scholarly journals Machine learning approach for early detection of autism by combining questionnaire and home video screening

2018 ◽  
Vol 25 (8) ◽  
pp. 1000-1007 ◽  
Author(s):  
Halim Abbas ◽  
Ford Garberson ◽  
Eric Glover ◽  
Dennis P Wall
Keyword(s):  
Machine Learning ◽  
Clinical Study ◽  
Early Detection ◽  
Low Cost ◽  
Clinical Analysis ◽  
Autism Spectrum ◽  
Training Data ◽  
Screening Tools ◽  
Home Video ◽  
Home Videos

Abstract Background Existing screening tools for early detection of autism are expensive, cumbersome, time- intensive, and sometimes fall short in predictive value. In this work, we sought to apply Machine Learning (ML) to gold standard clinical data obtained across thousands of children at-risk for autism spectrum disorder to create a low-cost, quick, and easy to apply autism screening tool. Methods Two algorithms are trained to identify autism, one based on short, structured parent-reported questionnaires and the other on tagging key behaviors from short, semi-structured home videos of children. A combination algorithm is then used to combine the results into a single assessment of higher accuracy. To overcome the scarcity, sparsity, and imbalance of training data, we apply novel feature selection, feature engineering, and feature encoding techniques. We allow for inconclusive determination where appropriate in order to boost screening accuracy when conclusive. The performance is then validated in a controlled clinical study. Results A multi-center clinical study of n = 162 children is performed to ascertain the performance of these algorithms and their combination. We demonstrate a significant accuracy improvement over standard screening tools in measurements of AUC, sensitivity, and specificity. Conclusion These findings suggest that a mobile, machine learning process is a reliable method for detection of autism outside of clinical settings. A variety of confounding factors in the clinical analysis are discussed along with the solutions engineered into the algorithms. Final results are statistically limited and will benefit from future clinical studies to extend the sample size.

scholarly journals Applying machine learning on home videos for remote autism diagnosis: Further study and analysis

2021 ◽  
Vol 27 (1) ◽  
pp. 146045822199188
Author(s):  
Mohamed A. Nabil ◽  
Ansam Akram ◽  
Karma M Fathalla
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
True Positive Rate ◽  
Autism Spectrum ◽  
Support Vector ◽  
Home Video ◽  
Home Videos ◽  
Machine Learning Approach ◽  
Positive Rate ◽  
Interpretable Models

Autism Spectrum Disorder (Autism) is a developmental disorder that impedes the social and communication capabilities of a person through out his life. Early detection of autism is critical in contributing to better prognosis. In this study, the use of home videos to provide accessible diagnosis is investigated. A machine learning approach is adopted to detect autism from home videos. Feature selection and state-of-the-art classification methods are applied to provide a sound diagnosis based on home video ratings obtained from non-clinicians feedback. Our models results indicate that home videos can effectively detect autistic group with True Positive Rate reaching 94.05% using Support Vector Machines and backwards feature selection. In this study, human-interpretable models are presented to elucidate the reasoning behind the classification process and its subsequent decision. In addition, the prime features that need to be monitored for early autism detection are revealed.

scholarly journals Genotype-Based Deep Learning in Autism Spectrum Disorder: Diagnostic Classification and Prognostic Prediction Using Common Genetic Variants (Preprint)

2020 ◽  
Author(s):  
Haishuai Wang ◽  
Paul Avillach
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Deep Learning ◽  
The United States ◽  
Autism Spectrum ◽  
Repetitive Behaviors ◽  
Spectrum Disorder ◽  
Screening Tools ◽  
Common Variants ◽  
Autism Screening

BACKGROUND In the United States, about 3 million people have autism spectrum disorder (ASD), and around 1 out of 59 children are diagnosed with ASD. People with ASD have characteristic social communication deficits and repetitive behaviors. The causes of this disorder remain unknown; however, in up to 25% of cases, a genetic cause can be identified. Detecting ASD as early as possible is desirable because early detection of ASD enables timely interventions in children with ASD. Identification of ASD based on objective pathogenic mutation screening is the major first step toward early intervention and effective treatment of affected children. OBJECTIVE Recent investigation interrogated genomics data for detecting and treating autism disorders, in addition to the conventional clinical interview as a diagnostic test. Since deep neural networks perform better than shallow machine learning models on complex and high-dimensional data, in this study, we sought to apply deep learning to genetic data obtained across thousands of simplex families at risk for ASD to identify contributory mutations and to create an advanced diagnostic classifier for autism screening. METHODS After preprocessing the genomics data from the Simons Simplex Collection, we extracted top ranking common variants that may be protective or pathogenic for autism based on a chi-square test. A convolutional neural network–based diagnostic classifier was then designed using the identified significant common variants to predict autism. The performance was then compared with shallow machine learning–based classifiers and randomly selected common variants. RESULTS The selected contributory common variants were significantly enriched in chromosome X while chromosome Y was also discriminatory in determining the identification of autistic from nonautistic individuals. The ARSD, MAGEB16, and MXRA5 genes had the largest effect in the contributory variants. Thus, screening algorithms were adapted to include these common variants. The deep learning model yielded an area under the receiver operating characteristic curve of 0.955 and an accuracy of 88% for identifying autistic from nonautistic individuals. Our classifier demonstrated a significant improvement over standard autism screening tools by average 13% in terms of classification accuracy. CONCLUSIONS Common variants are informative for autism identification. Our findings also suggest that the deep learning process is a reliable method for distinguishing the diseased group from the control group based on the common variants of autism.

scholarly journals 2D Materials in Development of Electrochemical Point-of-Care Cancer Screening Devices

Micromachines ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 662 ◽  
Author(s):  
Mohsen Mohammadniaei ◽  
Huynh Vu Nguyen ◽  
My Van Tieu ◽  
Min-Ho Lee
Keyword(s):  
Cancer Screening ◽  
2D Materials ◽  
Point Of Care ◽  
Low Cost ◽  
Clinical Analysis ◽  
Cancer Diagnostics ◽  
Electrochemical Sensing ◽  
Screening Tools ◽  
Electrochemical Biosensors ◽  
2D Material

Effective cancer treatment requires early detection and monitoring the development progress in a simple and affordable manner. Point-of care (POC) screening can provide a portable and inexpensive tool for the end-users to conveniently operate test and screen their health conditions without the necessity of special skills. Electrochemical methods hold great potential for clinical analysis of variety of chemicals and substances as well as cancer biomarkers due to their low cost, high sensitivity, multiplex detection ability, and miniaturization aptitude. Advances in two-dimensional (2D) material-based electrochemical biosensors/sensors are accelerating the performance of conventional devices toward more practical approaches. Here, recent trends in the development of 2D material-based electrochemical biosensors/sensors, as the next generation of POC cancer screening tools, are summarized. Three cancer biomarker categories, including proteins, nucleic acids, and some small molecules, will be considered. Various 2D materials will be introduced and their biomedical applications and electrochemical properties will be given. The role of 2D materials in improving the performance of electrochemical sensing mechanisms as well as the pros and cons of current sensors as the prospective devices for POC screening will be emphasized. Finally, the future scopes of implementing 2D materials in electrochemical POC cancer diagnostics for the clinical translation will be discussed.

scholarly journals A Comparison of Screening Tools for the Early Detection of Peripheral Neuropathy in Adults with and without Type 2 Diabetes

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Jennifer J. Brown ◽  
Shana L. Pribesh ◽  
Kimberly G. Baskette ◽  
Aaron I. Vinik ◽  
Sheri R. Colberg
Keyword(s):  
Type 2 Diabetes ◽  
Peripheral Neuropathy ◽  
Early Detection ◽  
Sural Nerve ◽  
Tuning Fork ◽  
Low Cost ◽  
Screening Tools ◽  
The Right ◽  
Design And Methods

Objective. Examine the effectiveness of the 128 Hz tuning fork, two monofilaments, and Norfolk Quality of Life Diabetic Neuropathy (QOL-DN) questionnaire as tools for the early detection of diabetic peripheral neuropathy (DPN) in overweight, obese, and inactive (OOI) adults or those who have prediabetes (PD) or type 2 diabetes (T2D). Research Design and Methods. Thirty-four adults (mean age 58.4 years ± 12.1) were divided by glycemia (10 OOI normoglycemic, 13 PD, and 11 T2D). Sural nerves were tested bilaterally with the NC-stat DPNCheck to determine sural nerve amplitude potential (SNAP) and sural nerve conduction velocity (SNCV). All other testing results were compared to SNAP and SNCV. Results. Total 1 g monofilament scores significantly correlated with SNAP values and yielded the highest sensitivity and specificity combinations of tested measures. Total QOL-DN scores negatively correlated with SNAP values, as did QOL-DN symptoms. QOL-DN activities of daily living correlated with the right SNAP, and the QOL-DN small fiber subscore correlated with SNCV. Conclusions. The 1 g monofilament and total QOL-DN are effective, low-cost tools for the early detection of DPN in OOI, PD, and T2D adults. The 128 Hz tuning fork and 10 g monofilament may assist DPN screening as a tandem, but not primary, early DPN detection screening tools.

scholarly journals Digital Biomarker Representing Frailty Phenotypes: The Use of Machine Learning and Sensor-Based Sit-to-Stand Test

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3258
Author(s):  
Catherine Park ◽  
Ramkinker Mishra ◽  
Amir Sharafkhaneh ◽  
Mon S. Bryant ◽  
Christina Nguyen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Low Cost ◽  
Wearable Sensors ◽  
Area Under The Curve ◽  
Frailty Model ◽  
Screening Tools ◽  
Physical Frailty ◽  
Stand Test ◽  
Sit To Stand ◽  
Range Indicator

Since conventional screening tools for assessing frailty phenotypes are resource intensive and unsuitable for routine application, efforts are underway to simplify and shorten the frailty screening protocol by using sensor-based technologies. This study explores whether machine learning combined with frailty modeling could determine the least sensor-derived features required to identify physical frailty and three key frailty phenotypes (slowness, weakness, and exhaustion). Older participants (n = 102, age = 76.54 ± 7.72 years) were fitted with five wearable sensors and completed a five times sit-to-stand test. Seventeen sensor-derived features were extracted and used for optimal feature selection based on a machine learning technique combined with frailty modeling. Mean of hip angular velocity range (indicator of slowness), mean of vertical power range (indicator of weakness), and coefficient of variation of vertical power range (indicator of exhaustion) were selected as the optimal features. A frailty model with the three optimal features had an area under the curve of 85.20%, a sensitivity of 82.70%, and a specificity of 71.09%. This study suggests that the three sensor-derived features could be used as digital biomarkers of physical frailty and phenotypes of slowness, weakness, and exhaustion. Our findings could facilitate future design of low-cost sensor-based technologies for remote physical frailty assessments via telemedicine.

scholarly journals Detecting Developmental Delay and Autism Through Machine Learning Models Using Home Videos of Bangladeshi Children: Development and Validation Study (Preprint)

2019 ◽  
Author(s):  
Qandeel Tariq ◽  
Scott Lanyon Fleming ◽  
Jessey Nicole Schwartz ◽  
Kaitlyn Dunlap ◽  
Conor Corbin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Developmental Delays ◽  
Area Under The Curve ◽  
Population Level ◽  
Autism Spectrum ◽  
Risk Scores ◽  
Future Research ◽  
Home Videos ◽  
Shapley Values ◽  
Children Development

BACKGROUND Autism spectrum disorder (ASD) is currently diagnosed using qualitative methods that measure between 20-100 behaviors, can span multiple appointments with trained clinicians, and take several hours to complete. In our previous work, we demonstrated the efficacy of machine learning classifiers to accelerate the process by collecting home videos of US-based children, identifying a reduced subset of behavioral features that are scored by untrained raters using a machine learning classifier to determine children’s “risk scores” for autism. We achieved an accuracy of 92% (95% CI 88%-97%) on US videos using a classifier built on five features. OBJECTIVE Using videos of Bangladeshi children collected from Dhaka Shishu Children’s Hospital, we aim to scale our pipeline to another culture and other developmental delays, including speech and language conditions. METHODS Although our previously published and validated pipeline and set of classifiers perform reasonably well on Bangladeshi videos (75% accuracy, 95% CI 71%-78%), this work improves on that accuracy through the development and application of a powerful new technique for adaptive aggregation of crowdsourced labels. We enhance both the utility and performance of our model by building two classification layers: The first layer distinguishes between typical and atypical behavior, and the second layer distinguishes between ASD and non-ASD. In each of the layers, we use a unique rater weighting scheme to aggregate classification scores from different raters based on their expertise. We also determine Shapley values for the most important features in the classifier to understand how the classifiers’ process aligns with clinical intuition. RESULTS Using these techniques, we achieved an accuracy (area under the curve [AUC]) of 76% (SD 3%) and sensitivity of 76% (SD 4%) for identifying atypical children from among developmentally delayed children, and an accuracy (AUC) of 85% (SD 5%) and sensitivity of 76% (SD 6%) for identifying children with ASD from those predicted to have other developmental delays. CONCLUSIONS These results show promise for using a mobile video-based and machine learning–directed approach for early and remote detection of autism in Bangladeshi children. This strategy could provide important resources for developmental health in developing countries with few clinical resources for diagnosis, helping children get access to care at an early age. Future research aimed at extending the application of this approach to identify a range of other conditions and determine the population-level burden of developmental disabilities and impairments will be of high value.

scholarly journals Intelligent Frame Work to Predict Autism in Infants using Machine Learning

2020 ◽  
Vol 9 (1) ◽  
pp. 675-679
Keyword(s):  
Machine Learning ◽  
Cognitive Abilities ◽  
False Positive Rate ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Training Data ◽  
Screening Tests ◽  
Behavioral Impairment ◽  
Medical Checkup ◽  
Positive Rate

Autistic Spectrum Disorder (ASD) is a behavioral impairment that interferes with the use of auditory, communicative, cognitive, abilities and social skills. ADS was introduce researched using advanced approaches based on machine learning to speed up the diagnostic period or increase the responsiveness, reliability or precision of the diagnosis process. Normal medical checkup data (training data) of the baby, test data is used to classify the child with autism. The assessment results showed that, for both types of datasets, the proposed prediction model produces better results in terms of precision, responsiveness, precision and false positive rate (FPR). In the modern day, Autism Spectrum Disorder (ASD) is gaining some momentum quicker than ever. Detecting signs of autism by screening tests is very costly and time consuming. Autism can be identified faster by combining artificial intelligence and Machine Learning (ML)

scholarly journals Deriving Field Scale Soil Moisture from Satellite Observations and Ground Measurements in a Hilly Agricultural Region

2019 ◽  
Vol 11 (22) ◽  
pp. 2596 ◽  
Author(s):  
Luca Zappa ◽  
Matthias Forkel ◽  
Angelika Xaver ◽  
Wouter Dorigo
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Soil Texture ◽  
Vegetation Cover ◽  
Learning Algorithm ◽  
Low Cost ◽  
Cost Effective ◽  
Training Data ◽  
Field Scale ◽  
Training Set

Agricultural and hydrological applications could greatly benefit from soil moisture (SM) information at sub-field resolution and (sub-) daily revisit time. However, current operational satellite missions provide soil moisture information at either lower spatial or temporal resolution. Here, we downscale coarse resolution (25–36 km) satellite SM products with quasi-daily resolution to the field scale (30 m) using the random forest (RF) machine learning algorithm. RF models are trained with remotely sensed SM and ancillary variables on soil texture, topography, and vegetation cover against SM measured in the field. The approach is developed and tested in an agricultural catchment equipped with a high-density network of low-cost SM sensors. Our results show a strong consistency between the downscaled and observed SM spatio-temporal patterns. We found that topography has higher predictive power for downscaling than soil texture, due to the hilly landscape of the study area. Furthermore, including a proxy of vegetation cover results in considerable improvements of the performance. Increasing the training set size leads to significant gain in the model skill and expanding the training set is likely to further enhance the accuracy. When only limited in-situ measurements are available as training data, increasing the number of sensor locations should be favored over expanding the duration of the measurements for improved downscaling performance. In this regard, we show the potential of low-cost sensors as a practical and cost-effective solution for gathering the necessary observations. Overall, our findings highlight the suitability of using ground measurements in conjunction with machine learning to derive high spatially resolved SM maps from coarse-scale satellite products.

scholarly journals Crowdsourced privacy-preserved feature tagging of short home videos for machine learning ASD detection

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Peter Washington ◽  
Qandeel Tariq ◽  
Emilie Leblanc ◽  
Brianna Chrisman ◽  
Kaitlyn Dunlap ◽  
...  
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
High Performance ◽  
High Sensitivity ◽  
Autism Spectrum ◽  
Privacy Preserving ◽  
Machine Learning Classification ◽  
Home Videos ◽  
Video Feature ◽  
Pitch Shift

AbstractStandard medical diagnosis of mental health conditions requires licensed experts who are increasingly outnumbered by those at risk, limiting reach. We test the hypothesis that a trustworthy crowd of non-experts can efficiently annotate behavioral features needed for accurate machine learning detection of the common childhood developmental disorder Autism Spectrum Disorder (ASD) for children under 8 years old. We implement a novel process for identifying and certifying a trustworthy distributed workforce for video feature extraction, selecting a workforce of 102 workers from a pool of 1,107. Two previously validated ASD logistic regression classifiers, evaluated against parent-reported diagnoses, were used to assess the accuracy of the trusted crowd’s ratings of unstructured home videos. A representative balanced sample (N = 50 videos) of videos were evaluated with and without face box and pitch shift privacy alterations, with AUROC and AUPRC scores > 0.98. With both privacy-preserving modifications, sensitivity is preserved (96.0%) while maintaining specificity (80.0%) and accuracy (88.0%) at levels comparable to prior classification methods without alterations. We find that machine learning classification from features extracted by a certified nonexpert crowd achieves high performance for ASD detection from natural home videos of the child at risk and maintains high sensitivity when privacy-preserving mechanisms are applied. These results suggest that privacy-safeguarded crowdsourced analysis of short home videos can help enable rapid and mobile machine-learning detection of developmental delays in children.

Sign in / Sign up

Export Citation Format

Share Document