Key result
Deep learning detects ~80% of end-diastolic frames in NIRS-IVUS images, beating conventional methods and experts.
Why the study?
Coronary luminal dimensions change during the cardiac cycle, but contemporary volumetric IVUS analysis uses non-gated images because existing methods to acquire or retrospectively gate IVUS images have failed to dominate.
Does a deep learning methodology improve the automated detection of end-diastolic frames in IVUS images compared to expert analysts and conventional methods?
Does a deep learning methodology improve the automated detection of end-diastolic frames in IVUS images compared to expert analysts and conventional methods?
Absolute Event Rate: 80.4% vs 42.8%
p-value: p=<0.05
A novel deep learning methodology significantly outperforms expert analysts and conventional image-based methods in accurately detecting end-diastolic frames in NIRS-IVUS images.
DL outperforms experts and conventional methods for ED frame detection; supports further development but should not yet change practice.
Coronary luminal dimensions change during the cardiac cycle. However, contemporary volumetric intravascular ultrasound (IVUS) analysis is performed in non-gated images as existing methods to acquire gated or to retrospectively gate IVUS images have failed to dominate in research. We developed a novel deep learning (DL)-methodology for end-diastolic frame detection in IVUS and compared its efficacy against expert analysts and a previously established methodology using electrocardiographic (ECG)-estimations as reference standard. Near-infrared spectroscopy-IVUS (NIRS-IVUS) data were prospectively acquired from 20 coronary arteries and co-registered with the concurrent ECG-signal to identify end-diastolic frames. A DL-methodology which takes advantage of changes in intensity of corresponding pixels in consecutive NIRS-IVUS frames and consists of a network model designed in a bidirectional gated-recurrent-unit (Bi-GRU) structure was trained to detect end-diastolic frames. The efficacy of the DL-methodology in identifying end-diastolic frames was compared with two expert analysts and a conventional image-based (CIB)-methodology that relies on detecting vessel movement to estimate phases of the cardiac cycle. A window of ± 100 ms from the ECG estimations was used to define accurate end-diastolic frames detection. The ECG-signal identified 3,167 end-diastolic frames. The mean difference between DL and ECG estimations was 3 ± 112 ms while the mean differences between the 1st-analyst and ECG, 2nd-analyst and ECG and CIB-methodology and ECG were 86 ± 192 ms, 78 ± 183 ms and 59 ± 207 ms, respectively. The DL-methodology was able to accurately detect 80.4%, while the two analysts and the CIB-methodology detected 39.0%, 43.4% and 42.8% of end-diastolic frames, respectively (P < 0.05). The DL-methodology can identify NIRS-IVUS end-diastolic frames accurately and should be preferred over expert analysts and CIB-methodologies, which have limited efficacy.
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Bajaj et al. (2021) studied Coronary artery disease. Deep learning (DL) methodology vs. Expert analysts and conventional image-based (CIB) methodology was evaluated on Accurate detection of end-diastolic frames (within ± 100 ms of ECG estimations) (p=<0.05). A deep learning methodology accurately detected 80.4% of end-diastolic frames in NIRS-IVUS images compared to 42.8% with conventional image-based methods and 39.0-43.4% by expert analysts (P<0.05).
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