MAE: Mean absolute error, MACE: Major adverse cardiac events
Bai et al. showed that Fully Convolution Network (FCN) matches expert like performance in speed, scalability and accuracy over CV magnetic resonant (CMR) image segmentation and clinical measurements [127]. In another study on fatty liver disease (FLD) tissue characterization, Kuppili et al. [131] observed accuracy of 96.75% using SymtosisTM, an extreme learning machine (ELM). Further, Biswas et al. [130] developed a DL-based system and exhibited 100% accuracy on a database of 63 samples. Meanwhile, Hemalatha et al. [132] used DL for analysing different grades of synovitis. The study observed an increase in false positives, a decrease in false negatives and an average accuracy of \(95.02\%\ \pm 2.78\) in 276 images. Wang et al. developed a CNN-based detection of calcification using mammograms and achieved human-like perfection [134]. Google used DL to analyse retinal fundus images and predicted multiple cardiovascular risk factors with good accuracy [133]. The team observed that retinal fundus images could accurately predict cardiovascular diseases and found method comparable to traditional risk calculators. Benchmarking studies and outcomes are summarized in Table III. Further, the favourable outcomes inspire the application of DL in stiffness computation.