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1. Detection of COVID-19 using multimodal data from a wearable device: results from the first TemPredict Study

   https://doi.org/10.1038/s41598-022-07314-0  

   Mason, Ashley E.; Hecht, Frederick M.; Davis, Shakti K.; Natale, Joseph L.; Hartogensis, Wendy; Damaso, Natalie; Claypool, Kajal T.; Dilchert, Stephan; Dasgupta, Subhasis; Purawat, Shweta; et al (December 2022, Scientific Reports)

   Abstract Early detection of diseases such as COVID-19 could be a critical tool in reducing disease transmission by helping individuals recognize when they should self-isolate, seek testing, and obtain early medical intervention. Consumer wearable devices that continuously measure physiological metrics hold promise as tools for early illness detection. We gathered daily questionnaire data and physiological data using a consumer wearable (Oura Ring) from 63,153 participants, of whom 704 self-reported possible COVID-19 disease. We selected 73 of these 704 participants with reliable confirmation of COVID-19 by PCR testing and high-quality physiological data for algorithm training to identify onset of COVID-19 using machine learning classification. The algorithm identified COVID-19 an average of 2.75 days before participants sought diagnostic testing with a sensitivity of 82% and specificity of 63%. The receiving operating characteristic (ROC) area under the curve (AUC) was 0.819 (95% CI [0.809, 0.830]). Including continuous temperature yielded an AUC 4.9% higher than without this feature. For further validation, we obtained SARS CoV-2 antibody in a subset of participants and identified 10 additional participants who self-reported COVID-19 disease with antibody confirmation. The algorithm had an overall ROC AUC of 0.819 (95% CI [0.809, 0.830]), with a sensitivity of 90% and specificity of 80% in these additional participants. Finally, we observed substantial variation in accuracy based on age and biological sex. Findings highlight the importance of including temperature assessment, using continuous physiological features for alignment, and including diverse populations in algorithm development to optimize accuracy in COVID-19 detection from wearables. 

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2. Evaluating Multicultural Autism Screening for Toddlers Using Machine Learning on the QCHAT-10

   https://doi.org/10.1101/2024.11.12.24317211  

   Sollis, Lydia J; Wall, Dennis P; Washington, Peter Y (November 2024, medRxiv)

   ABSTRACT Early identification and intervention often leads to improved life outcomes for individuals with Autism Spectrum Disorder (ASD). However, traditional diagnostic methods are time-consuming, frequently delaying treatment. This study examines the application of machine learning (ML) techniques to 10-question Quantitative Checklist for Autism in Toddlers (QCHAT-10) datasets, aiming to evaluate the predictive value of questionnaire features and overall accuracy metrics across different cultures. We trained models using three distinct datasets from three different countries: Poland, New Zealand, and Saudi Arabia. The New Zealand and Saudi Arabian-trained models were both tested on the Polish dataset, which consisted of diagnostic class labels derived from clinical diagnostic processes. The Decision Tree, Random Forest, and XGBoost models were evaluated, with XGBoost consistently performing best. Feature importance rankings revealed little consistency across models; however, Recursive Feature Elimination (RFE) to select the models with the four most predictive features retained three common features. Both models performed similarly on the Polish test dataset with clinical diagnostic labels, with the New Zealand models with all 13 features achieving an AUROC of 0.94 ± 0.06, and the Saudi Model having an AUROC of 93% ± 6. This compared favorably to the cross-validation analysis of a Polish-trained model, which had an AUROC of 94% ± 5, suggesting that answers to the QCHAT-10 can be predictive of an official autism diagnosis, even across cultures. The New Zealand model with four features had an AUROC of 85% ± 13, and the Saudi model had a similar result of 87% ± 11. These results were somewhat lower than the Polish cross-validation AUROC of 91% ± 5. Adjusting probability thresholds improved sensitivity in some models, which is crucial for screening tools. However, this threshold adjustment often resulted in low levels of specificity during the final testing phase. Our findings suggest that these screening tools may generalize well across cultures; however, more research is needed regarding differences in feature importance for different populations. 

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3. Classification accuracy of the Quick Interactive Language Screener for preschool children with and without developmental language disorder

   https://doi.org/10.1016/j.jcomdis.2022.106276  

   Pace, Amy; Curran, Maura; Van Horne, Amanda Owen; de Villiers, Jill; Iglesias, Aquiles; Golinkoff, Roberta Michnick; Wilson, Mary S.; Hirsh-Pasek, Kathy (November 2022, Journal of Communication Disorders)

   Abstract Introduction This research examined the classification accuracy of the Quick Interactive Language Screener (QUILS) for identifying preschool-aged children (3;0 to 6;9) with developmental language disorder (DLD). We present data from two independent samples that varied in prevalence and diagnostic reference standard. Methods Study 1 included a clinical sample of children (54 with DLD; 13 without) who completed the QUILS and a standardized assessment of expressive grammar (Syntax subtest from the Diagnostic Evaluation of Language Variation–Norm Referenced; Structured Photographic Expressive Language Test–Preschool 2nd Edition; or Structured Photographic Expressive Language Test–3 rd Edition). Study 2 included a community sample of children (25 with DLD; 101 without) who completed the QUILS and the Auditory Comprehension subtest of the Preschool Language Scales–5th Edition (PLS-5; Zimmerman et al., 2011). Discriminant analyses were conducted to compare classification accuracy (i.e., sensitivity and specificity) using the normreferenced cut score (< 25th percentile) with empirically derived cut scores. Results In Study 1, the QUILS led to low fail rates (i.e., high specificity) in children without impairment and statistically significant group differences as a function of children's clinical status; however, only 65% of children with DLD were accurately identified using the norm-referenced cutoff. In Study 2, 76% of children with DLD were accurately identified at the 25th percentile cutoff and accuracy improved to 84% when an empirically derived cutoff (<32nd percentile) was applied. Conclusions Findings support the clinical application of the QUILS as a component of the screening process for identifying the presence or absence of DLD in community samples of preschool-aged children. 

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4. Development and External Validation of a Machine Learning Tool to Rule Out COVID-19 Among Adults in the Emergency Department Using Routine Blood Tests: A Large, Multicenter, Real-World Study

   https://doi.org/10.2196/24048  

   Plante, Timothy B; Blau, Aaron M; Berg, Adrian N; Weinberg, Aaron S; Jun, Ik C; Tapson, Victor F; Kanigan, Tanya S; Adib, Artur B (January 2020, Journal of Medical Internet Research)

   null (Ed.)

   Background Conventional diagnosis of COVID-19 with reverse transcription polymerase chain reaction (RT-PCR) testing (hereafter, PCR) is associated with prolonged time to diagnosis and significant costs to run the test. The SARS-CoV-2 virus might lead to characteristic patterns in the results of widely available, routine blood tests that could be identified with machine learning methodologies. Machine learning modalities integrating findings from these common laboratory test results might accelerate ruling out COVID-19 in emergency department patients. Objective We sought to develop (ie, train and internally validate with cross-validation techniques) and externally validate a machine learning model to rule out COVID 19 using only routine blood tests among adults in emergency departments. Methods Using clinical data from emergency departments (EDs) from 66 US hospitals before the pandemic (before the end of December 2019) or during the pandemic (March-July 2020), we included patients aged ≥20 years in the study time frame. We excluded those with missing laboratory results. Model training used 2183 PCR-confirmed cases from 43 hospitals during the pandemic; negative controls were 10,000 prepandemic patients from the same hospitals. External validation used 23 hospitals with 1020 PCR-confirmed cases and 171,734 prepandemic negative controls. The main outcome was COVID 19 status predicted using same-day routine laboratory results. Model performance was assessed with area under the receiver operating characteristic (AUROC) curve as well as sensitivity, specificity, and negative predictive value (NPV). Results Of 192,779 patients included in the training, external validation, and sensitivity data sets (median age decile 50 [IQR 30-60] years, 40.5% male [78,249/192,779]), AUROC for training and external validation was 0.91 (95% CI 0.90-0.92). Using a risk score cutoff of 1.0 (out of 100) in the external validation data set, the model achieved sensitivity of 95.9% and specificity of 41.7%; with a cutoff of 2.0, sensitivity was 92.6% and specificity was 59.9%. At the cutoff of 2.0, the NPVs at a prevalence of 1%, 10%, and 20% were 99.9%, 98.6%, and 97%, respectively. Conclusions A machine learning model developed with multicenter clinical data integrating commonly collected ED laboratory data demonstrated high rule-out accuracy for COVID-19 status, and might inform selective use of PCR-based testing. 

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5. Hybrid high-definition microvessel imaging/shear wave elastography improves breast lesion characterization

   https://doi.org/10.1186/s13058-022-01511-5  

   Gu, Juanjuan; Ternifi, Redouane; Larson, Nicholas B.; Carter, Jodi M.; Boughey, Judy C.; Stan, Daniela L.; Fazzio, Robert T.; Fatemi, Mostafa; Alizad, Azra (December 2022, Breast Cancer Research)

   Abstract Background Low specificity in current breast imaging modalities leads to increased unnecessary follow-ups and biopsies. The purpose of this study is to evaluate the efficacy of combining the quantitative parameters of high-definition microvasculature imaging (HDMI) and 2D shear wave elastography (SWE) with clinical factors (lesion depth and age) for improving breast lesion differentiation. Methods In this prospective study, from June 2016 through April 2021, patients with breast lesions identified on diagnostic ultrasound and recommended for core needle biopsy were recruited. HDMI and SWE were conducted prior to biopsies. Two new HDMI parameters, Murray’s deviation and bifurcation angle, and a new SWE parameter, mass characteristic frequency, were included for quantitative analysis. Lesion malignancy prediction models based on HDMI only, SWE only, the combination of HDMI and SWE, and the combination of HDMI, SWE and clinical factors were trained via elastic net logistic regression with 70% (360/514) randomly selected data and validated with the remaining 30% (154/514) data. Prediction performances in the validation test set were compared across models with respect to area under the ROC curve as well as sensitivity and specificity based on optimized threshold selection. Results A total of 508 participants (mean age, 54 years ± 15), including 507 female participants and 1 male participant, with 514 suspicious breast lesions (range, 4–72 mm, median size, 13 mm) were included. Of the lesions, 204 were malignant. The SWE-HDMI prediction model, combining quantitative parameters from SWE and HDMI, with AUC of 0.973 (95% CI 0.95–0.99), was significantly higher than the result predicted with the SWE model or HDMI model alone. With an optimal cutoff of 0.25 for the malignancy probability, the sensitivity and specificity were 95.5% and 89.7%, respectively. The specificity was further improved with the addition of clinical factors. The corresponding model defined as the SWE-HDMI-C prediction model had an AUC of 0.981 (95% CI 0.96–1.00). Conclusions The SWE-HDMI-C detection model, a combination of SWE estimates, HDMI quantitative biomarkers and clinical factors, greatly improved the accuracy in breast lesion characterization. 

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