation of commercial MS QReports with a particular lack of clinician end-user testing. Our systematic review provides clinicians and institutions with the available evidence when considering adopting a quantitative reporting tool for MS.

show abstract

icobrain ms 5.1: Combining unsupervised and supervised approaches for improving the detection of multiple sclerosis lesions

Cited by 26 publications

References 38 publications

Multiple Sclerosis Diagnosis Using Machine Learning and Deep Learning: Challenges and Opportunities

Multiple Sclerosis Diagnosis Using Machine Learning and Deep Learning: Challenges and Opportunities

Neural correlates of digital measures shown by structural MRI: a post-hoc analysis of a smartphone-based remote assessment feasibility study in multiple sclerosis

Commercial volumetric MRI reporting tools in multiple sclerosis: a systematic review of the evidence

Contact Info

Product

Resources

About