Biomedical Engineering Device Development

5 key developments this month in Wearable Devices supporting Digital Health ranging from current innovations to exciting future breakthroughs. And I made it all the way through without mentioning AI… until now. Oops! >> 🔘Movano Health has received FDA 510(k) clearance for its EvieMED Ring, a wearable that tracks metrics like blood oxygen, heart rate, mood, sleep, and activity. This approval enables the company to expand into remote patient monitoring, clinical trials, and post-trial management, with upcoming collaborations including a pilot study with a major payor and a clinical trial at MIT 🔘ŌURA has launched Symptom Radar, a new feature for its smart rings that analyzes heart rate, temperature, and breathing patterns to detect early signs of respiratory illness before symptoms fully develop. While it doesn’t diagnose specific conditions, it provides an “illness warning light” so users can prioritize rest and potentially recover more quickly 🔘A temporary scalp tattoo made from conductive polymers can measure brain activity without bulky electrodes or gels simplifying EEG recordings and reducing patient discomfort. Printed directly onto the head, it currently works well on bald or buzz-cut scalps, and future modifications, like specialized nozzles or robotic 'fingers', may enable use with longer hair 🔘Researchers have developed a wearable ultrasound patch that continuously and non-invasively monitors blood pressure, showing accuracy comparable to clinical devices in tests. The soft skin patch sensor could offer a simpler, more reliable alternative to traditional cuffs and invasive arterial lines, with future plans for large-scale trials and wireless, battery-powered versions 🔘According to researchers, a new generation of wearable sensors will continuously track biochemical markers such as hydration levels, electrolytes, inflammatory signals, and even viruses, from bodily fluids like sweat, saliva, tears, and breath. By providing minimally invasive data and alerting users to subtle health changes before they become critical, these devices could accelerate diagnosis, improve patient monitoring, and reduce discomfort (see image) 👇Links to related articles in comments #DigitalHealth #Wearables

Quality isn’t expensive. Poor quality is. Most quality systems look good on paper. Reality tells a different story. ISO 13485 isn’t just another standard. It’s how you keep patients safe. Lost in the ISO maze? Here’s your practical guide through it: 1. Quality Management System (QMS) ↳ The foundation of everything you build • Design Controls  • Training management • Requirements management • Supplier Qualification • Product Record Control  • Quality Management 2. Risk-Based Thinking (RBT) ↳ Spot problems before they happen ↳ Put smart solutions in place early ↳ Stay ahead of what could go wrong 3. Design Controls ↳ Track every step with purpose ↳ Verify before moving forward ↳ Turn ideas into trusted products 4. CAPA Process ↳ Fix issues at their root ↳ Make solutions stick ↳ Learn from each problem 5. Post-Market Surveillance ↳ Your eyes in the real world ↳ Listen to what users tell you ↳ Turn feedback into improvement 6. QMS Structure ↳ Build consistency into everything ↳ Keep records that tell the story ↳ Make quality automatic 7. Implementation Best Practices ↳ Get real leadership commitment ↳ Train until it becomes natural ↳ Never stop improving 8. Smart Audit Strategy ↳ Keep internal checks honest ↳ Stay ahead of regulators ↳ Build trust through transparency These parts work together. Each one makes the others stronger. Remember: ISO 13485 builds more than compliance. It builds trust that saves lives. Which part challenges you most? ♻️ Find this valuable? Repost for your network. Follow Bastian Krapinger-Ruether expert insights on MedTech compliance and QM.

The Medical Device Iceberg: What’s hidden beneath your product is what matters most. Your technical documentation isn’t "surface work". It’s the foundation that the Notified Body look at first. Let’s break it down ⬇ 1/ What is TD really about? Your Technical Documentation is your device’s identity card. It proves conformity with MDR 2017/745. It’s not a binder of loose files. It’s a structured, coherent, evolving system. Annexes II & III of the MDR guide your structure. Use them. But make it your own. 2/ The 7 essential pillars of TD: → Device description & specification → Information to be supplied by the manufacturer → Design & manufacturing information → GSPR (General Safety & Performance Requirements) → Benefit-risk analysis & risk management → Product verification & validation (including clinical evaluation) → Post-market surveillance Each one matters. Each one connects to the rest. Your TD is not linear. It’s a living ecosystem. Change one thing → It impacts everything. That’s why consistency and traceability are key. 3/ Tips for compiling TD: → Use one “intended purpose” across all documents → Apply the 3Cs: ↳ Clarity (write for reviewers) ↳ Consistency (same terms, same logic) ↳ Connectivity (cross-reference clearly) → Manage it like a project: ↳ Involve all teams ↳ Follow MDR structure ↳ Trace everything → Use “one-sheet conclusions” ↳ Especially in risk, clinical, V&V docs ↳ Simple, precise summaries → Avoid infinite feedback loops: ↳ One doc, one checklist, one deadline ↳ Define “final” clearly 4/ Best practices to apply: → Add a summary doc for reviewers → Update documentation regularly → Create a V&V matrix → Maintain URS → FRS traceability → Hyperlink related docs → Provide objective evidence → Use searchable digital formats → Map design & mfg with flowcharts Clear TD = faster reviews = safer time to market. Save this for your next compilation session. You don't want to start from scratch? Use our templates to get started: → GSPR, which gives you a predefined list of standards, documents and methods. ( https://lnkd.in/eE2i43v7 ) → Technical Documentation, which gives you a solid structure and concrete examples for your writing. ( https://lnkd.in/eNcS4aMG )

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

Medical checkups can be mentally and physically stressful for patients. Will it hurt? How long will it take? Then there's the uncertainty before the diagnosis. But simply not going is not an option. That's why we do everything we can to make examinations as comfortable as possible for patients. It starts with the human-centered design of our modalities. Beginning in the development phase, we already take the patient's perspective into account, even working together to find the best solution. We also collaborate closely with the professionals who will be running the device for hours. For these experts, a safe, comfortable, and easy-to-operate workplace is essential. This is what human-centered innovation means to us at Siemens Healthineers: Our innovations are designed for patients and healthcare professionals alike – for everyone, everywhere, sustainably. These individuals are either in a personally sensitive situation – or it’s their job and passion to help others. For both groups, human-centered design is key to strengthening trust and enhancing the human side of healthcare. A solution's impact on a clinical workflow isn't determined by the range of technical functions it offers, but rather by how it provides those functions in an understandable, accessible, and practical way. Take mammography as an example – a particularly sensitive screening that is extremely important in our joint fight against cancer. After all, breast cancer is the most common type of cancer for half of humanity: Every minute, four women worldwide are diagnosed with this disease. Early detection is crucial, which is why the examination must not be daunting. As studies indicate, women with perceived pain or unpleasantness were more likely to avoid future mammograms. For this reason, designing medical devices to create a calming environment and promote a sense of safety for patients plays an important role in healthcare delivery.

BREAKING! The FDA just released this draft guidance, titled Artificial Intelligence-Enabled Device Software Functions: Lifecycle Management and Marketing Submission Recommendations, that aims to provide industry and FDA staff with a Total Product Life Cycle (TPLC) approach for developing, validating, and maintaining AI-enabled medical devices. The guidance is important even in its draft stage in providing more detailed, AI-specific instructions on what regulators expect in marketing submissions; and how developers can control AI bias. What’s new in it? 1) It requests clear explanations of how and why AI is used within the device. 2) It requires sponsors to provide adequate instructions, warnings, and limitations so that users understand the model’s outputs and scope (e.g., whether further tests or clinical judgment are needed). 3) Encourages sponsors to follow standard risk-management procedures; and stresses that misunderstanding or incorrect interpretation of the AI’s output is a major risk factor. 4) Recommends analyzing performance across subgroups to detect potential AI bias (e.g., different performance in underrepresented demographics). 5) Recommends robust testing (e.g., sensitivity, specificity, AUC, PPV/NPV) on datasets that match the intended clinical conditions. 6) Recognizes that AI performance may drift (e.g., as clinical practice changes), therefore sponsors are advised to maintain ongoing monitoring, identify performance deterioration, and enact timely mitigations. 7) Discusses AI-specific security threats (e.g., data poisoning, model inversion/stealing, adversarial inputs) and encourages sponsors to adopt threat modeling and testing (fuzz testing, penetration testing). 8) And proposed for public-facing FDA summaries (e.g., 510(k) Summaries, De Novo decision summaries) to foster user trust and better understanding of the model’s capabilities and limits.

AI in Healthcare Sepsis infection is one of the largest causes of deaths in hospitals, estimated 11 m deaths/year. AI can help. After a patient checks into the emergency ward of a hospital, AI can look into 150 patient variables like lab results, vital signs, current medications, medical history, demographics to predict risk profile for possible sepsis. Staying vigilant has brought down sepsis incidence in hospitals ! I just gave you one example of how AI can help in healthcare. Few more … DIAGNOSIS – GE is using gen AI for multi modal integration from sources like imaging, genomics, pathology to help a clinician in diagnosis. Another ex is ischemic stroke where the image has to be read by a radiologist quickly to identify the clot in the brain. This can be done by AI when radiologists are busy or limited in number. This speed in diagnosis can save lives. REMOTE PATIENT CARE – We are know that there is a demand & supply mismatch in doctors and nurses. Monitoring devices with AI can send a notification to the healthcare professionals to visit the patient as and when needed saving time. Such efficient remote care limits the number of days patient has to spend in the hospital thereby reducing cost of stay which is very helpful for patients and insurance companies. AI-trained Chatbots have shown the potential to answer patient questions when doctors are not available. DRUG DISCOVERY – With millions of people waiting for the approval of new medicines, bringing a drug to market still takes on average more than 10 years and costs over 1.9 billion Euros on average. Merck has launched a drug discovery software that identifies compounds from over 60 billion possibilities based on key properties like non toxicity, solubility and stability in the body. Insilico Medicine, a biotech company out of Hong Kong is the first company where an AI discovered drug has entered phase II clinical trials in US and China. CLINICAL TRIALS - AI can help in trials through patient recruitment (through analysing patient health records and identifying most suitable candidates thereby reducing recruitment time), patient monitoring (by identifying adverse events or complications real time), protocol design, trial site selection, predict enrolment rates, data analysis (AI can often spot patterns and correlations that might be missed by humans) and cost efficiency by automating a lot of the admin paperwork involved in trials. MANUFACTURING– AI can predict machine failure and schedule equipment maintenance before breakdown occurs. It can inspect products and detect defects more accurately than humans, it also ensures timely delivery of raw materials through analysis and prediction of typical delays due to logistics, weather, shortages etc. Way ahead - I have only skimmed the surface & covered a few areas above. There is no doubt that AI can transform healthcare in many way however the challenges of data privacy and related ethics, prohibitive costs and unclear regulations remain.

🍼Revolutionizing Prenatal Monitoring: Terna Wireless Handheld Ultrasound Imagine a device the size of a power bank that allows you to visualize your baby in seconds right from your smartphone. This isn’t science fiction; it’s happening now. Terna Wireless Handheld Ultrasound Scanner brings professional grade imaging into the comfort of home, letting expectant parents connect with their unborn child in real time. How it works: • Piezoelectric Probe: Converts electricity into high frequency sound waves, which bounce off tissues and return as signals to form images. -- • Wireless Transmission: Uses Wi-Fi/Bluetooth and System on Chip (SoC) tech to send real-time B-Mode images directly to your phone. -- • Optimized Frequency: 2–5 MHz convex probe balances depth and resolution, letting you see your baby clearly without sacrificing safety. -- • ALARA Safety Principles: Termal Index (TI) and Mechanical Index (MI) remain within safe limits for short, controlled sessions. Key Takeaways: ✔ Not a replacement for clinical ultrasound or medical advice. ✔ Ideal for bonding, keepsake images, and parental engagement. ✔ Cloud based AI can assist in basic measurements, though it’s not a diagnostic tool. Why it matters: Radiology is evolving. 2026 will mark a new era where parents can safely and instantly visualize their babies at home, bridging technology, convenience, and emotional connection. Discussion point for professionals: How will portable, AI-assisted imaging change patient engagement, prenatal care, and family experiences in the next 5 years? —————————————— 𝗙𝗼𝗹𝗹𝗼𝘄 👉Muhammet Furkan Bolakar and 𝗮𝗰𝘁𝗶𝘃𝗮𝘁𝗲 𝘁𝗵𝗲 𝗯𝗲𝗹𝗹𝗹 🔔 for more updates on how #robotics, #automation and #science are shaping the future. Robot Technology: RoboSapienss Science Biology: Mr.Biyolog Digital Marketing: Bignite Digital —————————————— Philipp Kozin, PhD, MBA Prisca Ekhaguere, CSc Florian Palatini Miloš Kučera Eduardo BANZATO Amir Sanatkar Amine BOUDER Christine Raibaldi Marcus Scholle Alexey Navolokin Sascha M. Koeppel Manuel Barragan #MedicalInnovation #Ultrasounde #HealthTech

🤩 Sensor Interface Circuit for Biomedical Devices & Biosensors 💥 💝 Learn How to Interface Glucose, Lactate and other Sensors with MCU 🧐 At the heart of most of these biosensors is LMP91000 by Texas Instruments which is a programmable analog front-end for use in micro-power electrochemical sensing applications. It provides a complete signal path solution between a sensor and a microcontroller that generates an output voltage proportional to the cell current. It supports multiple electrochemical sensors such as: 3-lead toxic gas sensors and 2-lead galvanic cell sensors. The core of the LMP91000 is a potentiostat circuit. It consists of a differential input amplifier used to compare the potential between the working and reference electrodes to a required working bias potential (set by the Variable Bias circuitry). The error signal is amplified and applied to the counter electrode (through the Control Amplifier - A1). Any changes in the impedance between the working and reference electrodes will cause a change in the voltage applied to the counter electrode, in order to maintain the constant voltage between working and reference electrodes. A Transimpedance Amplifier connected to the working electrode, is used to provide an output voltage that is proportional to the cell current. The working electrode is held at virtual ground (Internal ground) by the transimpedance amplifier. The potentiostat will compare the reference voltage to the desired bias potential and adjust the voltage at the counter electrode to maintain the proper working-to-reference voltage. How to build a circuit for your biomedical application? Orlando Hoilett built KickStat, a miniaturized potentiostat using LMP91000 with the processing power of the Arm Cortex-M0+ SAMD21 Microchip Technology Inc. microcontroller on a custom-designed 21.6 mm by 20.3 mm circuit board. By incorporating onboard signal processing via the SAMD21, h he achieved 1mV voltage resolution and an instrumental limit of detection of 4.5nA in a coin-sized form factor. He measured the faradaic current of an anti-cocaine aptamer using cyclic voltammetry and square wave voltammetry and demonstrated that KickStat’s response was within 0.6% of a high-end benchtop potentiostat. To further support others in electrochemical biosensors development, he has made KickStat’s design and firmware available in an online GitHub repository. 📢 KickStat Project: "KickStat: A Coin-Sized Potentiostat for High-Resolution Electrochemical Analysis" doi: https://lnkd.in/eFjdpWjQ GitHub repo: https://lnkd.in/eJAvT_kR Datasheet: https://lnkd.in/eKvkGWCt 💜 Share it with your biosensors, biomedical wearable network 👌 #biosensors #wearables #sensors #electronics #Potentiostat #lmp91000