Key result
Automated deep learning reduces LV cavity volume estimation error by ~38% versus common practice.
Why the study?
Does an automated deep learning and Gaussian process method improve the estimation of left ventricular cavity volume from CMR data compared to common practice?
Does an automated deep learning and Gaussian process method improve the estimation of left ventricular cavity volume from CMR data compared to common practice?
Absolute Event Rate: 8% vs 13%
A fully automated deep learning and Gaussian process method can estimate left ventricular cavity volume from CMR with an RMSE of 8 ml, approaching the 4 ml error of manual measurements.
May reduce automated CMR LV volume errors; leaves open clinical impact pending prospective validation.
In this investigation, an image-based method has been developed to estimate the volume of the left ventricular cavity using cardiac magnetic resonance (CMR) imaging data. Deep learning and Gaussian processes have been applied to bring the estimations closer to the cavity volumes manually extracted. CMR data from 339 patients and healthy volunteers have been used to train a stepwise regression model that can estimate the volume of the left ventricular cavity at the beginning and end of diastole. We have decreased the root mean square error (RMSE) of cavity volume estimation approximately from 13 to 8 ml compared to the common practice in the literature. Considering the RMSE of manual measurements is approximately 4 ml on the same dataset, 8 ml of error is notable for a fully automated estimation method, which needs no supervision or user-hours once it has been trained. Additionally, to demonstrate a clinically relevant application of automatically estimated volumes, we inferred the passive material properties of the myocardium given the volume estimates using a well-validated cardiac model. These material properties can be further used for patient treatment planning and diagnosis.
No takes yet. Share an insight, caveat, or question.
Rabbani et al. (2023) studied Patients and healthy volunteers (n=339). Deep learning and Gaussian process-based automated volume estimation vs. Common practice in the literature was evaluated on Root mean square error (RMSE) of cavity volume estimation. A fully automated deep learning and Gaussian process method decreased the root mean square error of left ventricular cavity volume estimation from 13 ml to 8 ml compared to common practice.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: