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Genomics & Precision Health Database|Non-Genomics Precision Health Update Archive|Public Health Genomics and Precision Health Knowledge Base (PHGKB) Published on 06/17/2021

About Non-Genomics Precision Health Scan

This update features emerging roles of big data science, machine learning, and predictive analytics across the life span. The scan focus on various conditions including, birth defects, newborn screening, reproductive health, childhood diseases, cancer, chronic diseases, medication, family health history, guidelines and recommendations. The sweep also includes news, reviews, commentaries, tools and database. View Data Selection Criteria

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Non-Genomics Precision Health Scan

Birth Defects and Child Health

Detecting acute bilirubin encephalopathy in neonates based on multimodal MRI with deep learning.
Wu Miao et al. Pediatric research 2021

Machine Learning Classification of Inflammatory Bowel Disease in Children Based on a Large Real-World Pediatric Cohort CEDATA-GPGE® Registry.
Schneider Nicolas et al. Frontiers in medicine 2021 8666190

Progress, Challenges, and Global Approaches to Rare Diseases.
Groft Stephen C et al. Acta paediatrica (Oslo, Norway : 1992) 2021

Cancer

High-resolution CT image analysis based on 3D convolutional neural network can enhance the classification performance of radiologists in classifying pulmonary non-solid nodules.
Zhang Teng et al. European journal of radiology 2021 141109810

Development and validation of artificial intelligence to detect and diagnose liver lesions from ultrasound images.
Tiyarattanachai Thodsawit et al. PloS one 2021 16(6) e0252882

Automated data abstraction for quality surveillance and outcome assessment in radiation oncology.
Kapoor Rishabh et al. Journal of applied clinical medical physics 2021

Role of Radiomics in the Prediction of Muscle-invasive Bladder Cancer: A Systematic Review and Meta-analysis.
Kozikowski Mieszko et al. European urology focus 2021

Deep learning for diagnosis of precancerous lesions in upper gastrointestinal endoscopy: A review.
Yan Tao et al. World journal of gastroenterology 2021 27(20) 2531-2544

[Molecular Diagnostic Approaches for Diffuse Gliomas Based on Updated WHO Classification and cIMPACT-NOW Proposals].
Masui Kenta et al. No shinkei geka. Neurological surgery 2021 49(3) 510-519

[Connected bras for breast cancer detection in 2021: Analysis and perspectives.]
Masry Zeina Al et al. Gynecologie, obstetrique, fertilite & senologie 2021

T-staging pulmonary oncology from radiological reports using natural language processing: translating into a multi-language setting.
Nobel J Martijn et al. Insights into imaging 2021 12(1) 77

Machine learning-based CT radiomics features for the prediction of pulmonary metastasis in osteosarcoma.
Pereira Helcio Mendonça et al. The British journal of radiology 2021 20201391

Future of biomarker evaluation in the realm of artificial intelligence algorithms: application in improved therapeutic stratification of patients with breast and prostate cancer.
Fitzgerald Jenny et al. Journal of clinical pathology 2021

Chronic Disease

Predictive analytics and tailored interventions improve clinical outcomes in older adults: a randomized controlled trial
SB Golas et al, NPJ DIgital Medicine

This randomized controlled trial evaluated the impact of a Stepped-Care approach (predictive analytics + tailored nurse-driven interventions) on healthcare utilization among 370 older adult patients enrolled in a homecare management program and using a Personal Emergency Response System. The Control group (CG) received care as usual, while the Intervention group (IG) received Stepped-Care during a 180-day intervention period.

Liver disease classification from ultrasound using multi-scale CNN.
Che Hui et al. International journal of computer assisted radiology and surgery 2021

Inter-Cohort Validation of SuStaIn Model for Alzheimer's Disease.
Archetti Damiano et al. Frontiers in big data 2021 4661110

Cross-cohort generalizability of deep and conventional machine learning for MRI-based diagnosis and prediction of Alzheimer's disease.
Bron Esther E et al. NeuroImage. Clinical 2021 31102712

Development of a predictive risk model for all-cause mortality in patients with diabetes in Hong Kong.
Lee Sharen et al. BMJ open diabetes research & care 2021 9(1)

Diagnostic Accuracy and Failure Mode Analysis of a Deep Learning Algorithm for the Detection of Cervical Spine Fractures.
Voter A F et al. AJNR. American journal of neuroradiology 2021

Deep learning-based amyloid PET positivity classification model in the Alzheimer's disease continuum by using 2-[F]FDG PET.
Kim Suhong et al. EJNMMI research 2021 11(1) 56

Prediction of Type II Diabetes Onset with Computed Tomography and Electronic Medical Records.
Tang Yucheng et al. Multimodal learning for clinical decision support and clinical image-based procedures : 10th International Workshop, ML-CDS 2020, and 9th International Workshop, CLIP 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4-8, ... 2021 1244513-23

Prediction of caregiver quality of life in amyotrophic lateral sclerosis using explainable machine learning.
Antoniadi Anna Markella et al. Scientific reports 2021 11(1) 12237

ARTIFICIAL INTELLIGENCE IN DIABETIC RETINOPATHY SCREENING. A REVIEW.
Stranák Z et al. Ceska a slovenska oftalmologie : casopis Ceske oftalmologicke spolecnosti a Slovenske oftalmologicke spolecnosti 2020 1(Ahead of print) 1-8

Predictive analytics and tailored interventions improve clinical outcomes in older adults: a randomized controlled trial.
Golas Sara Bersche et al. NPJ digital medicine 2021 4(1) 97

Ethical, Legal and Social Issues (ELSI)

Ensuring patient and public involvement in the transition to AI-assisted mental health care: A systematic scoping review and agenda for design justice.
Zidaru Teodor et al. Health expectations : an international journal of public participation in health care and health policy 2021

Choice of measurement approach for area-level social determinants of health and risk prediction model performance.
Vest J R et al. Informatics for health & social care 2021 1-12

General Practice

Deep Learning Model of fMRI Connectivity Predicts PTSD Symptom Trajectories in Recent Trauma Survivors.
Sheynin Shelly et al. NeuroImage 2021 118242

Collective human intelligence outperforms artificial intelligence in a skin lesion classification task.
Winkler Julia K et al. Journal der Deutschen Dermatologischen Gesellschaft = Journal of the German Society of Dermatology : JDDG 2021

A Position Statement on Population Data Science: The Science of Data about People.
McGrail Kimberlyn M et al. International journal of population data science 2018 3(1) 415

Evaluation of Three Machine Learning Algorithms for the Automatic Classification of EMG Patterns in Gait Disorders.
Fricke Christopher et al. Frontiers in neurology 2021 12666458

State of machine and deep learning in histopathological applications in digestive diseases.
Kobayashi Soma et al. World journal of gastroenterology 2021 27(20) 2545-2575

A preliminary evaluation study of applying a deep learning image reconstruction algorithm in low-kilovolt scanning of upper abdomen.
Wang Ya-Ning et al. Journal of X-ray science and technology 2021

Using large-scale experiments and machine learning to discover theories of human decision-making.
Peterson Joshua C et al. Science (New York, N.Y.) 2021 372(6547) 1209-1214

Predicting and understanding how people make decisions has been a long-standing goal in many fields, with quantitative models of human decision-making informing research in both the social sciences and engineering. We show how progress toward this goal can be accelerated by using large datasets to power machine-learning algorithms that are constrained to produce interpretable psychological theories.

Continuous health monitoring: An opportunity for precision health.
Gambhir Sanjiv S et al. Science translational medicine 2021 13(597)

Continuous health monitoring and integrated diagnostic devices, worn on the body and used in the home, will help to identify and prevent early manifestations of disease. However, challenges lie ahead in validating new health monitoring technologies and in optimizing data analytics to extract actionable conclusions from continuously obtained health data.

How does artificial intelligence in radiology improve efficiency and health outcomes?
van Leeuwen Kicky G et al. Pediatric radiology 2021

Wide range of applications for machine-learning prediction models in orthopedic surgical outcome: a systematic review.
Ogink Paul T et al. Acta orthopaedica 2021 1-6

Longitudinal cohorts for harnessing the electronic health record for disease prediction in a US population.
Manemann Sheila M et al. BMJ open 2021 11(6) e044353

Machine-generated theories of human decision-making
S Bhatia et al, Science, June 11, 2021

Heart, Lung, Blood and Sleep Diseases

Meeting the unmet needs of clinicians from AI systems showcased for cardiology with deep-learning-based ECG analysis.
Elul Yonatan et al. Proceedings of the National Academy of Sciences of the United States of America 2021 118(24)

Machine Learning-Based Prediction of Brain Tissue Infarction in Patients With Acute Ischemic Stroke Treated With Theophylline as an Add-On to Thrombolytic Therapy: A Randomized Clinical Trial Subgroup Analysis.
Modrau Boris et al. Frontiers in neurology 2021 12613029

A Versatile Big Data Health System for Australia: Driving Improvements in Cardiovascular Health.
Paige Ellie et al. Heart, lung & circulation 2021

Obstructive sleep apnea prediction from electrocardiogram scalograms and spectrograms using convolutional neural networks.
Nasifoglu Huseyin et al. Physiological measurement 2021

Use of artificial intelligence as an instrument of evaluation after stroke: a scoping review based on international classification of functioning, disability and health concept.
Luvizutto Gustavo José et al. Topics in stroke rehabilitation 2021 1-16

Implementation of a Machine-Learning Algorithm in the Electronic Health Record for Targeted Screening for Familial Hypercholesterolemia: A Quality Improvement Study.
Sheth Samip et al. Circulation. Cardiovascular quality and outcomes 2021 CIRCOUTCOMES120007641

Infectious Diseases

Leveraging artificial intelligence for pandemic preparedness and response: a scoping review to identify key use cases
A Syrowatka et al, NPJ Digital Medicine, June 10, 2021

The search identified 1167 articles that reported on traditional modeling approaches, which highlighted additional areas where ML could be leveraged for improving the accuracy of estimations or projections. Important ML-based solutions have been developed in response to pandemics, and particularly for COVID-19 but few were optimized for practical application early in the pandemic.


Disclaimer: Articles listed in Non-Genomics Precision Health Scan are selected by the CDC Office of Genomics and Precision Public Health to provide current awareness of the scientific literature and news. Inclusion in the update does not necessarily represent the views of the Centers for Disease Control and Prevention nor does it imply endorsement of the article's methods or findings. CDC and DHHS assume no responsibility for the factual accuracy of the items presented. The selection, omission, or content of items does not imply any endorsement or other position taken by CDC or DHHS. Opinion, findings and conclusions expressed by the original authors of items included in the Clips, or persons quoted therein, are strictly their own and are in no way meant to represent the opinion or views of CDC or DHHS. References to publications, news sources, and non-CDC Websites are provided solely for informational purposes and do not imply endorsement by CDC or DHHS.
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