💡Exciting Announcement!

We are proud to introduce our next healthcare #WebAI application: Varicose Veins AI Consultant! 🩺

🧠 This custom hybrid AI is powered by a #Tensorflow.js neural network, enhanced with #Gemini Flash and #Deepseek.

🔍 What does it do?
✅ Provides individual risk estimation for developing varicose veins over a lifetime 🏥
✅ Delivers up-to-date insights on diagnosis, treatment, and prevention of the disease 📊💊
✅ Supports patients and healthcare professionals with the latest scientific knowledge 🔬📚

🌐 Try it out now! 👉

#AI #HealthTech #MedTech #MachineLearning #VaricoseVeins #MedicalAI


https://mlhs.ink/varicose/

🚀 Announcement: Introducing our latest MLHS Playground extension—Video Classifier!

This application enables you to easily create custom #WebAI models directly from live videos, including camera feeds or connected video capture devices. Just follow the simple, step-by-step instructions and start building privacy-safe #medicalAI models!

https://mlhs.ink/Playground/#SVC

🦾 Lost limbs can’t be fully restored — but bionics can bring motion back, on demand.

Bionic systems translate anatomical sensor data into precise movements. The more accurate the anatomical input, and the simpler its transformation into prosthetic motion, the closer we get to natural, intuitive control.

💡 B-mode ultrasound offers real-time, high-resolution imaging of muscle activity. But its interpretation is complex, and the anatomical variance between users makes it computationally expensive.

⚡️ Enter #WebAI — enabling real-time client-side processing and training, right in the browser. With it, users can train and retrain their bionic limb model independently, without external services or data transfers.

🚀 Check out the latest research from our journal showcasing Web AI–powered ultrasound control of a virtual bionic hand.

https://youtu.be/D-MYmzrhI7w

Research paper: https://doi.org/10.62487/vjzf7254

#bionics, #ultrasound, #prosthetics , #healthcareai , #medicalai

𝐒𝐲𝐧𝐭𝐡𝐞𝐭𝐢𝐜𝐚 𝐢𝐬 𝐥𝐢𝐯𝐞 — 𝐚𝐮𝐠𝐦𝐞𝐧𝐭 + 𝐜𝐥𝐞𝐚𝐧 𝐦𝐞𝐝𝐢𝐜𝐚𝐥 𝐢𝐦𝐚𝐠𝐞𝐬 𝐢𝐧 𝐨𝐧𝐞 𝐬𝐭𝐞𝐩.

If you build medical computer vision, you know the pain: real-world images often come with overlays, text/IDs, UI frames, borders, and other artefacts that make datasets noisy and expensive to clean.

𝐒𝐲𝐧𝐭𝐡𝐞𝐭𝐢𝐜𝐚 𝐡𝐞𝐥𝐩𝐬 𝐲𝐨𝐮:
- 𝑅𝑒𝑚𝑜𝑣𝑒 𝑐𝑜𝑚𝑚𝑜𝑛 𝑎𝑐𝑞𝑢𝑖𝑠𝑖𝑡𝑖𝑜𝑛 𝑎𝑟𝑡𝑒𝑓𝑎𝑐𝑡𝑠 (𝑎𝑛𝑛𝑜𝑡𝑎𝑡𝑖𝑜𝑛𝑠/𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑚𝑒𝑛𝑡𝑠, 𝑟𝑒𝑎𝑑𝑎𝑏𝑙𝑒 𝑡𝑒𝑥𝑡, 𝑈𝐼/𝑚𝑜𝑛𝑖𝑡𝑜𝑟 𝑓𝑟𝑎𝑚𝑒𝑠, 𝑏𝑙𝑎𝑐𝑘 𝑏𝑜𝑟𝑑𝑒𝑟𝑠/𝑏𝑎𝑐𝑘𝑔𝑟𝑜𝑢𝑛𝑑)
- 𝐺𝑒𝑛𝑒𝑟𝑎𝑡𝑒 𝑟𝑒𝑎𝑙𝑖𝑠𝑡𝑖𝑐 𝑎𝑐𝑞𝑢𝑖𝑠𝑖𝑡𝑖𝑜𝑛-𝑙𝑒𝑣𝑒𝑙 𝑣𝑎𝑟𝑖𝑎𝑛𝑡𝑠 𝑤ℎ𝑖𝑙𝑒 𝑝𝑟𝑒𝑠𝑒𝑟𝑣𝑖𝑛𝑔 𝑡ℎ𝑒 𝑜𝑣𝑒𝑟𝑎𝑙𝑙 𝑣𝑖𝑠𝑢𝑎𝑙 𝑖𝑑𝑒𝑛𝑡𝑖𝑡𝑦 𝑎𝑛𝑑 𝑓𝑖𝑛𝑑𝑖𝑛𝑔𝑠 — 𝑓𝑜𝑟 𝑎𝑢𝑔𝑚𝑒𝑛𝑡𝑎𝑡𝑖𝑜𝑛, 𝑐𝑙𝑎𝑠𝑠 𝑏𝑎𝑙𝑎𝑛𝑐𝑖𝑛𝑔, 𝑎𝑛𝑑 𝑠𝑡𝑟𝑒𝑠𝑠-𝑡𝑒𝑠𝑡𝑖𝑛𝑔 𝑝𝑖𝑝𝑒𝑙𝑖𝑛𝑒𝑠.

𝐒𝐮𝐩𝐩𝐨𝐫𝐭𝐞𝐝 𝐦𝐨𝐝𝐚𝐥𝐢𝐭𝐢𝐞𝐬:
🩻 𝑅𝑎𝑑𝑖𝑜𝑙𝑜𝑔𝑦: 𝑋𝑅, 𝐴𝑛𝑔𝑖𝑜/𝐷𝑆𝐴, 𝑀𝑅𝐼, 𝑈𝑙𝑡𝑟𝑎𝑠𝑜𝑢𝑛𝑑, 𝐶𝑇 (𝑖𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦-𝑝𝑟𝑒𝑠𝑒𝑟𝑣𝑖𝑛𝑔)
🔬 𝑀𝑖𝑐𝑟𝑜𝑠𝑐𝑜𝑝𝑦: ℎ𝑖𝑠𝑡𝑜𝑙𝑜𝑔𝑦 / 𝑐𝑦𝑡𝑜𝑙𝑜𝑔𝑦 / 𝑠𝑙𝑖𝑑𝑒𝑠
📷 𝑀𝑎𝑐𝑟𝑜𝑠𝑐𝑜𝑝𝑦: 𝑑𝑒𝑟𝑚𝑎𝑡𝑜𝑙𝑜𝑔𝑦 / 𝑒𝑛𝑑𝑜𝑠𝑐𝑜𝑝𝑦 / 𝑔𝑟𝑜𝑠𝑠 𝑖𝑚𝑎𝑔𝑒𝑠

Built on Gemini 3.0 Image.
Each session creates individual image links + a ZIP + a session page with all outputs.

🎁 Early access is open (free testing + discounted launch access).
Try it: https://mlhs.ink/synthetica/

#MedicalAI #ComputerVision #DatasetCuration #DataAugmentation #SyntheticData #Radiology #Pathology #MLinHealthScience