Effect of image quality on automated evaluation of the left-sided heart volumes
Bibliographic record
Abstract
Abstract Introduction The accuracy of echocardiography interpretations is crucial. However, echocardiography has varying degrees of image quality, which can impact diagnostic decision-making. Automated chamber quantification of the left ventricle (LV) and left atrium (LA) has been shown to be feasible using machine learning (ML) systems. However, the effect of image quality on the performance of these ML systems has not been investigated. Purpose To assess the effect of different echocardiographic image quality on the performance of left-sided heart quantification using an ML system. Methods A multi-center study enrolled 240 patients who underwent stress echocardiography across five centers. The acquired rest phase images were anonymized for subsequent analysis. Expert cardiologists reviewed the studies, selecting apical four-chamber (4Ch) and two-chamber (2Ch) view images. The cardiologists identified end-systolic (ES) and end-diastolic (ED) frames. They traced the endocardial borders for the LA in ES and the LV in ED and ES. The same ED and ES frames were then processed by an ML algorithm trained on a separate set of images. Subsequently, an expert cardiologist, blinded to the previous measurements, assessed the image quality of LA and LV. The image quality for each chamber in the ES or ED frame was categorized into one of four grades: poor, suboptimal, fair, and optimal. Agreement analysis between human and AI measurements was performed within each image quality group. Yield was defined as the percentage of studies where the ML system predicted an echocardiographic parameter compared to a cardiologist. Results The yield of study parameters varied significantly with image quality. The lowest yield was 45%, observed for LV end-diastolic volume in the 2Ch view when image quality was poor. In contrast, the yield of 100% was seen for LV end-diastolic volumes in both the 4Ch and 2Ch views for optimal quality images. The correlation between automated and expert cardiologist measurements of left heart volumes varied depending on image quality. For poor or suboptimal images, the Pearson correlation coefficient ranged from 0.66 to 0.91 and improved to 0.85-0.92 in fair and optimal-quality images. Additionally, there was an inverse relation between agreement metrics (bias, limits of agreement, and coefficient of variation) and improvements in image quality. However, this tendency was less pronounced or nonexistent when comparing fair and optimal-quality images. (Table 1). Conclusions Image quality affects the yield of automated analysis and the agreement between cardiologists and automated measurements in echocardiography, highlighting the need for high-quality image acquisition in use with ML systems. Table 1
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".