Biomedical Engineering Device Development

𝐓𝐡𝐞 𝐢𝐝𝐞𝐚 𝐨𝐟 𝟑𝐃 𝐩𝐫𝐢𝐧𝐭𝐢𝐧𝐠 𝐡𝐚𝐬 𝐣𝐮𝐬𝐭 𝐛𝐞𝐞𝐧 𝐟𝐥𝐢𝐩𝐩𝐞𝐝 𝐨𝐧 𝐢𝐭𝐬 𝐡𝐞𝐚𝐝. Instead of printing metal, a team of scientists in Switzerland grew it from a gel – and the result is 20x stronger than previous methods. Using a water-based hydrogel as a scaffold, researchers at EPFL (École Polytechnique Fédérale de Lausanne) created complex structures that can be infused with metal salts. After several rounds of soaking and heating, the gel vanishes – leaving behind dense, ultra-strong metal or ceramic. Traditional metal 3D printing often results in porous structures with serious shrinkage. This new method dramatically reduces those flaws, producing durable, precisely shaped components with only 20% shrinkage. It also opens the door to building with a wide range of materials – the same gel template can be used to grow iron, silver, copper, or even advanced composites. The technique could revolutionize how we make complex, high-performance parts for energy systems, biomedical devices, and next-gen electronics. It’s also a shift in mindset: rather than designing around the limits of printing materials, this approach lets researchers build first, and choose the material later. The team is already working on automating the process, aiming to bring this breakthrough into real-world manufacturing. Read the study "𝐻𝑦𝑑𝑟𝑜𝑔𝑒𝑙‐𝐵𝑎𝑠𝑒𝑑 𝑉𝑎𝑡 𝑃ℎ𝑜𝑡𝑜𝑝𝑜𝑙𝑦𝑚𝑒𝑟𝑖𝑧𝑎𝑡𝑖𝑜𝑛 𝑜𝑓 𝐶𝑒𝑟𝑎𝑚𝑖𝑐𝑠 𝑎𝑛𝑑 𝑀𝑒𝑡𝑎𝑙𝑠 𝑤𝑖𝑡ℎ 𝐿𝑜𝑤 𝑆ℎ𝑟𝑖𝑛𝑘𝑎𝑔𝑒𝑠 𝑣𝑖𝑎 𝑅𝑒𝑝𝑒𝑎𝑡𝑒𝑑 𝐼𝑛𝑓𝑢𝑠𝑖𝑜𝑛 𝑃𝑟𝑒𝑐𝑖𝑝𝑖𝑡𝑎𝑡𝑖𝑜𝑛." 𝐴𝑑𝑣𝑎𝑛𝑐𝑒𝑑 𝑀𝑎𝑡𝑒𝑟𝑖𝑎𝑙𝑠, 2025 https://lnkd.in/eian6kVx

𝗣𝗿𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗮𝘁 𝘁𝗵𝗲 𝗣𝗶𝗰𝗼𝗹𝗶𝘁𝗲𝗿 𝗦𝗰𝗮𝗹𝗲: 𝗡𝗼𝗻-𝗖𝗼𝗻𝘁𝗮𝗰𝘁 𝗗𝗶𝘀𝗽𝗲𝗻𝘀𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗣𝗶𝗲𝘇𝗼𝗲𝗹𝗲𝗰𝘁𝗿𝗶𝗰 𝗔𝗰𝘁𝘂𝗮𝘁𝗼𝗿𝘀 Working in the field of microdispensing, I am always fascinated by the precision that can be achieved. The ability to dispense tiny droplets without touching the surface opens up a wide range of possibilities in 𝗯𝗶𝗼𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆, 𝗱𝗶𝗮𝗴𝗻𝗼𝘀𝘁𝗶𝗰𝘀, and 𝗲𝗹𝗲𝗰𝘁𝗿𝗼𝗻𝗶𝗰𝘀. This technique is crucial in applications such as 𝗺𝗶𝗰𝗿𝗼𝗮𝗿𝗿𝗮𝘆 𝗽𝗿𝗶𝗻𝘁𝗶𝗻𝗴, 𝗯𝗶𝗼𝘀𝗲𝗻𝘀𝗼𝗿 𝗳𝗮𝗯𝗿𝗶𝗰𝗮𝘁𝗶𝗼𝗻, and 𝗮𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗲𝗹𝗲𝗰𝘁𝗿𝗼𝗻𝗶𝗰𝘀 𝗺𝗮𝗻𝘂𝗳𝗮𝗰𝘁𝘂𝗿𝗶𝗻𝗴, where accuracy, repeatability, and contamination-free liquid handling are essential. But how does it actually work? 𝗛𝗼𝘄 𝗣𝗶𝗰𝗼𝗹𝗶𝘁𝗲𝗿 - 𝗡𝗼𝗻-𝗖𝗼𝗻𝘁𝗮𝗰𝘁 𝗗𝗶𝘀𝗽𝗲𝗻𝘀𝗶𝗻𝗴 𝗪𝗼𝗿𝗸𝘀 𝗮𝘁 M2-Automation GmbH: The key component is the piezoelectric actuator, which utilizes the converse piezoelectric effect - applying a voltage pulse causes the actuator to contract, generating a pressure wave inside a fine glass capillary. This wave forces out a controlled droplet, all while avoiding physical contact with the substrate. 👉 The 𝘀𝘁𝗮𝗻𝗱𝗮𝗿𝗱 𝗣𝗗𝗠𝗗 dispenser covers a 𝟯𝟬–𝟰𝟬𝟬 𝗽𝗟 volume range. 👉 With the 𝗣𝘂𝗹𝘀𝗲𝗦𝗵𝗮𝗽𝗲𝗿, it is possible to apply 𝗮𝗿𝗯𝗶𝘁𝗿𝗮𝗿𝘆 𝘄𝗮𝘃𝗲𝗳𝗼𝗿𝗺𝘀 to the actuator, extending the range to 𝟭𝟬–𝟴𝟬𝟬 𝗽𝗟 and optimizing droplet formation for different liquid properties. 👉 For 𝗵𝗶𝗴𝗵𝗲𝗿 𝘃𝗼𝗹𝘂𝗺𝗲𝘀 𝘂𝗽 𝘁𝗼 𝟰 𝗻𝗟, the 𝗣𝗗𝗠𝗗-𝗟 offers a larger nozzle diameter and higher aspiration volume, allowing efficient handling of larger liquid amounts. 𝗞𝗲𝘆 𝗔𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲𝘀 𝗼𝗳 𝗣𝗶𝗲𝘇𝗼𝗲𝗹𝗲𝗰𝘁𝗿𝗶𝗰 𝗡𝗼𝗻-𝗖𝗼𝗻𝘁𝗮𝗰𝘁 𝗗𝗶𝘀𝗽𝗲𝗻𝘀𝗶𝗻𝗴: ✅ 𝗛𝗶𝗴𝗵 𝗣𝗿𝗲𝗰𝗶𝘀𝗶𝗼𝗻 – Achieves <𝟮% 𝗖.𝗩., ensuring highly reproducible results. ✅ 𝗡𝗼 𝗖𝗿𝗼𝘀𝘀-𝗖𝗼𝗻𝘁𝗮𝗺𝗶𝗻𝗮𝘁𝗶𝗼𝗻 – The nozzle never touches the substrate, ensuring a clean and reliable process. ✅ 𝗣𝗿𝗲𝘀𝗲𝗿𝘃𝗲𝘀 𝗗𝗲𝗹𝗶𝗰𝗮𝘁𝗲 𝗦𝘂𝗿𝗳𝗮𝗰𝗲𝘀 – Ideal for applications where substrate integrity must remain intact. ✅ 𝗩𝗲𝗿𝘀𝗮𝘁𝗶𝗹𝗲 𝗟𝗶𝗾𝘂𝗶𝗱 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴 – Compatible with a wide range of liquids, from aqueous solutions to complex biological samples. ✅ 𝗛𝗶𝗴𝗵 𝗦𝗽𝗲𝗲𝗱 – Operates at frequencies up to 𝟭𝟬𝟬𝟬 𝗛𝘇, making it suitable for high-throughput applications. Picoliter dispensing, combined with precise humidity control and high-precision, ultra-fast axes systems, continues to push the boundaries of what is possible in precision liquid handling. These advancements enable new levels of 𝗮𝗰𝗰𝘂𝗿𝗮𝗰𝘆, 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆, 𝗮𝗻𝗱 𝘀𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆, driving innovation in biotechnology, diagnostics, and materials science. Where do you see the biggest potential for non-contact dispensing? Let’s discuss! BTW... like always, all videos recorded in real speed 😉

This paper explores the transformative impact of wearables and AI on healthcare workflows and patient care, focusing on enhanced efficiency, personalization, and cost-effectiveness. 1️⃣ IoMT (Internet of Medical Things) market is rapidly growing, projected to increase from $50.3 billion in 2020 to $135.87 billion by 2025, highlighting a significant shift toward digital health adoption. 2️⃣ Wearables have diverse applications, monitoring both biological factors (e.g., saliva, sweat) and utility-based measurements (e.g., smart fabrics, implants) to enhance patient data collection. 3️⃣ Real-time monitoring through wearables and AI supports early disease detection and continuous tracking, facilitating better treatment adherence and fewer hospital visits. 4️⃣ Patient interest in remote monitoring is strong, with 79% willing to use mobile ECG tools, and 74% feeling safer with constant monitoring, demonstrating growing acceptance of self-managed care. 5️⃣ AI-assisted monitoring with wearable sensors achieves high accuracy, including 97% accuracy in detecting atrial fibrillation, outperforming traditional methods. 6️⃣ AI models like deep learning and neural networks enable predictive diagnostics and personalized treatments, demonstrating 80% accuracy for heart disease, 80% for blood infections, and 94% for cancer detection. 7️⃣ Integration challenges include data management, EHR integration, privacy, bias, and transparency, all of which must be addressed to foster trust among healthcare providers and patients. 8️⃣ Automation potential is significant, with AI transforming tasks like medical billing, coding, and lab workflows, reducing errors and freeing up resources for patient care. 9️⃣ Future healthcare will increasingly depend on AI and wearables, reshaping patient management, especially for aging populations, and enabling personalized, real-time care delivery. 🔟 AI and wearables promise a comprehensive transformation of healthcare, enhancing efficiency, personalizing treatments, and reducing costs while overcoming obstacles to data integration and physician-patient trust. ✍🏻 Perry LaBoone, PE, CPA, PMP, Oge Marques. Overview of the future impact of wearables and artificial intelligence in healthcare workflows and technology. International Journal of Information Management Data Insights. 2024. DOI: 10.1016/j.jjimei.2024.100294

The Peterson Center on Healthcare just released a timely and thought-provoking report titled on the evolving landscape of remote monitoring. As remote physiologic (RPM) and therapeutic monitoring (RTM) gain traction, especially in Medicare and Medicaid, this report asks a critical question: are we paying for what truly works? Key Findings: 📈 Use is growing rapidly: Medicare beneficiaries using RPM jumped from 44,500 in 2019 to 451,000 in 2023. RTM is also rising fast. 💸 Spending is accelerating: RPM spend in Traditional Medicare surged to $194.5M in 2023, with 22% of episodes lasting over 9 months. 🩺 Effectiveness varies widely by condition: -RPM for hypertension shows strong short-term results (up to 6 months). -RTM for musculoskeletal conditions helps when used during focused PT episodes (2–4 months). -RPM for type 2 diabetes shows only modest, short-lived benefit — mostly in patients with very high HbA1c levels (we know this from the last PHTI study) ⏳ Current billing doesn’t match the evidence: Providers can bill indefinitely, even after the clinical benefit has faded (the do-more-make-more problem with FFS). 📊 Data gaps are a big problem: It’s often unclear what’s being monitored, for whom, and why. We have a massive opportunity to align coverage and reimbursement with actual clinical value — ensuring remote monitoring improves outcomes and spending efficiency. As adoption accelerates, it's going to be critical that we develop payment policies and the appropriate clinical models of care to ensure the right tools are reaching the right patients — and only for as long as they help. PDF of full report attached. #DigitalHealth #RemoteMonitoring #ValueBasedCare #healthcare #healthcareonlinkedin #ChronicDiseaseManagement Meg Barron Caroline Pearson

7 wearable and sensor innovations pushing health beyond “wellness” tracking this month: 🔘 Sibel Health is developing an AI-enabled wearable that tracks scratching behaviour in people with atopic dermatitis, turning something usually seen as a subjective symptom into a measurable clinical signal that could also support drug development. 🔘 CranioSense is working on a non-invasive approach to measuring intracranial pressure, which today often requires invasive procedures, and if validated could make brain pressure monitoring safer and more continuous in routine clinical care. 🔘 University of Technology Sydney researchers are developing AI-powered sweat sensors that can decode body chemistry in real time, tracking hormones, medication levels and potential early warning signs of disease, potentially offering a non-invasive alternative to some forms of blood testing 🔘 ŌURA rings are being used within Medicare Advantage Plans, with around one-third of eligible members opting in and sharing biometric data, which is already leading to improvements in sleep and light activity and is paving the way for deeper clinical use cases such as hypertension monitoring 🔘 Samsung Electronics is preparing to launch an AI Brain Health tool that uses data from smartphones and wearables, including speech, movement and sleep behaviour, to help detect early signs of dementia while aiming to keep the experience privacy-aware and clinically relevant 🔘 Researchers at the University of Arizona have created a wearable mesh sleeve that monitors gait and subtle movement patterns to identify early signs of frailty in older adults, with the goal of shifting care from reacting after a fall to proactively supporting prevention through continuous remote monitoring 🔘 And China is testing “smart urinals” that analyse urine in real time for markers like glucose and protein, which opens up interesting conversations about passive health screening, consent, and how health data might be gathered in everyday environments. 💬We are steadily moving from episodic health snapshots to passive, continuous and contextual signals across movement, sleep, behaviour and even body chemistry. The technology is getting closer. Now the real work is around validation, governance, reimbursement and making sure the data actually makes a difference in peoples lives 👇 Links to articles in comments #DigitalHealth #Wearables #AI

Your Wardrobe Goes Online From step counters to fart monitors, wearables are changing the epistemology of medicine itself We began with step counters. Then heart rhythms. Then sleep cycles. Then blood oxygen. Then glucose. Now? Farts. Scientists at the University of Maryland are piloting what they jokingly call a "Fitbit for farts"—a tiny hydrogen sensor worn discreetly on the body that continuously measures flatulence. It sounds like late-night comedy. It is, in fact, serious #gastroenterology. And it signals something much larger. This device sits at the crossroads of three powerful trends: extreme miniaturization, continuous monitoring, and edge computing. The same supply chains that gave us smart rings, smart watches, and wireless earbuds now enable a battery-powered sensor small enough to measure something we've never systematically measured before: baseline digestive patterns. Forty percent of American adults report regular digestive disruption. Fiber-rich diets, which reduce colon cancer risk, are often abandoned because of bloating and gas. Yet in 2026, we still don't know how often the average person passes gas in a day. Early data offers a hint at the range: one participant logged 175 emissions. For decades, digestive health relied on self-reporting and invasive lab work. Now we are entering an era of passive, ambient #health telemetry. The #AppleWatch moved the cardiology ward to your wrist. Continuous glucose monitors brought the endocrinology lab to your arm. Each time, the shift was the same: from episodic snapshot to living dashboard. When you move from occasional measurement to continuous signal detection, you don't just gather more data—you change the epistemology of medicine itself. Patterns that were previously invisible become legible. Causation, not just correlation, becomes possible. This is just the beginning. Imagine clothing that tracks inflammatory markers. Glasses that monitor neurological drift. Belts—we make a few of those at Randa Apparel & Accessories—that detect posture, waist measurement, and metabolic change. Fabrics embedded with biosensors that surface early-stage disease before symptoms arrive. Your wardrobe becomes diagnostic infrastructure. Real questions follow: #data ownership, #privacy, psychological burden, the quiet anxiety of living with a dashboard of yourself. Continuous monitoring can empower patients, or produce a nation of worried well, over-interpreting every signal. These are not small concerns. But the direction is unmistakable. #Healthcare is migrating from hospitals to homes to bodies. From appointments to algorithms. From episodic to continuous. Technology has always moved closer, first to our pockets with smartphones, then our wrists. Now it is woven into the textiles we wear and clipped discreetly where biology actually happens. Fitbit (now part of Google) and ŌURA are not the destination. They are the prologue. Walt Whitman sang the body electric. We're adding sensors.

If your medical device has software, FDA demands cybersecurity. And if you architect your system incorrectly, trying to secure it later will be painful. So… Before choosing components or writing code, think through the whole system architecture. This diagram is part of a book we’re writing on MedTech Cybersecurity. Let me know if you’re open to reviewing an advanced electronic copy. The figure is imperfect because there are nuances that are hard to capture, but here’s the headline: Start cybersecurity early and consider each subsystem. There are many moving parts in cybersecurity, and architecting the overall system and each subsystem is iterative. Here are some key steps: ↳ Understand user needs and the role security plays ↳ Consider other systems your device will talk to ↳ Capture security requirements early ↳ Architect with a defense-in-depth approach ↳ Choose hardware components that are likely to meet security requirements ↳ Propose a software architecture and then evaluate it against the constraints of the system and hardware ↳ If necessary, adjust device-level requirements that drive changes to the system or hardware architecture ↳ Generate architecture security views ↳ Perform threat modeling ↳ Estimate and evaluate risk (security, safety, etc.) ↳ Determine necessary controls ↳ Evaluate whether the system, hardware, and architecture are adequate for the controls ↳ Adjust the relevant requirements and architecture as needed ↳ Rinse and repeat until your entire architecture is amenable to cybersecurity At that point, you’re ready to design and implement. But don’t be surprised if you have to revisit requirements or architecture later. PS. Fellow system architects and cybersecurity experts: what did I miss? Keep in mind that the focus here is on architecture. PPS. If you’re open to reviewing the book pre-release, let me know in the Comments. ♻️ And please repost if you think this is helpful!

The most important wearable of the next decade won’t be something you show. It won’t sit on your wrist. It won’t light up. You may even forget it’s there. Health technology is shifting from devices we notice to systems that quietly work in the background. Lumia™ Health is a strong signal of that shift. They didn’t choose the wrist. They chose the ear. They didn’t optimize battery life. They removed charging altogether. A solar-powered earable, under one gram, always on, fueled by ambient light as life unfolds. No habits to build. No charging reminders. No data gaps because the device died. But the real innovation isn’t convenience. It’s what becomes measurable. The wrist captures movement. Steps. Heart rate. Activity trends. The ear opens access to cephalic blood flow and that’s how blood reaches the brain in real time. That matters for symptoms people experience daily: brain fog, dizziness, fatigue, head pressure. Not acute illness. Not “nothing” either. These signals live between annual checkups and lived experience that quietly shaping focus, energy, and performance. With continuous sensing, context appears: during work, under stress, in recovery. Health stops being episodic. It becomes adaptive. Instead of reviewing data after the fact, the system responds as conditions change by detecting early shifts, linking them to behavior and environment, and guiding action before symptoms escalate. This is the next phase of wearables: less attention, more intelligence. From a health futurist’s lens, three forces are converging: • Invisible design over visible tech • Deep physiology over surface metrics • Continuous guidance over periodic insight Lumia™ Health sits right at that intersection. We’re moving beyond the wrist. Beyond dashboards. Beyond once-a-year health. Toward silent, solar, brain-aware systems that work with us, not on us. #wearabletech #thewearablesexpert How do you see earables and invisible wearables redefining health products?

An AI model that "kind of" works isn’t good enough. Here’s 10 principle form the last IMDRF : 1) Define a clear intended use & involve experts Outline a precise intended use that meets clinical needs. Engage experts across disciplines to refine it and assess risks at every stage. 2) Strong engineering, design & security practices Ensure traceability, reproducibility, and data integrity. Apply robust security and risk management to protect patient safety. 3) Representative datasets for clinical evaluation Use datasets that reflect the real patient population. Diversity and sufficient size help ensure unbiased performance. 4) Independent training & test datasets Keep training and test datasets completely separate. Perform external validation based on risk levels. 5) Fit-for-purpose reference standards Use clinically relevant standards aligned with the intended use. If no standard exists, document the rationale for selection. 6) Model choice aligned with data & intended use Ensure model design fits the data and mitigates risks. Set clear performance goals and account for variability. 7) Human-AI interaction in device assessment Evaluate performance within clinical workflows. Consider human factors like skill level, autonomy, and misuse risks. 8) Clinically relevant performance testing Assess real-world performance independently from training data. Test across patient subgroups and factor in human-AI interactions. 9) Clear & essential user information Communicate intended use, limitations, and updates transparently. Ensure users understand model function, risks, and feedback mechanisms. 10) Ongoing monitoring & retraining risk management Continuously monitor models to ensure safety and performance. Use risk-based safeguards to manage bias, overfitting, and dataset drift. Developing AI/ML medical devices? These principles should be your foundation. Source: Good machine learning practice for medical device development: Guiding principles / IMDRF/AIML WG/N88 FINAL:2025

🔬 A New Era in Medicine: First-Ever 3D-Printed Windpipe Implanted in Cancer Survivor In a groundbreaking medical achievement, South Korean scientists have successfully implanted a 3D-printed trachea (windpipe) into a patient — marking a world-first and redefining the future of regenerative medicine. The patient, a woman who had lost a part of her windpipe due to thyroid cancer surgery, became the recipient of this bioengineered miracle. The artificial trachea was developed using bio-ink composed of the patient's own living cells — including cartilage and mucosal cells — combined with a biodegradable polymer scaffold (PCL). This scaffold not only provided mechanical strength but also allowed the body to regenerate its own tissue around it. What makes this even more astonishing? ✅ No immunosuppressants were needed. Since the trachea was built from the patient’s own cells, her body accepted it naturally. ✅ Healthy blood vessels formed within 6 months, a critical sign of integration and healing. ✅ The patient regained normal function without the usual complications of transplant rejection. Led by Seoul St. Mary’s Hospital and T&R Biofab, this achievement is being hailed as a major milestone in personalized medicine and bioprinting technology. The future is no longer dependent solely on donors — it's now being printed, cell by cell. This opens the door for the possibility of 3D-printed lungs, kidneys, even hearts — tailored for the individual, reducing waitlists, and eliminating the risk of rejection. We are witnessing the dawn of a medical revolution where organs won’t just be donated… they’ll be designed. #RegenerativeMedicine #3DPrinting #HealthcareInnovation #Biotech #FutureOfMedicine #MedicalBreakthrough #OrganTransplant 🪻Ram Sharma 🪻