Quality assurance of late gadolinium enhancement cardiac magnetic resonance images: a deep learning classifier for confidence in the presence or absence of abnormality with potential to prompt real-time image optimization.

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 artifact) 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 optimization 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 late gadolinium images were extracted from our clinical cardiac magnetic resonance (CMR) database and shuffled. Two, independent, blinded experts scored each individual slice for "LGE likelihood" on a visual analog 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 two 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 center. RESULTS: One thousand six hundred and forty-five images (from 272 patients) were labeled 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 center was also employed. After 100 epochs, the performance on the internal test set was accuracy = 0.94, 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 = 0.91, 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 0.86. CONCLUSION: 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.

Aerosolised fluorescein can quantify FFP mask faceseal leakage: a cost-effective adaptation to the existing point of care fit test.

BACKGROUND: A qualitative fit test using bitter-tasting aerosols is the commonest way to determine filtering face-piece (FFP) mask leakage. This taste test is subjective and biased by placebo. We propose a cheap, quantitative modification of the taste test by measuring the amount of fluorescein stained filter paper behind the mask using image analysis. METHODS: A bitter-tasting fluorescein solution was aerosolised during mask fit tests, with filter paper placed on masks' inner surfaces. Participants reported whether they could taste bitterness to determine taste test 'pass' or 'fail' results. Filter paper photographs were digitally analysed to quantify total fluorescence (TF). RESULTS: Fifty-six healthcare professionals were fit tested; 32 (57%) 'passed' the taste test. TF between the taste test 'pass' and 'fail' groups was significantly different (p<0.001). A cut-off (TF = 5.0 × 106 units) was determined at precision (78%) and recall (84%), resulting in 5/56 participants (9%) reclassified from 'pass' to 'fail' by the fluorescein test. Seven out of 56 (12%) reclassified from 'fail' to 'pass'. CONCLUSION: Fluorescein is detectable and sensitive at identifying FFP mask leaks. These low-cost adaptations can enhance exiting fit testing to determine 'pass' and 'fail' groups, protecting those who 'passed' the taste test but have high fluorescein leak, and reassuring those who 'failed' the taste test despite having little fluorescein leak.

Arrhythmia Mechanisms Revealed by Ripple Mapping.

Ripple mapping is a novel method of 3D intracardiac electrogram visualisation that allows activation of the myocardium to be tracked visually without prior assignment of local activation times and without interpolation into unmapped regions. It assists in the identification of tachycardia mechanism and optimal ablation site, without the need for an experienced computer-operating assistant. This expert opinion presents evidence demonstrating the benefit of Ripple Mapping, compared with traditional electroanatomic mapping techniques, for the diagnosis and management of atrial and ventricular tachyarrhythmias during electrophysiological procedures.