ArticleClinical cardiology2022
A deep neural network using audio files for detection of aortic stenosis.
Article in Clinical cardiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses 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
8 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Unimodal to multimodal: a systematic review of predictive machine learning models for valvular heart diseases.Frontiers in cardiovascular medicine · 2026Pooled it
- Diagnostic Accuracy of AI Algorithms in Aortic Stenosis Screening: A Systematic Review and Meta-Analysis.Clinical medicine & research · 2024Pooled it
- Severe aortic stenosis detection using seismocardiography.Open heart · 2026Article
- Cardiac murmur grading and risk analysis of cardiac diseases based on adaptable heterogeneous-modality multi-task learning.Health information science and systems · 2024Article
- Severe aortic stenosis detection by deep learning applied to echocardiography.European heart journal · 2023Article
- What Is Machine Learning, Artificial Neural Networks and Deep Learning?-Examples of Practical Applications in Medicine.Diagnostics (Basel, Switzerland) · 2023Review
- Audiological Diagnosis of Valvular and Congenital Heart Diseases in the Era of Artificial Intelligence.Reviews in cardiovascular medicine · 2023Review
- A deep neural network using audio files for detection of aortic stenosis.Clinical cardiology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAlthough aortic stenosis (AS) is the most common valvular heart disease in the western world, many affected patients remain undiagnosed. Auscultation is a readily available screening tool for AS. However, it requires a high level of professional expertise. HYPOTHESIS: An AI algorithm can detect AS using audio files with the same accuracy as experienced cardiologists.
methodsA deep neural network (DNN) was trained by preprocessed audio files of 100 patients with AS and 100 controls. The DNN's performance was evaluated with a test data set of 40 patients. The primary outcome measures were sensitivity, specificity, and F1-score. Results of the DNN were compared with the performance of cardiologists, residents, and medical students.
resultsEighteen percent of patients without AS and 22% of patients with AS showed an additional moderate or severe mitral regurgitation. The DNN showed a sensitivity of 0.90 (0.81-0.99), a specificity of 1, and an F1-score of 0.95 (0.89-1.0) for the detection of AS. In comparison, we calculated an F1-score of 0.94 (0.86-1.0) for cardiologists, 0.88 (0.78-0.98) for residents, and 0.88 (0.78-0.98) for students.
conclusionsThe present study shows that deep learning-guided auscultation predicts significant AS with similar accuracy as cardiologists. The results of this pilot study suggest that AI-assisted auscultation may help general practitioners without special cardiology training in daily practice.
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.