ReviewmHealth2017
Deep learning for cardiac computer-aided diagnosis: benefits, issues & solutions.
Review in mHealth, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Advances in machine and deep learning for ECG beat classification: a systematic review.Frontiers in digital health · 2025Pooled it
- Artificial intelligence-powered coronary artery disease diagnosis from SPECT myocardial perfusion imaging: a comprehensive deep learning study.European journal of nuclear medicine and molecular imaging · 2025Article
- Artificial intelligence in aortic CT angiography: current applications and future perspectives.Frontiers in cardiovascular medicine · 2025Review
- Predicting physical functioning status in older adults: insights from wrist accelerometer sensors and derived digital biomarkers of physical activity.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- A Systematic Review of the Barriers to the Implementation of Artificial Intelligence in Healthcare.Cureus · 2023Review
- Heartbeat Classification and Arrhythmia Detection Using a Multi-Model Deep-Learning Technique.Sensors (Basel, Switzerland) · 2022Article
- Advanced Ultrasound and Photoacoustic Imaging in Cardiology.Sensors (Basel, Switzerland) · 2021Review
- Automatic Evaluation of Heart Condition According to the Sounds Emitted and Implementing Six Classification Methods.Healthcare (Basel, Switzerland) · 2021Article
- A machine learning cardiac magnetic resonance approach to extract disease features and automate pulmonary arterial hypertension diagnosis.European heart journal. Cardiovascular Imaging · 2021Article
- Deep Neural Network-Aided Histopathological Analysis of Myocardial Injury.Frontiers in cardiovascular medicine · 2021Article
- How the Smartphone Is Changing Allergy Diagnostics.Current allergy and asthma reports · 2018Review
- Attention-assisted hybrid CNN-BILSTM-BiGRU model with SMOTE-Tomek method to detect cardiac arrhythmia based on 12Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cardiovascular diseases are one of the top causes of deaths worldwide. In developing nations and rural areas, difficulties with diagnosis and treatment are made worse due to the deficiency of healthcare facilities. A viable solution to this issue is telemedicine, which involves delivering health care and sharing medical knowledge at a distance. Additionally, mHealth, the utilization of mobile devices for medical care, has also proven to be a feasible choice. The integration of telemedicine, mHealth and computer-aided diagnosis systems with the fields of machine and deep learning has enabled the creation of effective services that are adaptable to a multitude of scenarios. The objective of this review is to provide an overview of heart disease diagnosis and management, especially within the context of rural healthcare, as well as discuss the benefits, issues and solutions of implementing deep learning algorithms to improve the efficacy of relevant medical applications.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.