Iyengar, Srikanth

Abstract Objectives Minimally invasive cardiac surgery (MICS) offers significant advantages over conventional sternotomy, including reduced trauma and faster recovery. Preoperative planning is crucial, with computed tomography (CT) emerging as an essential imaging tool. This systematic review aims to evaluate CT’s role in MICS planning, focusing on imaging protocols, surgical guidance, and patient outcomes. Methods This study included studies published between January 2003 and January 2025 that examined the role of CT imaging in planning minimally invasive valvular heart surgery. A comprehensive search was conducted in Medical Literature Analysis and Retrieval System Online (MEDLINE), Cumulative Index to Nursing and Allied Health Literature (CINAHL), and Excerpta Medica Database (EMBASE) using predefined keywords and medical subject headings (MeSH) terms related to CT imaging, angiography, minimally invasive surgery, robotic surgery, aortic valve surgery, and mitral valve surgery. Results Initial search found 378 studies, and 17 studies were included in the review. The studies highlight CT’s value in assessing surgical feasibility, vascular access, and risk stratification. CT aids in selecting arterial cannulation sites, guiding aortic occlusion strategies, and optimizing port placement. It also identifies contraindications, such as extensive atherosclerosis and anatomical abnormalities, that may necessitate sternotomy. Conclusions Current evidence underscores CT’s critical role in optimizing MICS planning and enhancing patient safety.

To evaluate deep learning-based calcium segmentation and quantification on ECG-gated cardiac CT scans compared with manual evaluation. Automated calcium quantification was performed using a neural network based on mask regions with convolutional neural networks (R-CNNs) for multi-organ segmentation. Manual evaluation of calcium was carried out using proprietary software. This is a retrospective study of archived data. This study used 40 patients to train the segmentation model and 110 patients were used for the validation of the algorithm. The Pearson correlation coefficient between the reference actual and the computed predictive scores shows high level of correlation (0.84; P < .001) and high limits of agreement (±1.96 SD; −2000, 2000) in Bland–Altman plot analysis. The proposed method correctly classifies the risk group in 75.2% and classifies the subjects in the same group. In total, 81% of the predictive scores lie in the same categories and only seven patients out of 110 were more than one category off. For the presence/absence of coronary artery calcifications, the deep learning model achieved a sensitivity of 90% and a specificity of 94%. Fully automated model shows good correlation compared with reference standards. Automating process reduces evaluation time and optimizes clinical calcium scoring without additional resources.