| Abstract |
Background: Late gadolinium enhancement (LGE) of the myocardium has significant diagnostic and prognostic implications, with even small areas of enhancement being important. Distinguishing between definitely normal and definitely abnormal LGE images is usually straightforward; but diagnostic uncertainty arises when reporters are not sure whether the observed LGE is genuine or not. This uncertainty might be resolved by repetition (to remove artefact) or further acquisition of intersecting images, but this must take place before the scan finishes. Real-time quality assurance by humans is a complex task requiring training and experience, so being able to identify which images have an intermediate likelihood of LGE while the scan is ongoing, without the presence of an expert is of high value. This decision-support could prompt immediate image optimisation or acquisition of supplementary images to confirm or refute the presence of genuine LGE. This could reduce ambiguity in reports.
Methods: Short-axis, phase sensitive inversion recovery (PSIR) late gadolinium images were extracted from our clinical CMR database and shuffled. Two, independent, blinded experts scored each individual slice for 'LGE likelihood' on a visual analogue scale, from 0 (absolute certainty of no LGE) to 100 (absolute certainty of LGE), with 50 representing clinical equipoise. The scored images were split into 2 classes - either "high certainty" of whether LGE was present or not, or "low certainty". The dataset was split into training, validation and test sets (70:15:15). A deep learning binary classifier based on the EfficientNetV2 convolutional neural network architecture was trained to distinguish between these categories. Classifier performance on the test set was evaluated by calculating the accuracy, precision, recall, F1-score, and area under the receiver operating characteristics curve (ROC AUC). Performance was also evaluated on an external test set of images from a different centre.
Results: 1645 images (from 272 patients) were labelled and split at the patient level into training (1151 images), validation (247 images) and test (247 images) sets for the deep learning binary classifier. Of these, 1208 images were 'high certainty' (255 for LGE, 953 for no LGE), and 437 were 'low certainty'). An external test comprising 247 images from 41 patients from another centre was also employed. After 100 epochs the performance on the internal test set was: accuracy = 94%, recall = 0.80, precision = 0.97, F1-score = 0.87 and ROC AUC = 0.94. The classifier also performed robustly on the external test set (accuracy = 91%, recall = 0.73, precision = 0.93, F1-score = 0.82 and ROC AUC = 0.91). These results were benchmarked against a reference inter-expert accuracy of 86%.
Conclusions: Deep learning shows potential to automate quality control of late gadolinium imaging in CMR. The ability to identify short-axis images with intermediate LGE likelihood in real-time may serve as a useful decision support tool. This approach has the potential to guide immediate further imaging while the patient is still in the scanner, thereby reducing the frequency of recalls and inconclusive reports due to diagnostic indecision.
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