Ethical Considerations in the Advent of 3D Printing Technology in Healthcare

The emergence of 3D printing technology in healthcare has ushered in a new era of personalized medical solutions. However, alongside its promises, this technology also introduces several critical challenges that demand attention. This research investigates the implications of 3D printing on patient safety, intellectual property, equity, data security, informed consent, and the roles of healthcare professionals. 3D printing has opened up remarkable opportunities in the creation of medical devices, implants, and prosthetics. Nevertheless, the potential for errors during the manufacturing process poses a significant concern. Ensuring the safety and reliability of 3D-printed medical products becomes paramount, as any defects or inaccuracies could have severe consequences on patient health and well-being. The accessibility of 3D printing technology raises apprehensions regarding intellectual property rights and regulatory standards. The possibility of replicating medical devices and pharmaceuticals may lead to patent infringements and pose difficulties in enforcing regulatory compliance. Striking a balance between innovation and protection of intellectual property becomes crucial in fostering a thriving 3D printing healthcare ecosystem. While 3D printing holds to democratize healthcare by offering personalized medical solutions, it also has the potential to exacerbate existing disparities in healthcare access. The cost of 3D printing technology and related services might prove prohibitive for certain communities, thereby widening the gap in access to advanced medical treatments. Addressing these disparities and ensuring equitable access to 3D printing healthcare solutions must be a priority for healthcare policymakers and stakeholders. The integration of 3D printing in healthcare necessitates the utilization and storage of sensitive patient data. However, ethical concerns emerge around the security and privacy of this data. Any breaches or misuse of patient information could not only compromise patient confidentiality but also erode trust in healthcare systems. Implementing robust data security measures and respecting patient privacy rights are essential to maintain public trust in 3D printing healthcare applications. As 3D printing enables the production of custom medical devices and implants, obtaining informed consent from patients becomes increasingly complex. Patients must comprehend the risks, benefits, and uncertainties associated with these personalized treatments to make autonomous decisions about their healthcare. Healthcare providers must develop comprehensive strategies to ensure adequate patient education and empowerment during the informed consent process

NS-HGlio: A generalizable and repeatable HGG segmentation and volumetric measurement AI algorithm for the longitudinal MRI assessment to inform RANO in trials and clinics

BACKGROUND Accurate and repeatable measurement of high-grade glioma (HGG) enhancing (Enh.) and T2/FLAIR hyperintensity/edema (Ed.) is required for monitoring treatment response. 3D measurements can be used to inform the modified Response Assessment in Neuro-oncology criteria. We aim to develop an HGG volumetric measurement and visualization AI algorithm that is generalizable and repeatable. METHODS A single 3D-Convoluted Neural Network, NS-HGlio, to analyze HGG on MRIs using 5-fold cross validation was developed using retrospective (557 MRIs), multicentre (38 sites) and multivendor (32 scanners) dataset divided into training (70%), validation (20%), and testing (10%). Six neuroradiologists created the ground truth (GT). Additional Internal validation (IV, three institutions) using 70 MRIs, and External validation (EV, single institution) using 40 MRIs through measuring the Dice Similarity Coefficient (DSC) of Enh., Ed. ,and Enh. + Ed. (WholeLesion/WL) tumor tissue and repeatability testing on 14 subjects from the TCIA MGH-QIN-GBM dataset using volume correlations between timepoints were performed. RESULTS IV Preoperative median DSC Enh. 0.89 (SD 0.11), Ed. 0.88 (0.28), WL 0.88 (0.11). EV Preoperative median DSC Enh. 0.82 (0.09), Ed. 0.83 (0.11), WL 0.86 (0.06). IV Postoperative median DSC Enh. 0.77 (SD 0.20), Ed 0.78. (SD 0.09), WL 0.78 (SD 0.11). EV Postoperative median DSC Enh. 0.75 (0.21), Ed 0.74 (0.12), WL 0.79 (0.07). Repeatability testing; Intraclass Correlation Coefficient of 0.95 Enh. and 0.92 Ed. CONCLUSION NS-HGlio is accurate, repeatable, and generalizable. The output can be used for visualization, documentation, treatment response monitoring, radiation planning, intra-operative targeting, and estimation of Residual Tumor Volume among others

NS-HGlio: A generalizable and repeatable HGG segmentation and volumetric measurement AI algorithm for the longitudinal MRI assessment to inform RANO in trials and clinics.

BACKGROUND Accurate and repeatable measurement of high-grade glioma (HGG) enhancing (Enh.) and T2/FLAIR hyperintensity/edema (Ed.) is required for monitoring treatment response. 3D measurements can be used to inform the modified Response Assessment in Neuro-oncology criteria. We aim to develop an HGG volumetric measurement and visualization AI algorithm that is generalizable and repeatable. METHODS A single 3D-Convoluted Neural Network, NS-HGlio, to analyze HGG on MRIs using 5-fold cross validation was developed using retrospective (557 MRIs), multicentre (38 sites) and multivendor (32 scanners) dataset divided into training (70%), validation (20%), and testing (10%). Six neuroradiologists created the ground truth (GT). Additional Internal validation (IV, three institutions) using 70 MRIs, and External validation (EV, single institution) using 40 MRIs through measuring the Dice Similarity Coefficient (DSC) of Enh., Ed. ,and Enh. + Ed. (WholeLesion/WL) tumor tissue and repeatability testing on 14 subjects from the TCIA MGH-QIN-GBM dataset using volume correlations between timepoints were performed. RESULTS IV Preoperative median DSC Enh. 0.89 (SD 0.11), Ed. 0.88 (0.28), WL 0.88 (0.11). EV Preoperative median DSC Enh. 0.82 (0.09), Ed. 0.83 (0.11), WL 0.86 (0.06). IV Postoperative median DSC Enh. 0.77 (SD 0.20), Ed 0.78. (SD 0.09), WL 0.78 (SD 0.11). EV Postoperative median DSC Enh. 0.75 (0.21), Ed 0.74 (0.12), WL 0.79 (0.07). Repeatability testing; Intraclass Correlation Coefficient of 0.95 Enh. and 0.92 Ed. CONCLUSION NS-HGlio is accurate, repeatable, and generalizable. The output can be used for visualization, documentation, treatment response monitoring, radiation planning, intra-operative targeting, and estimation of Residual Tumor Volume among others

Preoperative Smoking Cessation Intervention: A Critical Appraisal of the Evidence With Practice Recommendations

Smoking is the single most important risk factor in the development of postoperative complications. Daily smoking increases the risk of postoperative complications by a factor of two to four. Smoking cessation preoperatively is beneficial in increasing rates of cessation and therefore reducing the incidence of complications postoperatively. As a result, smoking cessation should be recognized as a core element of care for the preoperative management of the surgical patient. Although the benefits of smoking cessation are well established, as is substantial evidence demonstrating that brief interventions are effective in increasing cessation rates among users, clinicians fail to consistently address the issue of tobacco use or provide smoking cessation interventions. Referral to elective surgical procedures provides an excellent opportunity for primary providers to promote smoking cessation interventions

Assessing Risk For Right Heart Failure After Left Ventricular Assist Device Implantation

The lives of more than six million people in the United States are negatively impacted by the diagnosis of Advanced Heart Failure. Financial burden, repeated hospitalizations, and declining quality of life account for poor outcomes. Implantation of a left ventricular assist device (LVAD) has offered the promise of improved financial, clinical, and functional outcomes for those awaiting or ineligible for heart transplantation. Right Heart Failure (RHF), however, threatens positive outcomes as it remains the leading cause of mortality and morbidity following LVAD placement. Despite extensive research, there is no comprehensive tool for RHF risk assessment and stratification for this population. The D.N.P. project aimed to adapt and implement a scoring tool for such assessment. Providers rated the assessment tool to be feasible and useful in practice. Though limited by a small number of LVAD patients, RHF risk was found to fluctuate for each patient throughout the phases of care, and no single parameter consistently trended in the same direction as the combined score. This pilot project should inspire future projects aimed at identifying risk for RHF which can offer opportunities for preventative care and realization of all positive outcomes for LVAD recipients

Statistical atlases for electroanatomical mapping of cardiac arrhythmias

Electroanatomical mapping is a mandatory time-consuming planning step in cardiac catheter ablation. In practice, interventional cardiologists target specific endocardial areas for mapping based on personal experience, general electrophysiology principles, and preoperative anatomical scans. Effective fusion of all available information towards a useful mapping strategy has not been standardised and achieving the optimal map within time and space constraints is challenging. In this paper, a novel framework for computing optimal endocardial mapping locations in patients with congenital heart disease (CHD) is proposed. The method is based on a statistical electroanatomical model (SEAM) which is instantiated from preoperative anatomy in order to achieve an initial prediction of the electrical map. Simultaneously, the anatomical areas with the highest frequency of mapping among the similar cases in the dataset are detected and a classifier is trained to filter these points based on the electroanatomical data. The framework was tested in an iterative process of adding mapping points to the SEAM and computing the instantiation error, with retrospective clinical data of 66 CHD cases available