Search Results (23)

The healthcare sector is a major contributor to global greenhouse gas emissions. Little is known about the impact of individual clinical practices on overall emissions; more granular healthcare emissions data are needed to identify opportunities for resource stewardship. Our objective was to deploy an interdisciplinary team to perform Life Cycle Assessments (LCAs) comparing carbon emissions attributable to a novel home-care program for premature infants to those attributable to routine care in the Neonatal Intensive Care Unit (NICU). We used LCA methodology to compare the carbon footprint of two weeks of traditional care of infants in our NICU to that of those enrolled in an institutional alternative care program known as “Hope Grows at Home,” which transitions eligible infants requiring nasogastric feeds to the home setting with ongoing NICU team support. Our analysis showed that in-home care produces 77 kg of CO₂ emissions (kgCO₂e) per infant over a 14-day period, as compared to in-hospital care, which produced 338 kgCO₂e. Transportation to a healthcare facility accounted for the majority of emissions in both groups (292 kgCO₂e for NICU care and 58 kgCO₂e for home care). This finding is likely impacted by our facility’s rural location. Home care reduced solid waste emissions by approximately 94% relative to NICU care (1.74 vs. 26.97 kgCO₂e per term), reflecting the home setting’s reuse of feeding syringes and bottles that are routinely single-use in the hospital. Prospective data collection strategies for infants enrolled in home care will further refine our results. Exploring additional interdisciplinary collaborations may facilitate similar analyses, offering more insight into environmental stewardship opportunities within healthcare. Full article

Capsule endoscopy (CE) is evolving from a primarily small-bowel imaging modality into a broader diagnostic platform that increasingly incorporates artificial intelligence (AI), robotic technologies, biosensing capabilities, and decentralized models of care. The REFLECT symposium brought together an international, multidisciplinary audience of clinicians, engineers, scientists, and healthcare stakeholders to critically evaluate the present and future role of CE across a range of gastrointestinal (GI) applications, including inflammatory bowel disease, GI bleeding, coeliac disease, and colorectal cancer screening. Discussions explored the clinical impact of panenteric and colon capsule endoscopy, the potential of AI to enhance diagnostic performance and streamline workflows, innovations in capsule hardware, and the design of patient-centred diagnostic pathways. While conventional endoscopy continues to serve as the benchmark in many clinical scenarios, CE was recognized for its ability to improve access, acceptability, and scalability when deployed in appropriately selected populations. The symposium also identified key barriers to broader implementation, such as reinvestigation rates, absence of standardized quality indicators, limited real-world evidence for AI tools, and ongoing economic and environmental challenges. Overall, the meeting highlighted the importance of gradual, evidence-driven integration of CE, supported by robust validation, standardized metrics, close clinician-engineer collaboration, and meaningful incorporation of patient experience, to support the development of a safe, equitable, and sustainable pathway. Full article

Infertility treatment often requires patients to self-administer hormonal injections, creating significant physical, logistical, and psychological burdens. While medical technologies have improved pharmacological efficacy and safety, design aspects addressing usability, portability, and emotional distress remain underexplored. This study presents Blloom, a compact self-injection device that integrates ergonomic, thermal, and emotional considerations designed through an interdisciplinary design-thinking framework. This study identified critical user needs related to self-injection anxiety, medication refrigeration, and treatment-related stigma through in-depth, multi-method qualitative design research. The resulting prototype is characterized by one-handed operation, concealed needle delivery, and built-in passive cooling (2–8 °C for up to 8 h). Formative evaluations with patients and clinicians confirmed its improved usability, emotional comfort, and contextual compatibility. At this prototypical stage, medication- and container-specific compatibility, as well as long-term reliability, require further bench testing and clinical validation. Process analysis further revealed how designer–engineer collaboration evolved from empathic exploration to implementation-driven convergence. The findings demonstrate how human-centered design can mitigate the multidimensional burdens of infertility treatment and provide a replicable framework for interdisciplinary innovation in self-managed healthcare devices. Full article

Artificial intelligence (AI) has the potential to transform healthcare by supporting more accurate diagnoses and personalized treatments. However, its adoption in practice remains constrained by fragmented data sources, strict privacy rules, and the technical complexity of building reliable clinical systems. To address these challenges, we introduce a model-driven engineering (MDE) framework designed specifically for healthcare AI. The framework relies on formal metamodels, domain-specific languages (DSLs), and automated transformations to move from high-level specifications to running software. At its core is the Medical Interoperability Language (MILA), a graphical DSL that enables clinicians and data scientists to define queries and machine learning pipelines using shared ontologies. When combined with a federated learning architecture, MILA allows institutions to collaborate without exchanging raw patient data, ensuring semantic consistency across sites while preserving privacy. We evaluate this approach in a multi-center cancer immunotherapy study. The generated pipelines delivered strong predictive performance, with best-performing models achieving up to 98.5% accuracy on selected prediction tasks, while substantially reducing manual coding effort. These findings suggest that MDE principles—metamodeling, semantic integration, and automated code generation—can provide a practical path toward interoperable, reproducible, and reliable digital health platforms. Full article

Three-dimensional (3D) anatomic modeling derived from high-resolution medical imaging, such as computed tomography (CT) and magnetic resonance imaging (MRI), has been increasingly adopted in preclinical testing and device development. This white paper describes a cardiac-specific workflow that integrates 3D printing and silicone molding for support device development and procedural simulation. Patient-derived computed tomography angiography data were segmented using FDA-cleared medical modeling software to isolate the left ventricular anatomy and were further processed in computer-aided design (CAD) to ensure accurate physiological wall thickness and structural fidelity. Material jetting 3D printing was performed on a Stratasys J750 using material distributions designed to mimic the mechanical properties of myocardium, thereby approximating myocardial compliance. In parallel, stereolithography apparatus molds were designed from the left ventricle CAD model to cast transparent, pliable left ventricular models in Sorta-Clear™ 18 silicone. The 3D-printed models preserved intricate morphological detail and were suitable for mechanical manipulation and device deployment studies, whereas silicone models offered tunable mechanical properties, transparency for visualization, and durability for repeated use. Together, these complementary modalities provided rapid manufacturing capability and application-relevant physical representation. Case-specific parameters, strengths, and limitations of both models in enhancing patient care and device testing are highlighted, with relevance to heart failure applications. Current knowledge gaps, workflow and integration challenges, and future opportunities are identified, positioning this work as a reference framework for continued innovation in anatomic modeling. Within the collaborative framework of Mayo Clinic’s Anatomic Modeling Unit and Simulation Center, this integrated modeling workflow demonstrates the value of multidisciplinary collaboration between engineers and clinicians. Clinically, these patient-specific left ventricular models may enable pre-procedural device sizing and positioning and may support simulation of mechanical circulatory support (MCS) deployment while identifying possible anatomic constraints prior to intervention. This workflow has direct applicability in advanced heart failure patients undergoing MCS support, such as the Impella axillary MCS device or the durable LVAD, with potential to reduce procedural uncertainty while reducing complications and improving peri-procedural outcomes. Additionally, these models also serve as high-accuracy educational tools, enabling trainees and multidisciplinary care teams to visualize and possibly rehearse procedural steps while gaining hands-on experience in a risk-free environment. Full article

Liquid biopsy and multi-omic biomarker integration are transforming precision oncology in breast cancer, providing real-time, minimally invasive insights into tumor biology. By analyzing circulating tumor DNA, circulating tumor cells, exosomal non-coding RNAs, and proteomic or metabolomic profiles, clinicians can monitor clonal evolution, therapeutic response, and recurrence risk in real time. Recent advances in sequencing technologies, methylation profiling, and artificial intelligence–driven data integration have markedly improved diagnostic sensitivity and predictive accuracy. Multi-omic frameworks combining genomic, transcriptomic, and proteomic data enable early detection of resistance, molecular stratification, and identification of actionable targets, while machine learning models enhance outcome prediction and therapy optimization. Despite these advances, key challenges persist. Pre-analytical variability, lack of standardized protocols, and disparities in access continue to limit reproducibility and clinical adoption. High costs, incomplete regulatory validation, and the absence of definitive evidence for mortality reduction underscore the need for larger, prospective trials. Integrating multi-omic assays into clinical workflows will require robust bioinformatics pipelines, clinician-friendly reporting systems, and interdisciplinary collaboration among molecular scientists, data engineers, and oncologists. In the near future, liquid biopsy is expected to complement, not replace, traditional tissue analysis, serving as a cornerstone of adaptive cancer management. As sequencing becomes faster and more affordable, multi-omic and AI-driven analyses will allow earlier detection, more precise treatment adjustments, and continuous monitoring across the disease course. Ultimately, these innovations herald a shift toward real-time, data-driven oncology that personalizes breast cancer care and improves patient outcomes. Full article

Artificial intelligence (AI) is rapidly transforming cardiovascular medicine, with profound implications for congenital heart disease (CHD). Tetralogy of Fallot (ToF), the most common cyanotic disease, requires lifelong surveillance and complex management because of late complications such as pulmonary regurgitation, arrhythmias, and right ventricular dysfunction. This review synthesizes current evidence on AI applications across the continuum of ToF care—from prenatal diagnosis to adulthood follow-up. We examine advances in imaging, perioperative planning, intraoperative monitoring, intensive care, and long-term surveillance, including wearable and implantable technologies. Machine learning (ML), deep learning (DL), and natural language processing (NLP) are revolutionizing diagnostic accuracy, risk stratification, surgical decision-making, and personalized long-term care. The future lies in the integration of multimodal data, including imaging, electronic health records (EHRs), genomic information, and continuous monitoring, to support precision medicine. Challenges remain regarding dataset limitations, interpretability, regulatory standards, and ethical concerns. Nevertheless, ongoing innovation and collaboration between clinicians, engineers, and regulators promise a new era in congenital cardiology. By embedding AI throughout the patient journey, healthcare systems may improve outcomes and quality of life for individuals with ToF. Full article

Football (soccer) is the world’s most widely played sport, but it carries a high incidence of traumatic injuries, particularly to the head, face, and lower limbs. Once regarded as a low-equipment discipline, the role of protective devices has expanded substantially in recent decades, both in injury prevention and in return-to-play strategies. This review provides a comprehensive overview of the historical evolution, typology, and materials of football protective equipment, with additional focus on regulatory frameworks, cultural acceptance, and illustrative cases from elite athletes. Shin guards remain the only mandatory device, yet the use of facial masks, headgear, braces, and orthoses is increasing, particularly following high-profile injuries. Advances in carbon fiber composites, thermoplastics, viscoelastic foams, and additive manufacturing have enabled lightweight, customized devices that balance protection with comfort and adherence. Beyond biomechanics, psychological reassurance, esthetics, durability, and hygiene strongly influence player compliance and perception. Despite this progress, critical challenges remain. Football lacks standardized testing protocols, clear certification pathways, and longitudinal studies on long-term outcomes. Evidence is particularly limited for youth athletes and newer categories of equipment. Looking ahead, the integration of wearable technologies, systematic hygiene and durability testing, and sustainable materials could transform protective gear into multifunctional tools for safety, monitoring, and performance optimization. Protective equipment in football has thus evolved into a multidisciplinary field at the intersection of medicine, engineering, psychology, and regulation. Future advances will depend on stronger collaboration between clinicians, researchers, governing bodies, and manufacturers to ensure safe, effective, and widely accepted protective solutions at all levels of the game. Full article

Introduction: Wearable devices capable of continuously sampling movement, autonomic arousal and neuro-electrical activity are emerging as promising complements to traditional assessment and treatment of Attention-Deficit/Hyperactivity Disorder (ADHD). By moving data collection from the clinic to everyday settings, these technologies offer an unprecedented window onto the moment-to-moment fluctuations that characterise the condition. Methods: Drawing on a comprehensive literature search spanning 2013 to February 2025 across biomedical and engineering databases, we reviewed empirical studies that used commercial or research-grade wearables for ADHD-related diagnosis, monitoring or intervention. Titles and abstracts were screened against predefined inclusion criteria, with full-text appraisal and narrative synthesis of the eligible evidence. A narrative synthesis was conducted, with inclusion criteria targeting empirical studies of wearable devices applied to ADHD for monitoring, mixed monitoring-plus-intervention, or intervention-only applications. No quantitative pooling was undertaken due to heterogeneity of designs, endpoints, and analytic methods. Results: The reviewed body of work demonstrates that accelerometers, heart-rate and electrodermal sensors, and lightweight EEG headsets can enrich clinical assessment by capturing ecologically valid markers of hyperactivity, arousal and attentional lapses. Continuous monitoring studies suggest that wearable-derived metrics align with symptom trajectories and medication effects, while early intervention trials explore haptic prompts, attention-supporting apps and non-invasive neuromodulation delivered through head-worn devices. Across age groups, participants generally tolerate these tools well and value the objective feedback they provide. Nevertheless, the literature is limited by heterogeneous study designs, modest sample sizes and short follow-up periods, making direct comparison and clinical translation challenging. Conclusions: Current evidence paints an optimistic picture of the feasibility and acceptability of wearables in ADHD, yet larger, standardised and longer-term investigations are needed to confirm their clinical utility. Collaboration between clinicians, engineers and policymakers will be crucial to address data-privacy, equity and cost-effectiveness concerns and to integrate wearable technology into routine ADHD care. Full article

The increasing global health crisis of neurodegenerative diseases such as Alzheimer’s, Parkinson’s, amyotrophic lateral sclerosis, and Huntington’s disease is worsening because of a rapidly increasing aging population. Disease-modifying therapies continue to face development challenges due to the blood–brain barrier (BBB), which prevents more than 98% of small molecules and all biologics from entering the central nervous system. The therapeutic landscape for neurodegenerative diseases has recently undergone transformation through advances in targeted drug delivery that include ligand-decorated nanoparticles, bispecific antibody shuttles, focused ultrasound-mediated BBB modulation, intranasal exosomes, and mRNA lipid nanoparticles. This review provides an analysis of the molecular pathways that cause major neurodegenerative diseases, discusses the physiological and physicochemical barriers to drug delivery to the brain, and reviews the most recent drug targeting strategies including receptor-mediated transcytosis, cell-based “Trojan horse” approaches, gene-editing vectors, and spatiotemporally controlled physical methods. The review also critically evaluates the limitations such as immunogenicity, scalability, and clinical translation challenges, proposing potential solutions to enhance therapeutic efficacy. The recent clinical trials are assessed in detail, and current and future trends are discussed, including artificial intelligence (AI)-based carrier engineering, combination therapy, and precision neuro-nanomedicine. The successful translation of these innovations into effective treatments for patients with neurodegenerative diseases will require essential interdisciplinary collaboration between neuroscientists, pharmaceutics experts, clinicians, and regulators. Full article

Depression represents one of the most prevalent mental health disorders globally, significantly impacting quality of life and posing substantial healthcare challenges. Traditional diagnostic methods rely on subjective assessments and clinical interviews, often leading to misdiagnosis, delayed treatment, and suboptimal outcomes. Recent advances in biosensing technologies offer promising avenues for objective depression assessment through detection of relevant biomarkers and physiological parameters. This review examines multi-modal biosensing approaches for depression by analyzing electrochemical biosensors for neurotransmitter monitoring alongside wearable sensors tracking autonomic, neural, and behavioral parameters. We explore sensor fusion methodologies, temporal dynamics analysis, and context-aware frameworks that enhance monitoring accuracy through complementary data streams. The review discusses clinical validation across diagnostic, screening, and treatment applications, identifying performance metrics, implementation challenges, and ethical considerations. We outline technical barriers, user acceptance factors, and data privacy concerns while presenting a development roadmap for personalized, continuous monitoring solutions. This integrative approach holds significant potential to revolutionize depression care by enabling earlier detection, precise diagnosis, tailored treatment, and sensitive monitoring guided by objective biosignatures. Successful implementation requires interdisciplinary collaboration among engineers, clinicians, data scientists, and end-users to balance technical sophistication with practical usability across diverse healthcare contexts. Full article

Artificial intelligence (AI) is transforming cardiovascular medicine by enabling the analysis of high-dimensional biomedical data with unprecedented precision. Initially employed to automate human tasks such as electrocardiogram (ECG) interpretation and imaging segmentation, AI’s true potential lies in uncovering hidden disease data patterns, predicting long-term cardiovascular risk, and personalizing treatments. Unlike human cognition, which excels in certain tasks but is limited by memory and processing constraints, AI integrates multimodal data sources—including ECG, echocardiography, cardiac magnetic resonance (CMR) imaging, genomics, and wearable sensor data—to generate novel clinical insights. AI models have demonstrated remarkable success in early dis-ease detection, such as predicting heart failure from standard ECGs before symptom on-set, distinguishing genetic cardiomyopathies, and forecasting arrhythmic events. However, several challenges persist, including AI’s lack of contextual understanding in most of these tasks, its “black-box” nature, and biases in training datasets that may contribute to disparities in healthcare delivery. Ethical considerations and regulatory frameworks are evolving, with governing bodies establishing guidelines for AI-driven medical applications. To fully harness the potential of AI, interdisciplinary collaboration among clinicians, data scientists, and engineers is essential, alongside open science initiatives to promote data accessibility and reproducibility. Future AI models must go beyond task automation, focusing instead on augmenting human expertise to enable proactive, precision-driven cardiovascular care. By embracing AI’s computational strengths while addressing its limitations, cardiology is poised to enter an era of transformative innovation beyond traditional diagnostic and therapeutic paradigms. Full article

The fifth annual REFLECT (The futuRE oF MinimalLy InvasivE GI and Capsule diagnosTics) symposium, held in October 2024 in Nyborg, Denmark, focused on advancements in minimally invasive gastrointestinal (GI) diagnostics, particularly capsule endoscopy (CE) technologies. Key discussions included clinical updates, innovations in hardware and software, and the growing role of colon CE (CCE) in colorectal cancer screening. The event provided a platform for clinicians, engineers, industry representatives, and scientists to exchange knowledge and present the latest advancements in the field. Discussions covered clinical studies, future research protocols, and technological innovations, with also a notable focus on commercial solutions and expansion of the implementation of capsule endoscopy. The symposium also highlighted the significance of predictive models for patient selection and developments in panenteric CE. Innovative technologies presented included robotics for drug delivery and magnetic endoscopic guidance systems. AI advancements were discussed for their potential to reduce diagnostic fatigue and standardize image interpretation, but ethical concerns and the need for transparent algorithms remain. The importance of multidisciplinary collaboration was emphasized to bridge innovation and clinical practice. Home-based CCE delivery emerged as a promising model, despite mixed results from environmental impact assessments. Overall, REFLECT 2024 reinforced the clinical utility and challenges of capsule-based diagnostics, advocating for ongoing interdisciplinary research to support safe and effective integration into healthcare systems. Full article

Within biomedical engineering, there has been significant collaboration among clinicians, control engineers, and researchers to tailor treatments to individual patients. Anesthesia is integral to numerous medical procedures, necessitating precise management of hypnosis, analgesia, neuromuscular blockade, and hemodynamic variables. Recent attention has focused on computer-controlled anesthesia and hemodynamic stabilization. This research proposes the integration of a decentralized control strategy for the induction phase with a decoupled control approach for the maintenance phase, aimed at mitigating interactions within the multivariable human system. The proposed strategy is based on fractional-order controllers. The solution is validated using an open-source patient simulator featuring data from 24 virtual patients, demonstrating the efficiency of the proposed approach with respect to decentralized control. Full article

Electrochemical biosensors have emerged as powerful tools for the ultrasensitive detection of lung cancer biomarkers like carcinoembryonic antigen (CEA), neuron-specific enolase (NSE), and alpha fetoprotein (AFP). This review comprehensively discusses the progress and potential of nanocomposite-based electrochemical biosensors for early lung cancer diagnosis and prognosis. By integrating nanomaterials like graphene, metal nanoparticles, and conducting polymers, these sensors have achieved clinically relevant detection limits in the fg/mL to pg/mL range. We highlight the key role of nanomaterial functionalization in enhancing sensitivity, specificity, and antifouling properties. This review also examines challenges related to reproducibility and clinical translation, emphasizing the need for standardization of fabrication protocols and robust validation studies. With the rapid growth in understanding lung cancer biomarkers and innovations in sensor design, nanocomposite electrochemical biosensors hold immense potential for point-of-care lung cancer screening and personalized therapy guidance. Realizing this goal will require strategic collaboration among material scientists, engineers, and clinicians to address technical and practical hurdles. Overall, this work provides valuable insight for developing next-generation smart diagnostic devices to combat the high mortality of lung cancer. Full article