Diagnostic Device Engineering

When Debugging Meets Portability – An 𝗢𝘀𝗰𝗶𝗹𝗹𝗼𝘀𝗰𝗼𝗽𝗲 in the Palm of Your Hand In electronics, seeing your signal is often the key to solving the puzzle. Here’s a perfect example — an 𝗢𝗟𝗘𝗗 𝗺𝗶𝗻𝗶 𝗼𝘀𝗰𝗶𝗹𝗹𝗼𝘀𝗰𝗼𝗽𝗲 directly mounted on a microcontroller board, showing a 1 kHz sine wave with crystal clarity. Traditionally, oscilloscopes are bench-only tools: large, expensive, and not exactly carry-in-your-pocket friendly. But with today’s embedded displays and firmware libraries, we can shrink that capability to a 0.96-inch screen — without compromising on core functionality. 𝗪𝗵𝗮𝘁’𝘀 𝗵𝗮𝗽𝗽𝗲𝗻𝗶𝗻𝗴 𝗵𝗲𝗿𝗲? The OLED is driven by the microcontroller, which samples an analog signal in real-time. The signal is processed and rendered as a waveform directly on the display. Alongside, it shows essential parameters like amplitude, voltage range, and time scale. 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗶𝘀 𝗽𝗼𝘄𝗲𝗿𝗳𝘂𝗹: 🔹 Field diagnostics without bulky gear – Engineers can troubleshoot circuits directly in the field. 🔹 Real-time embedded feedback – No need to connect to an external PC or scope for basic measurements. 🔹 Cost-effective – Makes oscilloscopes accessible to hobbyists, students, and small labs. 🔹 Customizable for any application – From waveform capture to sensor monitoring, the display can be tailored for specific needs. 𝗣𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹 𝘂𝘀𝗲𝘀 𝗯𝗲𝘆𝗼𝗻𝗱 𝘄𝗮𝘃𝗲𝗳𝗼𝗿𝗺𝘀: - IoT nodes with live status displays - Portable signal analyzers for audio and sensors - On-device calibration tools for field techs - Educational kits for teaching electronics fundamentals 💡This is a great reminder that innovation isn’t always about adding more power — sometimes it’s about making the same power more accessible. 𝗥𝗲𝗳𝗲𝗿𝗲𝗻𝗰𝗲: For those interested in upgrading a mini DSO with advanced features like trigger modes, frequency display, and OLED brightness control, check out this detailed guide on Instructables: https://lnkd.in/gT7mewe2 𝗪𝗵𝗮𝘁’𝘀 𝘆𝗼𝘂𝗿 𝘁𝗮𝗸𝗲? Would you integrate a micro-display oscilloscope into your next project, or do you still prefer a dedicated lab scope? #EmbeddedSystems #Oscilloscope #ElectronicsEngineering #IoT #WaveformAnalysis #Innovation #Prototyping

Bringing AUTOSAR Diagnostics to Life: DCM, DEM, RTE, NvM & FEE – The Invisible Backbone of ECU Communication In modern automotive systems, seamless diagnostic communication and fault handling are critical—not just for compliance, but also for safety, serviceability, and long-term performance. The AUTOSAR stack is engineered to support this with a coordinated suite of modules. Here’s how key components interact in diagnostic workflows: 1. DCM – Diagnostic Communication Manager Facilitates the diagnostic interface between external tools (testers) and ECUs. It interprets UDS/OBD requests and manages security access, sessions, and timing. 2. DEM – Diagnostic Event Manager The brain behind fault logging. DEM tracks DTCs (Diagnostic Trouble Codes), captures snapshot/freeze data, manages fault aging, and triggers UDS events. 3. RTE – Runtime Environment Acts as a middleware between SWCs (Software Components) and BSW modules. For diagnostics, it ensures data flow between DEM/DCM and application SWCs, facilitating trigger-based events. 4. NvM – Non-volatile Memory Manager Ensures persistence of diagnostic and configuration data. NvM interfaces with DEM to store DTCs and associated metadata securely across ignition cycles. 5. FEE – Flash EEPROM Emulation Underlying memory abstraction layer that allows NvM to access non-volatile storage (like EEPROM/Flash) reliably. Essential for storing diagnostic data without risking hardware wear. Why this matters: When these layers work in harmony, we get robust diagnostic fault management, data persistence, tester tool interaction, and standard compliance (UDS/OBD). This is essential not only for development but for servicing vehicles across their lifecycle. Diagnostics isn’t just about error codes—it’s about a highly coordinated architecture that keeps our vehicles safe and smart. #AUTOSAR #DCM #DEM #NvM #FEE #RTE #ECUDevelopment #Diagnostics #UDS #AutomotiveEngineering #BSW #FunctionalSafety

🔬 Miniaturizing Optical Innovation: Lens Design for Scanning Fiber Endoscopes Excited to share insights from Andrew Daniel Rocha's paper that ignited our recent collaboration with the University of Arizona BIO5 Institute and Ansys Optics. In this paper, published in Optical Engineering, we explore the optical design strategies behind scanning fiber endoscopes (SFEs)—a class of ultra-miniature imaging devices with transformative potential in biomedical diagnostics. 📌 Key Highlights: We present a comprehensive framework for designing the illumination optics of SFEs, focusing on the piezoelectric scanner tube architecture. The paper dives into optical principles, system-level trade-offs, and performance metrics critical for achieving high-resolution imaging in devices with diameters as small as 1–2 mm. Topics include: Beam shaping and projection optics Field-of-view vs. resolution trade-offs Aberration control in miniature lens systems Integration challenges with fiber scanning mechanisms 🎯 Whether you're working on endoscopic imaging, optical system miniaturization, or biomedical device design, this paper offers practical guidance and design heuristics for building next-gen imaging tools. 📖 Read the full paper here: Lens design strategies for miniature scanning fiber endoscopes (https://lnkd.in/gcTJqJmz) #OpticalEngineering #Endoscopy #BiomedicalImaging #FiberOptics #LensDesign #Miniaturization #SPIE

Last month, a founder called us with a surprise: their AI-based diagnostic tool had just been classified as an In Vitro Diagnostic (IVD) under the IVDR. Their first reaction: “But it’s just software!” It’s a moment we’ve seen often. The line between software and diagnostics is disappearing fast, and for many innovators, this means their app, algorithm, or data platform is now subject to one of Europe’s most stringent regulations. IVDR doesn’t just redefine compliance; it redefines accountability. Software that interprets biological or diagnostic data must now show clinical performance, cybersecurity robustness, and traceability, just like traditional test kits or analyzers. This shift is catching companies off guard, not because the rules are unclear, but because they assume “code” operates outside the lab. In our reviews, three mistakes keep showing up: ❌ Treating software validation like IT testing and not clinical evidence. ❌ Weak traceability between code logic, intended use, and risk controls. ❌ Missing cybersecurity and PMS plans for continuous updates. Here’s how we guide teams stepping into this “Software as IVD” frontier: ✅ Clearly define the intended purpose and diagnostic claim. ✅ Map algorithm outputs to clinical relevance. ✅ Validate analytical + clinical performance, not just functionality. ✅ Align lifecycle with IEC 62304 and IVDR Annex XIII. ✅ Set up PMS to capture real-world software performance. As the IVDR era unfolds, every diagnostic startup must ask: Is our software just functioning, or is it clinically proven to perform? #IVDR #SaMD #DigitalHealth #IVDSoftware #RegulatoryStrategy #MedTechLeadership #Elexes #AI #SAMD #IVD #Regulatory #Medicaldevice

The science is often elegant. The engineering is clever. But there’s a recurring challenge I see once these systems reach the edge of translation: the algorithmic decision layer. For a diagnostic to reach physicians, it’s not enough to produce raw data, whether that’s a CCD image, a fluorescence signal, or a multiplexed panel. The data has to be translated into a robust yes-or-no output that regulators, clinicians, and patients can trust. This is where things get messy. Biological noise, variability in sample prep, imaging artifacts, edge cases across populations, all conspire to undermine an algorithm that looked good on a handful of pilot runs. And unlike early-stage research, there is no margin for ambiguity. A diagnostic that wavers between “yes,” “no,” or “maybe” won’t clear FDA review. I’ve come to think of this as the silent bottleneck of diagnostics innovation. We don’t talk about it as much because it isn’t glamorous. But it is decisive. Brilliant biology and slick devices won’t matter if the decision algorithm can’t prove reproducibility, specificity, and robustness at scale. The startups that thrive are those who recognize this early. They invest not just in CRISPR enzymes or cartridge design, but in building data pipelines and algorithmic frameworks that can survive regulatory scrutiny. They look beyond accuracy in ideal conditions and focus on edge-case resilience, bias detection, and traceability. In an era where diagnostics are increasingly multimodal, merging molecular biology, engineering, and AI, the winners will not just be those who invent new assays. They will be those who can guarantee that the yes-or-no answer is one you can trust, every time. That, to me, is the frontier where science meets regulation. And it’s where the future of diagnostics will be decided.

Are you building an AI/ML-enabled neurology diagnostic device? We have some interesting facts for you: Predetermined Change Control Plans are a new regulatory tool that allow companies to “pre-clear” certain changes within a 510(k), De Novo, or PMA. Of the 6 most recent AI/ML neurology diagnostic devices cleared in 2025, only 2 included a PCCP: • Ceribell │ AI-Powered Point-of-Care EEG Infant Seizure Detection Software – Deep learning for EEG seizure detection in infants • Nox Medical DeepRESP v2.0 – AI and rule-based models for sleep study analysis • Brain Electrophysiology Laboratory NEAT 001 – Machine learning for automatic sleep staging from EEG • Holberg EEG AS, a Natus company autoSCORE V2.0.0 – Deep learning AI model for EEG abnormality detection • Cognoa Canvas Dx – Machine learning for autism spectrum disorder diagnosis aid • LVIS NeuroMatch – Deep learning for EEG source localization in epilepsy Unlike radiology (where FDA reviewers see high volumes of AI/ML submissions and are fluent in ML architectures, training/test splits, and performance benchmarking), neurology panels see fewer AI/ML devices. Panels with less AI/ML experience tend to be more conservative with PCCPs. The administrative overhead is already high for these new regulatory tools, but it's even higher when you're working with reviewers who need more foundational education about your algorithm. Predetermined Change Control Protocols work best when: → You have well-understood, repeatable changes you plan to make multiple times → Each change would otherwise require a separate 510(k) submission → You can define precise acceptance criteria and validation protocols upfront → Your documentation educates reviewers on ML fundamentals without assuming prior AI/ML fluency → The changes stay within your original intended use Best practices for PCCPs in low-AI-volume panels: → Expect to provide more detail than you would for radiology devices → Translate ML concepts into clinical language the panel will understand → Consider a separate Pre-Sub specifically for your PCCP to gauge panel comfort level → If the PCCP adds relatively little value, it may not be worth the extra scrutiny yet Your submission needs to educate, not just demonstrate. You need reviewers to understand not just *what* your algorithm does, but *how* and *why* it's safe to modify it within your planned parameters. --- We specialize in translating complex ML architectures into FDA-ready documentation that panels with limited AI/ML experience can confidently review. Whether you need a PCCP or standard change control, we help you navigate review divisions where AI fluency varies: Read more here: https://hubs.li/Q042nH4_0 #FDA #MedicalDevices #RegulatoryStrategy #PCCP #AIinHealthcare #Neurology #D...

Your sweat is the new 𝐛𝐥𝐨𝐨𝐝 𝐭𝐞𝐬𝐭. 💦 𝐖𝐡𝐲 𝐲𝐨𝐮𝐫 𝐦𝐨𝐫𝐧𝐢𝐧𝐠 𝐰𝐨𝐫𝐤𝐨𝐮𝐭 𝐢𝐬 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐚 𝐝𝐢𝐚𝐠𝐧𝐨𝐬𝐭𝐢𝐜 𝐥𝐚𝐛 𝐢𝐧 𝐝𝐢𝐬𝐠𝐮𝐢𝐬𝐞. Imagine if your smartwatch didn’t just tell you you burned 300 calories, but told you your 𝐜𝐨𝐫𝐭𝐢𝐬𝐨𝐥 is spiking or that you’re low on 𝐦𝐚𝐠𝐧𝐞𝐬𝐢𝐮𝐦 before the cramp hits. The “holy grail” of MedTech has always been 𝐧𝐨𝐧-𝐢𝐧𝐯𝐚𝐬𝐢𝐯𝐞 𝐦𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠. For decades, we’ve been obsessed with blood. But now, the shift is happening in 𝐬𝐰𝐞𝐚𝐭 𝐚𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬. 🛠️ 𝐓𝐡𝐞 𝐓𝐞𝐜𝐡 𝐃𝐞𝐜𝐨𝐝𝐞𝐫: 𝐌𝐢𝐜𝐫𝐨𝐟𝐥𝐮𝐢𝐝𝐢𝐜 “𝐒𝐤𝐢𝐧 𝐏𝐚𝐭𝐜𝐡𝐞𝐬” I’ve been tracking the shift from bulky sensors to 𝐟𝐥𝐞𝐱𝐢𝐛𝐥𝐞, 𝐛𝐢𝐨-𝐢𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐞𝐝 𝐩𝐚𝐭𝐜𝐡𝐞𝐬. These aren’t just stickers. They’re tiny engineering systems. How it works: 1. 𝐂𝐚𝐩𝐢𝐥𝐥𝐚𝐫𝐲 𝐀𝐜𝐭𝐢𝐨𝐧 Micro-channels wick sweat away from your skin. 2. 𝐄𝐥𝐞𝐜𝐭𝐫𝐨𝐜𝐡𝐞𝐦𝐢𝐜𝐚𝐥 𝐒𝐞𝐧𝐬𝐢𝐧𝐠 Enzymes react with biomarkers like glucose, lactate, or vitamin C to generate a signal. 3. 𝐓𝐡𝐞 𝐃𝐞𝐜𝐨𝐝𝐢𝐧𝐠 That signal is sent to your phone, giving a 𝐦𝐨𝐥𝐞𝐜𝐮𝐥𝐚𝐫-𝐥𝐞𝐯𝐞𝐥 𝐛𝐫𝐞𝐚𝐤𝐝𝐨𝐰𝐧 in real time. 💡 This is the bridge between 𝐜𝐨𝐧𝐬𝐮𝐦𝐞𝐫 𝐭𝐞𝐜𝐡 and 𝐜𝐥𝐢𝐧𝐢𝐜𝐚𝐥 𝐝𝐢𝐚𝐠𝐧𝐨𝐬𝐭𝐢𝐜𝐬. • For Engineers We’re moving from rigid chips to 𝐛𝐢𝐨-𝐢𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐞𝐝 𝐞𝐥𝐞𝐜𝐭𝐫𝐨𝐧𝐢𝐜𝐬 that move with the body. • For Med Students Diagnostics are shifting from labs to 𝐫𝐞𝐚𝐥-𝐰𝐨𝐫𝐥𝐝 𝐞𝐧𝐯𝐢𝐫𝐨𝐧𝐦𝐞𝐧𝐭𝐬 like gyms, offices, and homes. • For Professionals It’s about 𝐩𝐫𝐞𝐯𝐞𝐧𝐭𝐚𝐭𝐢𝐯𝐞 𝐝𝐚𝐭𝐚. Fix the imbalance today, not after you’re sick. Companies like Epicore Biosystems are already pushing the boundaries of sweat analytics, while players like Abbott and Apple are shaping what continuous health monitoring could look like at scale. 𝐌𝐲 𝐓𝐚𝐤e We’re moving toward a world where your body is constantly “𝐭𝐚𝐥𝐤𝐢𝐧𝐠.” The real question is Are we ready to 𝐥𝐢𝐬𝐭𝐞𝐧 to what our sweat is saying? 💉🚫What’s your take? Would you wear a “𝐬𝐦𝐚𝐫𝐭 𝐩𝐚𝐭𝐜𝐡”, if it meant never needing routine blood tests again? #HealthTech #WearableTech #MedTech2026 #EngineeringTheFuture #JiyaTomar

Ironically, most people check Instagram more times a day than they do their medical checkups in a decade… 🤷 Longevity conversations often focus on breakthrough therapies, while it is foundational infrastructure that shapes real healthspan outcomes more than anything. One of those foundations is blood testing 🩸. Here is a shout out to the latest Substack newsletter from LongeVC team (kudos to Vlad Cernouţan), where authors examine why blood diagnostics remain a limiting factor for preventive medicine and longevity, and how SiPhox Health, a LongeVC portfolio company, approaches this problem. The status quo is that the current blood testing experience relies on centralized labs, venous draws, and long turnaround times. This structure produces infrequent measurements, which reduces visibility into slow biological shifts across cardiometabolic health, thyroid function, hormones, and inflammatory markers. Healthspan optimization depends on tighter feedback loops. On the other hand, at-home diagnostics introduce a demanding technical challenge:. ⚙️ Microliter-scale capillary samples require wide dynamic range, low limits of detection, stable multiplexing, controlled pre-analytics, and rigorous analytical validation. Solving all of these simultaneously defines the engineering frontier. But the good news is that recent advances in silicon photonics enable a new class of solutions. 🔬 SiPhox uses a microring resonator–based immunoassays that detect subtle refractive index changes when target proteins bind to antibodies on a photonic chip. Arrays of functionalized rings allow multiplexed protein measurement from small blood volumes with real-time optical readout. SiPhox brings semiconductor economics to diagnostics, basically, and combines it with a staged go-to-market strategy, operating mail-in testing today while advancing toward at-home use. 👉 This matters for longevity because behavior follows visibility. Continuous glucose monitoring demonstrated how frequent, interpretable biomarker data reshapes disease management. The same feedback-loop dynamics apply to lipid metabolism, inflammation, and hormonal regulation when testing becomes accessible at higher frequency. 💉📊 Our perspective at LongeVC often focuses on medical infrastructure. Because personalized longevity depends on affordable, frequent, multi-analyte testing that fits into everyday life! Diagnostics provide the measurement layer that makes optimization actionable. So, that’s the focus of this piece: the engineering, physics, and system design required to build durable foundations for population-scale healthspan gains. 📨 The full article is live on the Through The Longevity Lens newsletter on Substack, read it here and subscribe: https://lnkd.in/gve28Nqd