Multimodal MR Images-Based Diagnosis of Early Adolescent Attention-Deficit/Hyperactivity Disorder Using Multiple Kernel Learning

Zhou, Xiaocheng; Lin, Qingmin; Gui, Yuanyuan; Wang, Zixin; Liu, Manhua; Lü, Hui

doi:10.3389/fnins.2021.710133

Cited by 27 publications

(53 citation statements)

References 80 publications

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“…Recent findings that indicate the benefit of radiomic features in neonatal respiratory disease ( 115 ), Alzheimer’s and Parkinson’s disease ( 116 ), and attention-deficit/hyperactivity disorder ( 73 ) suggest that radiomics is also effective in non-oncology cases.…”

Section: Discussionmentioning

confidence: 99%

“…Supervised learning models are analyzed in conjunction with outcome variables to establish a mathematical representation between the selected characteristics and the target variables, a widely utilized method in radiomic analysis. Support vector machine (SVM) is a commonly employed promising discriminative classification technique and is a typical practice to introduce multiple classification models for profiling to achieve better performance (24,(71)(72)(73). For instance, Kim et al (71) showed that SVM, logistic regression (LR), bagging tree, boosting tree, and dual-channel bidirectional long and short-term memory network performed well for prostate cancer identification on tissue images.…”

Section: Model Construction and Classification/ Predictivementioning

confidence: 99%

“…Shiri et al (66) performed a radiomic analysis of CT, ceCT, and PET images of NSCLC targeting EGFR and Kirsten rat sarcoma virus mutations with 6 feature selection methods and 12 different classifiers for the combination with genomic data and revealed that the stochastic gradient descent model outperformed the best among the 12 methods. Besides tumor-related applications, Zhou et al (73) employed multimodality signatures of children's multiparametric MR images and an SVM classifier to differentiate attention-deficit/hyperactivity disorder from normal children with diagnostic AUC and accuracy of 69.8% and64.3%, respectively, a significant improvement over earlier feature fusion and single-modality approaches. These studies demonstrate that multimodality fusion features possess rich and complementary information that allows robust and highly accurate tumor characterization.…”

Section: Multimodality Analysismentioning

confidence: 99%

“…Besides tumor-related applications, Zhou et al. ( 73 ) employed multimodality signatures of children’s multiparametric MR images and an SVM classifier to differentiate attention-deficit/hyperactivity disorder from normal children with diagnostic AUC and accuracy of 69.8% and64.3%, respectively, a significant improvement over earlier feature fusion and single-modality approaches. These studies demonstrate that multimodality fusion features possess rich and complementary information that allows robust and highly accurate tumor characterization.…”

Section: Some Case Studies and Applicationsmentioning

confidence: 99%

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Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential

Zhang

et al. 2022

Front. Oncol.

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The high-throughput extraction of quantitative imaging features from medical images for the purpose of radiomic analysis, i.e., radiomics in a broad sense, is a rapidly developing and emerging research field that has been attracting increasing interest, particularly in multimodality and multi-omics studies. In this context, the quantitative analysis of multidimensional data plays an essential role in assessing the spatio-temporal characteristics of different tissues and organs and their microenvironment. Herein, recent developments in this method, including manually defined features, data acquisition and preprocessing, lesion segmentation, feature extraction, feature selection and dimension reduction, statistical analysis, and model construction, are reviewed. In addition, deep learning-based techniques for automatic segmentation and radiomic analysis are being analyzed to address limitations such as rigorous workflow, manual/semi-automatic lesion annotation, and inadequate feature criteria, and multicenter validation. Furthermore, a summary of the current state-of-the-art applications of this technology in disease diagnosis, treatment response, and prognosis prediction from the perspective of radiology images, multimodality images, histopathology images, and three-dimensional dose distribution data, particularly in oncology, is presented. The potential and value of radiomics in diagnostic and therapeutic strategies are also further analyzed, and for the first time, the advances and challenges associated with dosiomics in radiotherapy are summarized, highlighting the latest progress in radiomics. Finally, a robust framework for radiomic analysis is presented and challenges and recommendations for future development are discussed, including but not limited to the factors that affect model stability (medical big data and multitype data and expert knowledge in medical), limitations of data-driven processes (reproducibility and interpretability of studies, different treatment alternatives for various institutions, and prospective researches and clinical trials), and thoughts on future directions (the capability to achieve clinical applications and open platform for radiomics analysis).

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Section: Discussionmentioning

confidence: 99%

Section: Model Construction and Classification/ Predictivementioning

confidence: 99%

Section: Multimodality Analysismentioning

confidence: 99%

Section: Some Case Studies and Applicationsmentioning

confidence: 99%

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Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential

Zhang

et al. 2022

Front. Oncol.

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“…MKL-SVM has been successfully applied to integrate multimodal structural neuroimaging for predicting differential diagnosis between bipolar and unipolar depression [286] and to combine sMRI and fMRI for improved classification of trauma survivors with and without PTSD [287]. More recently, it also showed efficacy in the diagnosis of early adolescent ADHD by integrating sMRI, fMRI, and DTI [288]. In learning low dimensional representations of functional and structural MRI [289], the functional MRI can be split into several independent component networks, each treated as a separate modality along with the structural scan for learning using autoencoders.…”

Section: Multimodal Neuroimaging Studiesmentioning

confidence: 99%

Modern Views of Machine Learning for Precision Psychiatry

Chen¹,

Prathamesh²,

Kulkarni³

et al. 2022

Preprint

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In light of the NIMH’s Research Domain Criteria (RDoC), the advent of functional neuroimaging, novel technologies and methods provide new opportunities to develop precise and personalized prognosis and diagnosis of mental disorders. Machine learning (ML) and artificial intelligence (AI) technologies are playing an increasingly critical role in the new era of precision psychiatry. Combining ML/AI with neuromodulation technologies can potentially provide explainable solutions in clinical practice and effective therapeutic treatment. Advanced wearable and mobile technologies also call for the new role of ML/AI for digital phenotyping in mobile mental health. In this review, we provide a comprehensive review of the ML methodologies and applications by combining neuroimaging, neuromodulation and advanced mobile technologies in psychiatry practice. Additionally, we review the role of ML in molecular phenotyping, cross-species biomarker identification in precision psychiatry. We further discuss explainable AI (XAI) and causality testing in a closed-human-in-the-loop manner, and highlight the ML potential in multimedia information extraction and multimodal data fusion. Finally, we discuss conceptual and practical challenges in precision psychiatry and highlight ML opportunities in future research.

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Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis

et al. 2023

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ImportanceNeuroimaging-based artificial intelligence (AI) diagnostic models have proliferated in psychiatry. However, their clinical applicability and reporting quality (ie, feasibility) for clinical practice have not been systematically evaluated.ObjectiveTo systematically assess the risk of bias (ROB) and reporting quality of neuroimaging-based AI models for psychiatric diagnosis.Evidence ReviewPubMed was searched for peer-reviewed, full-length articles published between January 1, 1990, and March 16, 2022. Studies aimed at developing or validating neuroimaging-based AI models for clinical diagnosis of psychiatric disorders were included. Reference lists were further searched for suitable original studies. Data extraction followed the CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. A closed-loop cross-sequential design was used for quality control. The PROBAST (Prediction Model Risk of Bias Assessment Tool) and modified CLEAR (Checklist for Evaluation of Image-Based Artificial Intelligence Reports) benchmarks were used to systematically evaluate ROB and reporting quality.FindingsA total of 517 studies presenting 555 AI models were included and evaluated. Of these models, 461 (83.1%; 95% CI, 80.0%-86.2%) were rated as having a high overall ROB based on the PROBAST. The ROB was particular high in the analysis domain, including inadequate sample size (398 of 555 models [71.7%; 95% CI, 68.0%-75.6%]), poor model performance examination (with 100% of models lacking calibration examination), and lack of handling data complexity (550 of 555 models [99.1%; 95% CI, 98.3%-99.9%]). None of the AI models was perceived to be applicable to clinical practices. Overall reporting completeness (ie, number of reported items/number of total items) for the AI models was 61.2% (95% CI, 60.6%-61.8%), and the completeness was poorest for the technical assessment domain with 39.9% (95% CI, 38.8%-41.1%).Conclusions and RelevanceThis systematic review found that the clinical applicability and feasibility of neuroimaging-based AI models for psychiatric diagnosis were challenged by a high ROB and poor reporting quality. Particularly in the analysis domain, ROB in AI diagnostic models should be addressed before clinical application.

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Multimodal MR Images-Based Diagnosis of Early Adolescent Attention-Deficit/Hyperactivity Disorder Using Multiple Kernel Learning

Cited by 27 publications

References 80 publications

Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential

Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential

Modern Views of Machine Learning for Precision Psychiatry

Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis

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