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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 12/22/2022

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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Birth Defects and Child Health

From computer to bedside, involving neonatologists in artificial intelligence models for neonatal medicine.
Vijlbrief Daniel et al. Pediatric research 2022

Cancer

Explainable ensemble learning model improves identification of candidates for oral cancer screening.
Adeoye John et al. Oral oncology 2022 136106278

Performance and comparison of artificial intelligence and human experts in the detection and classification of colonic polyps.
Li Ming-De et al. BMC gastroenterology 2022 22(1) 517

Development of a Machine Learning-Based Screening Method for Thyroid Nodules Classification by Solving the Imbalance Challenge in Thyroid Nodules Data.
Khodabandelu Sajad et al. Journal of research in health sciences 2022 22(3) e00555

Radiomics-based machine learning models for prediction of medulloblastoma subgroups: a systematic review and meta-analysis of the diagnostic test performance.
Karabacak Mert et al. Acta radiologica (Stockholm, Sweden : 1987) 2022 2841851221143496

High-fidelity detection, subtyping, and localization of five skin neoplasms using supervised and semi-supervised learning.
Requa James et al. Journal of pathology informatics 2022 14100159

Breast cancer screening with digital breast tomosynthesis: comparison of different reading strategies implementing artificial intelligence.
Dahlblom Victor et al. European radiology 2022

Deep Learning for the Diagnosis of Esophageal Cancer in Endoscopic Images: A Systematic Review and Meta-Analysis.
Islam Md Mohaimenul et al. Cancers 2022 14(23)

Deep learning-based image reconstruction improves radiologic evaluation of pituitary axis and cavernous sinus invasion in pituitary adenoma.
Park Hyeryeong et al. European journal of radiology 2022 158110647

Evaluation of grade and invasiveness of bladder urothelial carcinoma using infrared imaging and machine learning.
Kujdowicz Monika et al. The Analyst 2022

Application of deep learning as an ancillary diagnostic tool for thyroid FNA cytology.
Hirokawa Mitsuyoshi et al. Cancer cytopathology 2022

Artificial intelligence neural network analysis and application of CT imaging features to predict lymph node metastasis in non-small cell lung cancer.
Geng Mingfei et al. Journal of thoracic disease 2022 14(11) 4384-4394

A deep-learning based system using multi-modal data for diagnosing gastric neoplasms in real-time (with video).
Du Hongliu et al. Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association 2022

Artificial intelligence in cancer research and precision medicine: Applications, limitations and priorities to drive transformation in the delivery of equitable and unbiased care.
Corti Chiara et al. Cancer treatment reviews 2022 112102498

Assessing the efficacy of immunotherapy in lung squamous carcinoma using artificial intelligence neural network.
Li Siqi et al. Frontiers in immunology 2022 131024707

Bladder Cancer Radiation Oncology of the Future: Prognostic Modelling, Radiomics, and Treatment Planning With Artificial Intelligence.
Moore Nicholas S et al. Seminars in radiation oncology 2022 33(1) 70-75

Machine Learning Model Development and Validation for Predicting Outcome in Stage 4 Solid Cancer Patients with Septic Shock Visiting the Emergency Department: A Multi-Center, Prospective Cohort Study.
Ko Byuk Sung et al. Journal of clinical medicine 2022 11(23)

Artificial intelligence: A critical review of applications for lung nodule and lung cancer.
de Margerie-Mellon Constance et al. Diagnostic and interventional imaging 2022

Chronic Disease

Discriminating between bipolar and major depressive disorder using a machine learning approach and resting-state EEG data.
Ravan M et al. Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology 2022 14630-39

Convolutional Neural Network Classification of Rest EEG Signals among People with Epilepsy, Psychogenic Non Epileptic Seizures and Control Subjects.
Lo Giudice Michele et al. International journal of environmental research and public health 2022 19(23)

Identification and prediction of Parkinson's disease subtypes and progression using machine learning in two cohorts.
Dadu Anant et al. NPJ Parkinson's disease 2022 8(1) 172

Smartphone accelerometer data as a proxy for clinical data in modeling of bipolar disorder symptom trajectory.
Bennett Casey C et al. NPJ digital medicine 2022 5(1) 181

Artificial intelligence and the future of radiographic scoring in rheumatoid arthritis: a viewpoint.
Bird Alix et al. Arthritis research & therapy 2022 24(1) 268

Automated artificial intelligence scoring systems for the endoscopic assessment of ulcerative colitis: How far are we from clinical application?
Murino Alberto et al. Gastrointestinal endoscopy 2022

Development and validation of a machine learning-augmented algorithm for diabetes screening in community and primary care settings: A population-based study.
Liu XiaoHuan et al. Frontiers in endocrinology 2022 131043919

Ethical, Legal and Social Issues (ELSI)

"Data makes the story come to life:" understanding the ethical and legal implications of Big Data research involving ethnic minority healthcare workers in the United Kingdom-a qualitative study.
Dove Edward S et al. BMC medical ethics 2022 23(1) 136

General Practice

On the impact of predictive analytics-driven disease management interventions.
Ukert Benjamin et al. The American journal of managed care 2022 28(12) 668-674

Artificial Intelligence Solution for Chest Radiographs in Respiratory Outpatient Clinics: Multicenter Prospective Randomized Study.
Lee Hyun Woo et al. Annals of the American Thoracic Society 2022

The performance evaluation of the state-of-the-art EEG-based seizure prediction models.
Ren Zhe et al. Frontiers in neurology 2022 131016224

Artificial Intelligence Implementation in Healthcare: A Theory-Based Scoping Review of Barriers and Facilitators.
Chomutare Taridzo et al. International journal of environmental research and public health 2022 19(23)

Using Tree-Based Machine Learning for Health Studies: Literature Review and Case Series.
Hu Liangyuan et al. International journal of environmental research and public health 2022 19(23)

Beyond the Digital Competencies of Medical Students: Concerns over Integrating Data Science Basics into the Medical Curriculum.
Lungeanu Diana et al. International journal of environmental research and public health 2022 19(23)

Exploring Stakeholder Requirements to Enable the Research and Development of Artificial Intelligence Algorithms in a Hospital-Based Generic Infrastructure: Protocol for a Multistep Mixed Methods Study.
Weinert Lina et al. JMIR research protocols 2022 11(12) e42208

Implementation of Machine Learning Pipelines for Clinical Practice: Development and Validation Study.
Kanbar Lara J et al. JMIR medical informatics 2022 10(12) e37833

Clinical implementation of suicide risk prediction models in healthcare: a qualitative study.
Yarborough Bobbi Jo H et al. BMC psychiatry 2022 22(1) 789

Physician preference for receiving machine learning predictive results: A cross-sectional multicentric study.
Wichmann Roberta Moreira et al. PloS one 2022 17(12) e0278397

Evidence synthesis, digital scribes, and translational challenges for artificial intelligence in healthcare.
Coiera Enrico et al. Cell reports. Medicine 2022 100860

Heart, Lung, Blood and Sleep Diseases

Artificial intelligence-driven ASPECTS for the detection of early stroke changes in non-contrast CT: a systematic review and meta-analysis.
Adamou Antonis et al. Journal of neurointerventional surgery 2022

Deep learning based on carotid transverse B-mode scan videos for the diagnosis of carotid plaque: a prospective multicenter study.
Liu Jia et al. European radiology 2022

Prediction of incident cardiovascular events using machine learning and CMR radiomics.
Pujadas Esmeralda Ruiz et al. European radiology 2022

Artificial Intelligence Analysis of Bronchiectasis Is Predictive of Outcomes in Chronic Obstructive Pulmonary Disease.
Schiebler Mark L et al. Radiology 2022 222675

Personalized prediction of incident hospitalization for cardiovascular disease in patients with hypertension using machine learning.
Feng Yuanchao et al. BMC medical research methodology 2022 22(1) 325

A Machine-Learning Model for the Prognostic Role of C-Reactive Protein in Myocarditis.
Baritussio Anna et al. Journal of clinical medicine 2022 11(23)

Children's views on artificial intelligence and digital twins for the daily management of their asthma: a mixed-method study.
Gonsard Apolline et al. European journal of pediatrics 2022

Infectious Diseases

Ultrasound image intelligent diagnosis in community-acquired pneumonia of children using convolutional neural network-based transfer learning.
Fang Xiaohui et al. Frontiers in pediatrics 2022 101063587

Towards digital diagnosis of malaria: How far have we reached?
Aqeel Sana et al. Journal of microbiological methods 2022 204106630

Artificial intelligence (AI): a new window to revamp the vector-borne disease control.
Nayak Basudev et al. Parasitology research 2022


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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