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Investigating the effect of clinical history before electrocardiogram interpretation on the visual behavior and interpretation accuracy of clinicians.

ABSTRACT: We examine the impact of the presentation of a patient's clinical history on subsequent visual appraisal and interpretation accuracy of electrocardiograms (ECGs). Healthcare-practitioners (N = 31) skilled in 12-lead ECG interpretation took part in a repeated-measures experiment with counterbalancing viewing 9 ECGs on a computer screen in two separate conditions: with/without an associated patient-history. A Hellinger-distance calculation was applied using a permutation test to eye-movement transitions at two granularity levels: between the ECG leads, and between smaller grid-cells, whose size was determined via data-driven clustering of the fixation points. Findings indicate that presentation of clinical-history does affect accuracy of interpretation in one ECG. Visual-behavior differed as a function of both history presentation and accuracy when considering transitions between the data-driven grid units (using a fine granularity, and able to show attention to parts of the waveform). Differences in visual-behavior at waveform level demonstrate an influence of patient-history and expertise that are not detected at the lead level. Visual-behaviour differs according to whether a patient-history is presented, and whether a clinician provides an accurate interpretation. This difference is evident in how the waveform itself is viewed, and is less present at the coarse granularity of visual transitions between leads. To understand how clinicians interpret ECGs, and potentially other medical images, visual transitions should be considered at a fine level of granularity, determined in a data-driven fashion.

SUBMITTER: Davies A

PROVIDER: S-EPMC6683299 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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Investigating the effect of clinical history before electrocardiogram interpretation on the visual behavior and interpretation accuracy of clinicians.

Davies Alan A Harper Simon S Vigo Markel M Jay Caroline C

Scientific reports 20190805 1

We examine the impact of the presentation of a patient's clinical history on subsequent visual appraisal and interpretation accuracy of electrocardiograms (ECGs). Healthcare-practitioners (N = 31) skilled in 12-lead ECG interpretation took part in a repeated-measures experiment with counterbalancing viewing 9 ECGs on a computer screen in two separate conditions: with/without an associated patient-history. A Hellinger-distance calculation was applied using a permutation test to eye-movement trans ...[more]

PMID: 31383896

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Project description:BackgroundThe 12-lead electrocardiogram (ECG) remains a cornerstone of cardiac diagnostics, yet existing artificial intelligence (AI) solutions for automated interpretation often lack generalizability, remain closed-source, and are primarily trained using supervised learning, limiting their adaptability across diverse clinical settings. To address these challenges, we developed and compared two open-source foundational ECG models: DeepECG-SSL, a self-supervised learning model, and DeepECG-SL, a supervised learning model.MethodsBoth models were trained on over 1 million ECGs using a standardized preprocessing pipeline and automated free-text extraction from ECG reports to predict 77 cardiac conditions. DeepECG-SSL was pretrained using self-supervised contrastive learning and masked lead modeling. The models were evaluated on six multilingual private healthcare systems and four public datasets for ECG interpretation across 77 diagnostic categories. Fairness analyses assessed disparities in performance across age and sex groups, while also investigating fairness and resource utilization.ResultsDeepECG-SSL achieved AUROCs of 0.990 (95%CI 0.990, 0.990) on internal dataset, 0.981 (95%CI 0.981, 0.981) on external public datasets, and 0.983 (95%CI 0.983, 0.983) on external private datasets, while DeepECG-SL demonstrated AUROCs of 0.992 (95%CI 0.992, 0.992), 0.980 (95%CI 0.980, 0.980) and 0.983 (95%CI 0.983, 0.983) respectively. Fairness analyses revealed minimal disparities (true positive rate & false positive rate difference<0.010) across age and sex groups. Digital biomarker prediction (Long QT syndrome (LQTS) classification, 5-year atrial fibrillation prediction and left ventricular ejection fraction (LVEF) classification) with limited labeled data, DeepECG-SSL outperformed DeepECG-SL in predicting 5-year atrial fibrillation risk (N=132,050; AUROC 0.742 vs. 0.720; Δ=0.022; P<0.001), identifying reduced LVEF ≤40% (N=25,252; 0.928 vs. 0.900; Δ=0.028; P<0.001), and classifying LQTS syndrome subtypes (N=127; 0.931 vs. 0.853; Δ=0.078; P=0.026).ConclusionBy releasing model weights, preprocessing tools, and validation code, we aim to support robust, data-efficient AI diagnostics across diverse clinical environments. This study establishes self-supervised learning as a promising paradigm for ECG analysis, particularly in settings with limited annotated data, enhancing accessibility, generalizability, and fairness in AI-driven cardiac diagnostics.

Dataset Information

Investigating the effect of clinical history before electrocardiogram interpretation on the visual behavior and interpretation accuracy of clinicians.

Publications

Investigating the effect of clinical history before electrocardiogram interpretation on the visual behavior and interpretation accuracy of clinicians.

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