Motivation: The price of medical treatment continues to rise due to (i) an increasing population; (ii) an aging human growth; (iii) disease prevalence; (iv) a rise in the frequency of patients that utilize health care services; and (v) increase in the price. Objective: Artificial Intelligence (AI) is already well-known for its superiority in various healthcare applications, including the segmentation of lesions in images, speech recognition, smartphone personal assistants, navigation, ride-sharing apps, and many more. Our study is based on two hypotheses: (i) AI offers more economic solutions compared to conventional methods; (ii) AI treatment offers stronger economics compared to AI diagnosis. This novel study aims to evaluate AI technology in the context of healthcare costs, namely in the areas of diagnosis and treatment, and then compare it to the traditional or non-AI-based approaches. Methodology: PRISMA was used to select the best 200 studies for AI in healthcare with a primary focus on cost reduction, especially towards diagnosis and treatment. We defined the diagnosis and treatment architectures, investigated their characteristics, and categorized the roles that AI plays in the diagnostic and therapeutic paradigms. We experimented with various combinations of different assumptions by integrating AI and then comparing it against conventional costs. Lastly, we dwell on three powerful future concepts of AI, namely, pruning, bias, explainability, and regulatory approvals of AI systems. Conclusions: The model shows tremendous cost savings using AI tools in diagnosis and treatment. The economics of AI can be improved by incorporating pruning, reduction in AI bias, explainability, and regulatory approvals.

The Official Journal of EuroPCR and the European Association of Percutaneous Cardiovascular Interventions (EAPCI)

The IN.PACT Global Study is the largest prospective, multicenter, independently adjudicated trial to evaluate a paclitaxel drug-coated balloon in patients with lifestyle-limiting claudication and/or ischemic rest pain due to atherosclerotic disease of the femoropopliteal artery and includes complex lesions beyond what are typically included in randomized controlled trials. Randomized controlled trials have demonstrated the safety and efficacy of drug-coated balloons for the treatment of Trans-Atlantic Inter-Society Consensus Document II A and B lesions, but there is a need for large-scale prospective studies to evaluate a broader range of lesions. The IN.PACT Global Study enrolled 1,535 subjects, and 1,406 (1,773 lesions) were included in the pre-defined clinical cohort analysis. Freedom from clinically driven target lesion revascularization was evaluated at 24 months. The safety composite endpoint was freedom from device- and procedure-related death through 30 days and freedom from target limb major amputation and clinically driven target vessel revascularization within 24 months. Mean lesion length was 12.1 cm, 35.5% were total occlusions, and 18.0% had in-stent restenosis. Freedom from clinically driven target lesion revascularization at 24 months was 83.3%, the composite safety endpoint was met in 81.7%, the 2-year all-cause mortality rate was 7.0%, and the major target limb amputation rate was 0.7%. Increased lesion length and the presence of de novo in-stent restenosis or coronary artery disease were associated with increased risk for clinically driven target lesion revascularization by 24 months. This real-world study of femoropopliteal artery disease treatment with drug-coated balloons confirmed positive findings reported from more strictly designed randomized controlled trials and showed that outcomes are durable in this population up to 2 years after treatment. (IN.PACT Global Clinical Study; NCT01609296).

Background: The previous COVID-19 lung diagnosis system lacks both scientific validation and the role of explainable artificial intelligence (AI) for understanding lesion localization. This study presents a cloud-based explainable AI, the “COVLIAS 2.0-cXAI” system using four kinds of class activation maps (CAM) models. Methodology: Our cohort consisted of ~6000 CT slices from two sources (Croatia, 80 COVID-19 patients and Italy, 15 control patients). COVLIAS 2.0-cXAI design consisted of three stages: (i) automated lung segmentation using hybrid deep learning ResNet-UNet model by automatic adjustment of Hounsfield units, hyperparameter optimization, and parallel and distributed training, (ii) classification using three kinds of DenseNet (DN) models (DN-121, DN-169, DN-201), and (iii) validation using four kinds of CAM visualization techniques: gradient-weighted class activation mapping (Grad-CAM), Grad-CAM++, score-weighted CAM (Score-CAM), and FasterScore-CAM. The COVLIAS 2.0-cXAI was validated by three trained senior radiologists for its stability and reliability. The Friedman test was also performed on the scores of the three radiologists. Results: The ResNet-UNet segmentation model resulted in dice similarity of 0.96, Jaccard index of 0.93, a correlation coefficient of 0.99, with a figure-of-merit of 95.99%, while the classifier accuracies for the three DN nets (DN-121, DN-169, and DN-201) were 98%, 98%, and 99% with a loss of ~0.003, ~0.0025, and ~0.002 using 50 epochs, respectively. The mean AUC for all three DN models was 0.99 (p < 0.0001). The COVLIAS 2.0-cXAI showed 80% scans for mean alignment index (MAI) between heatmaps and gold standard, a score of four out of five, establishing the system for clinical settings. Conclusions: The COVLIAS 2.0-cXAI successfully showed a cloud-based explainable AI system for lesion localization in lung CT scans.