Diagnosing autism spectrum disorder in children using conventional MRI and apparent diffusion coefficient based deep learning algorithms

Guo, Xiang; Wang, Jiehuan; Wang, Xiaoqiang; Liu, Wenjing; Yu, Hao; Liu, Xü; Li, Hengyan; Wu, Jiangfen; Dong, Mengxing; Tan, Wenming; Chen, Weijian; Yang, Yunjun; Chen, Yue‐Qin

doi:10.1007/s00330-021-08239-4

Cited by 13 publications

(12 citation statements)

References 42 publications

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“…One-hundred fifty of 555 models (27.0%; 95% CI, 23.3%-30.7%) were rated with poor reporting quality in the applications domain across 2 signaling questions. 26 , 28 , 29 , 32 , 36 , 38 , 41 , 45 , 50 , 51 , 60 , 65 , 69 , 74 , 78 , 82 , 84 , 94 , 97 , 99 , 105 , 107 , 108 , 112 , 117 , 119 , 120 , 125 , 132 , 133 , 134 , 138 , 139 , 141 , 143 , 146 , 150 , 159 , 172 , 183 , 186 , 190 , 193 , 194 , 200 , 203 , 204 , 214 , 219 , 220 , 222 , 223 , 228 , 229 , 230 , 232 , 233 , 241 ,…”

Section: Resultsmentioning

confidence: 99%

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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Section: Resultsmentioning

confidence: 99%

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

et al. 2023

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“…According to Irimia et al (2018) , those with ASD exhibited larger areas and volumes in some limbic structures such as the cingulate gyrus and the pericallosal sulcus. The temporal lobe, corpus callosum, middle cerebellar peduncle, caudate, and cingulate nucleus were the most relevant regions identified for predicting ASD in Guo et al (2021) .…”

Section: Highlighted Researchmentioning

confidence: 99%

“…The 2D/3D CNN and RAM fared the best overall. Using both MRI (containing sagittal T1/T2 sequences, FLAIR, and T1/T2 axial sequences) and apparent diffusion coefficient (ADC) approaches, Guo et al (2021) created a set of DL algorithms. The algorithms were trained using the ResNet-18 model, which had a “spatial channel” block to enhance feature identification.…”

Section: Highlighted Researchmentioning

confidence: 99%

“…Furthermore, ABIDE offers a unique opportunity to analyze a large sample of females with ASD, as this was not possible with other datasets and is partly due to differential prevalence rates between males and females ( Richards et al, 2020 ). The ABIDE site displays scan techniques, settings, and participants’ inclusion and exclusion criteria for each site ( Guo et al, 2021 ).…”

Section: Highlighted Researchmentioning

confidence: 99%

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Machine learning for autism spectrum disorder diagnosis using structural magnetic resonance imaging: Promising but challenging

Bahathiq

Banjar

Bamaga

et al. 2022

Front. Neuroinform.

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Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that affects approximately 1% of the population and causes significant burdens. ASD’s pathogenesis remains elusive; hence, diagnosis is based on a constellation of behaviors. Structural magnetic resonance imaging (sMRI) studies have shown several abnormalities in volumetric and geometric features of the autistic brain. However, inconsistent findings prevented most contributions from being translated into clinical practice. Establishing reliable biomarkers for ASD using sMRI is crucial for the correct diagnosis and treatment. In recent years, machine learning (ML) and specifically deep learning (DL) have quickly extended to almost every sector, notably in disease diagnosis. Thus, this has led to a shift and improvement in ASD diagnostic methods, fulfilling most clinical diagnostic requirements. However, ASD discovery remains difficult. This review examines the ML-based ASD diagnosis literature over the past 5 years. A literature-based taxonomy of the research landscape has been mapped, and the major aspects of this topic have been covered. First, we provide an overview of ML’s general classification pipeline and the features of sMRI. Next, representative studies are highlighted and discussed in detail with respect to methods, and biomarkers. Finally, we highlight many common challenges and make recommendations for future directions. In short, the limited sample size was the main obstacle; Thus, comprehensive data sets and rigorous methods are necessary to check the generalizability of the results. ML technologies are expected to advance significantly in the coming years, contributing to the diagnosis of ASD and helping clinicians soon.

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“…Eine im Februar 2022 publizierte retrospektive Fall-Kontrollstudie einer chinesischen Arbeitsgruppe 42 erfolgte an 151 Patienten mit ASD und 152 gesunden altersparallelisierten Kontrollen im Alter von etwa 2,5 Jahren (Daten von Januar 2016 bis Dezember 2019 erhoben) sowie einer zusätzlichen unabhängigen Validierungsstichprobe von 20 Patienten mit ASD und 25 gesunden Kontrollen (Januar bis Juni 2020). Die Autoren nutzten ebenfalls strukturelle MRT sowie die Messung eines richtungsunspezifischen Wasser-Diffusionskoeffizienten (apparent diffusion coefficient, ADC) in verschiedenen Gehirnregionen (bekanntermaßen bei ASD erhöht als Zeichen einer Veränderung der weißen Substanz) 84 .…”

Section: Gehirngröße Und Autismusunclassified

Autismus: Kindheit – Krankheit – Menschheit

Spitzer¹

2022

Nervenheilkunde

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Diagnosing autism spectrum disorder in children using conventional MRI and apparent diffusion coefficient based deep learning algorithms

Cited by 13 publications

References 42 publications

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

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

Machine learning for autism spectrum disorder diagnosis using structural magnetic resonance imaging: Promising but challenging

Autismus: Kindheit – Krankheit – Menschheit

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