Skip directly to search Skip directly to A to Z list Skip directly to navigation Skip directly to page options Skip directly to site content


Genomics & Precision Health Database|Non-Genomics Precision Health Update Archive|Public Health Genomics and Precision Health Knowledge Base (PHGKB) Published on 04/15/2021

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

Archived Editions

Search Precision Health database

Visit CDC Office of Public Health Genomics website

Non-Genomics Precision Health Scan

Cancer

[Artificial intelligence in the diagnosis of prostate cancer].
Popov G V et al. Arkhiv patologii 2021 83(3) 38-45

Predicting the clinical management of skin lesions using deep learning.
Abhishek Kumar et al. Scientific reports 2021 11(1) 7769

Machine Learning for Early Lung Cancer Identification Using Routine Clinical and Laboratory Data.
Gould Michael K et al. American journal of respiratory and critical care medicine 2021

Most lung cancers are diagnosed at an advanced stage. Pre-symptomatic identification of high-risk individuals can prompt earlier intervention and improve long-term outcomes. To develop a model to predict a future diagnosis of lung cancer based on routine clinical and laboratory data, using machine-learning. We assembled 6,505 non-small cell lung cancer (NSCLC) cases and 189,597 contemporaneous controls and compared the accuracy of a novel machine-learning model to a modified version of the well-validated PLCOm2012 risk model.

Putting Cancer Data in the Fast Lane
CDC Cancer Blog, April 2021

CDC looks for ways to help central cancer registries speed up process of data collection. One important way is to replace the individual This new cloud-based computer system will make the information available almost as soon as it’s entered. You—and your doctor and researchers—will know how cancer rates changed in your area last year, instead of 2 or 3 years ago. That information can help save lives.

Impact of real-time use of artificial intelligence in improving adenoma detection during colonoscopy: A systematic review and meta-analysis.
Ashat Munish et al. Endoscopy international open 2021 9(4) E513-E521

Prediction of histologic grade and type of small (< 4 cm) papillary renal cell carcinomas using texture and neural network analysis: a feasibility study.
Haji-Momenian Shawn et al. Abdominal radiology (New York) 2021

High precision localization of pulmonary nodules on chest CT utilizing axial slice number labels.
Chillakuru Yeshwant Reddy et al. BMC medical imaging 2021 21(1) 66

Chronic Disease

The Application of a Machine Learning-Based Brain Magnetic Resonance Imaging Approach in Major Depression.
Na Kyoung-Sae et al. Advances in experimental medicine and biology 2021 130557-69

Medical records-based chronic kidney disease phenotype for clinical care and “big data” observational and genetic studies
N Shang et al, NPJ Digital Medicine, April 13, 2021

Chronic Kidney Disease (CKD) represents a slowly progressive disorder that is typically silent until late stages, but early intervention can significantly delay its progression. We designed a portable and scalable electronic CKD phenotype to facilitate early disease recognition and empower large-scale observational studies of kidney traits. The algorithm uses a combination of rule-based and machine-learning methods.

[A Preliminary Study of Applying Geometric Deep Learning in Brain Morphometry for Diagnosis of Alzheimer's Disease].
Xie Wei et al. Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition 2021 52(2) 300-305

Ethical, Legal and Social Issues (ELSI)

Clinical applications of AI in MSK imaging: a liability perspective.
Harvey H Benjamin et al. Skeletal radiology 2021

Artificial Intelligence and Liability in Medicine: Balancing Safety and Innovation.
Maliha George et al. The Milbank quarterly 2021

Artificial intelligence and multi agent based distributed ledger system for better privacy and security of electronic healthcare records.
Alruwaili Fahad F et al. PeerJ. Computer science 2021 6e323

General Practice

Voice-Controlled Intelligent Personal Assistants in Health Care: International Delphi Study.
Ermolina Alena et al. Journal of medical Internet research 2021 23(4) e25312

Coming to Terms with the Black Box Problem: How to Justify AI Systems in Health Care.
Felder Ryan Marshall et al. The Hastings Center report 2021

Current status of clinical research using artificial intelligence techniques: A registry-based audit.
Karekar Sonali Rajiv et al. Perspectives in clinical research 2021 12(1) 48-52

Artificial intelligence in managing clinical trial design and conduct: Man and machine still on the learning curve?
Bhatt Arun et al. Perspectives in clinical research 2021 12(1) 1-3

Predicting need for hospital admission in patients with traumatic brain injury or skull fractures identified on CT imaging: a machine learning approach.
Marincowitz Carl et al. Emergency medicine journal : EMJ 2021

Artificial intelligence in healthcare: opportunities and risk for future.
Sunarti Sri et al. Gaceta sanitaria 2021 35 Suppl 1S67-S70

Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis.
Aggarwal Ravi et al. NPJ digital medicine 2021 4(1) 65

Understanding the role of digital platforms in technology readiness.
Halamka John et al. Regenerative medicine 2021

Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis
R Aggarwal et al, NPJ Digital Medicine, April 7, 2021

Heterogeneity was high between studies and extensive variation in methodology, terminology and outcome measures was noted. This can lead to an overestimation of the diagnostic accuracy of DL algorithms on medical imaging. There is an immediate need for the development of artificial intelligence-specific EQUATOR guidelines, particularly STARD, in order to provide guidance around key issues in this field.

Heart, Lung, Blood and Sleep Diseases

Biosensing human blood clotting factor by dual probes: Evaluation by deep LSTM networks in time series forecasting.
Gopinath Subash C B et al. Biotechnology and applied biochemistry 2021

Development, validation, and proof-of-concept implementation of a two-year risk prediction model for undiagnosed atrial fibrillation using common electronic health data (UNAFIED).
Grout Randall W et al. BMC medical informatics and decision making 2021 21(1) 112

Improving patient identification for advanced cardiac imaging through machine learning-integration of clinical and coronary CT angiography data.
Benjamins Jan Walter et al. International journal of cardiology 2021

Noninvasive Hemoglobin Level Prediction in a Mobile Phone Environment: State of the Art Review and Recommendations.
Hasan Md Kamrul et al. JMIR mHealth and uHealth 2021 9(4) e16806

Machine learning for subtype definition and risk prediction in heart failure, acute coronary syndromes and atrial fibrillation: systematic review of validity and clinical utility.
Banerjee Amitava et al. BMC medicine 2021 19(1) 85

Infectious Diseases

Health information technology interventions and engagement in HIV care and achievement of viral suppression in publicly funded settings in the US: A cost-effectiveness analysis.
Shade Starley B et al. PLoS medicine 2021 18(4) e1003389

Prediction of vancomycin dose on high-dimensional data using machine learning techniques.
Huang Xiaohui et al. Expert review of clinical pharmacology 2021 1-11

Otitis media detection using tympanic membrane images with a novel multi-class machine learning algorithm.
Alhudhaif Adi et al. PeerJ. Computer science 2021 7e405


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.
TOP