Autism Spectrum Disorder Screening and Early Action

Dunlap, Jayne Jennings

doi:10.1016/j.nurpra.2019.04.001

Cited by 10 publications

(6 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…First, it remains unclear whether and to what extent these tools are typically developed with input from both end users and clinicians [ 38 ]. Frameworks outlining guidelines for the design and development of digital biomarkers would help unify approaches across companies and research entities and ensure standards of quality and safety [ 39 ]. Our work also outlined exciting trends toward the development of mobile-first digital technologies.…”

Section: Discussionmentioning

confidence: 99%

App Characteristics and Accuracy Metrics of Available Digital Biomarkers for Autism: Scoping Review

Ponzo,

May,

Tamayo-Elizalde

et al. 2023

JMIR Mhealth Uhealth

View full text Add to dashboard Cite

Background Diagnostic delays in autism are common, with the time to diagnosis being up to 3 years from the onset of symptoms. Such delays have a proven detrimental effect on individuals and families going through the process. Digital health products, such as mobile apps, can help close this gap due to their scalability and ease of access. Further, mobile apps offer the opportunity to make the diagnostic process faster and more accurate by providing additional and timely information to clinicians undergoing autism assessments. Objective The aim of this scoping review was to synthesize the available evidence about digital biomarker tools to aid clinicians, researchers in the autism field, and end users in making decisions as to their adoption within clinical and research settings. Methods We conducted a structured literature search on databases and search engines to identify peer-reviewed studies and regulatory submissions that describe app characteristics, validation study details, and accuracy and validity metrics of commercial and research digital biomarker apps aimed at aiding the diagnosis of autism. Results We identified 4 studies evaluating 4 products: 1 commercial and 3 research apps. The accuracy of the identified apps varied between 28% and 80.6%. Sensitivity and specificity also varied, ranging from 51.6% to 81.6% and 18.5% to 80.5%, respectively. Positive predictive value ranged from 20.3% to 76.6%, and negative predictive value fluctuated between 48.7% and 97.4%. Further, we found a lack of details around participants’ demographics and, where these were reported, important imbalances in sex and ethnicity in the studies evaluating such products. Finally, evaluation methods as well as accuracy and validity metrics of available tools were not clearly reported in some cases and varied greatly across studies. Different comparators were also used, with some studies validating their tools against the Diagnostic and Statistical Manual of Mental Disorders criteria and others through self-reported measures. Further, while in most cases, 2 classes were used for algorithm validation purposes, 1 of the studies reported a third category (indeterminate). These discrepancies substantially impact the comparability and generalizability of the results, thus highlighting the need for standardized validation processes and the reporting of findings. Conclusions Despite their popularity, systematic evaluations and syntheses of the current state of the art of digital health products are lacking. Standardized and transparent evaluations of digital health tools in diverse populations are needed to assess their real-world usability and validity, as well as help researchers, clinicians, and end users safely adopt novel tools within clinical and research practices.

show abstract

Section: Discussionmentioning

confidence: 99%

App Characteristics and Accuracy Metrics of Available Digital Biomarkers for Autism: Scoping Review

Ponzo,

May,

Tamayo-Elizalde

et al. 2023

JMIR Mhealth Uhealth

View full text Add to dashboard Cite

show abstract

“…Gangguan spektrum autisme (ASD) terdiri dari beberapa gangguan perkembangan saraf kompleks yang menyatu di sekitar gangguan dalam kemampuan komunikatif, fungsi sosial, dan perilaku kaku atau berulang. Cacat yang menyebar ini digambarkan sebagai gangguan spektrum karena variasi yang cukup besar dalam bagaimana individu memanifestasikan gejala [2]. Prevalensi ASD pada populasi global diperkirakan sekitar 1%, tetapi jumlah itu telah meningkat selama dekade terakhir.…”

Section: Pendahuluanunclassified

Prediksi Gejala Autism Spectrum Disorders pada Remaja Menggunakan Optimasi Particle Swarm Optimization dan Algoritma Support Vector Machine

Kurniawan

2020

Journal of Informatics

View full text Add to dashboard Cite

Abstrak: Telah ada peningkatan prevalensi diagnosis Autism Spectrum Disorder (ASD) secara global selama dekade terakhir. Perkiraan prevalensi ASD yang diperbarui dan keseluruhan di Asia akan membantu para profesional kesehatan untuk mengembangkan strategi kesehatan masyarakat yang relevan. Dalam penelitian ini, mengusulkan metode untuk prediksi gejala ASD menggunakan teknik integrasi seleksi fitur PSO dan algoritma Support Vector Machine. Penelitian ini menggunakan dataset dari UCI repository. Model yang diusulkan meliputi penerapan seleksi fitur menggunakan particle swarm optimization (PSO), dengan algoritma pengklasifikasi. Hasil akhir akan dilakukan perbandingan pengujian dan analisa terhadap model prediksi yang memiliki tingkat akurasi tertinggi atau terbaik dalam prediksi gejala ASD. Dalam penelitian ini menggunakan dataset UCI repository yaitu data ASD pada remaja, data tersebut memiliki jumlah data sebanyak 104 instance dan 21 atribut, 41 orang tidak menderita ASD dan 63 orang menderita ASD, tools yang digunakan untuk menerapkan model usulan menggunakan aplikasi Weka versi 3.8.4. Untuk mengetahui model usulan yang diajukan pada penelitian ini, pertama menguji dengan klasifikasi tunggal SVM, dan kedua, menguji dengan seleksi fitur PSO dan algoritma klasifikasi SVM. Untuk mengetahui apakah seleksi fitur PSO berpengaruh terhadap performa algoritma klasifikasi SVM. Pengujian pertama, nilai akurasi yang dihasilkan oleh algoritma klasifikasi SVM adalah sebesar 89.42%, dan nilai AUC sebesar 0.891. Berdasarkan pengujian yang kedua yaitu menggunakan seleksi fitur PSO, seleksi fitur PSO dapat meningkatkan performa algoritma klasifikasi SVM sebesar 2,88% dan nilai AUC sebesar 0,024. Kata kunci: Autism Spectrum Disorder, Particle Swarm Optimizatio, Support Vector Machine Abstract: There has been an increase in the prevalence of diagnoses of Autism Spectrum Disorder (ASD) globally over the past decade. Updated and overall ASD prevalence estimates in Asia will help health professionals to develop relevant public health strategies. In this study, proposing a method for ASD symptom prediction using PSO feature selection integration techniques and the Support Vector Machine algorithm. This study uses a dataset from the UCI repository. The proposed model includes the application of feature selection using particle swarm optimization (PSO), with the classification algorithm. The final result will be a comparison test and analysis of prediction models that have the highest or best accuracy in predicting ASD symptoms. In this study using the UCI repository dataset, ASD data on adolescents, the data has 104 data and 21 attributes, 41 people do not suffer from ASD and 63 people suffer from ASD, tools used to implement the proposed model using the Weka application version 3.8.4 . To find out the proposed model proposed in this study, firstly testing with SVM single classification, and secondly, testing with PSO feature selection and SVM classification algorithm. To find out whether the PSO feature selection affects the performance of the SVM classification algorithm. The first test, the accuracy value generated by the SVM classification algorithm is 89.42%, and the AUC value is 0.891. Based on the second test using PSO feature selection, PSO feature selection can improve the performance of the SVM classification algorithm by 2.88% and the AUC value of 0.024. Keywords: Autism Spectrum Disorder, Particle Swarm Optimizatio, Support Vector Machine.

show abstract

“…Early parental concerns about infants' or toddlers' atypical development may receive little attention until formal ASD diagnosis (Freuler et al, 2014), which is less likely to be confirmed under 2 years old (Crane et al, 2016;Pasco, 2018). Early detection (or diagnosis) of ASD by primary care professionals is crucial to expedite referrals to early intervention services (Dunlap, 2019). Longitudinal studies have shown that early language and attentional skills predicted social functioning outcomes in adults with ASD (Gillespie-Lynch et al, 2012), illustrating potential targets for early interventions.…”

Section: Introductionmentioning

confidence: 99%

“…Longitudinal studies have shown that early language and attentional skills predicted social functioning outcomes in adults with ASD (Gillespie-Lynch et al, 2012), illustrating potential targets for early interventions. In theory, early interventions in ASD may have the potential to alter later developmental outcomes if implemented within the first 2 years of life, which is a critical period of brain development (Botteron, 2019;Dunlap, 2019). Recent research suggests that interventions can be initiated before the emergence of core ASD symptoms.…”

Section: Introductionmentioning

confidence: 99%

Parent-Mediated Interventions for Infants under 24 Months at Risk for Autism Spectrum Disorder: A Systematic Review of Randomized Controlled Trials

Law

Singh

Mastroianni

et al. 2021

J Autism Dev Disord

View full text Add to dashboard Cite

Prodromal symptoms of Autism Spectrum Disorder (ASD) have been detected within the first year of life. This review evaluated evidence from randomized controlled trials (RCTs) of parent-mediated interventions for infants under 24 months who are at risk for ASD. Electronic databases, including grey literature, were searched up till November 2019. Seven RCTs were identified. There was substantial heterogeneity in recruitment, outcome measures and effect size calculations. Interventions did not reduce the risk of later ASD diagnosis and post-intervention effects on infant outcomes were inconsistent, with five studies reporting significant improvements across both treatment and control groups. Moderate level of evidence of intervention effects on parental interaction skills and the small number of RCTs, and significant limitations restrict generalizability across studies.

show abstract

Autism Spectrum Disorder Screening and Early Action

Cited by 10 publications

References 24 publications

App Characteristics and Accuracy Metrics of Available Digital Biomarkers for Autism: Scoping Review

App Characteristics and Accuracy Metrics of Available Digital Biomarkers for Autism: Scoping Review

Prediksi Gejala Autism Spectrum Disorders pada Remaja Menggunakan Optimasi Particle Swarm Optimization dan Algoritma Support Vector Machine

Parent-Mediated Interventions for Infants under 24 Months at Risk for Autism Spectrum Disorder: A Systematic Review of Randomized Controlled Trials

Contact Info

Product

Resources

About