đź‘…Can a Tongue Photo Flag Heart Disease?

Inside: diet-linked cancer risk, sweat biomarkers at CES, IVF AI, and faster brain mapping.

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AIHealthTech Insider: Issue #82

January 5, 2026

From tongue photos that hint at heart disease, to sweat that reveals metabolic limits, to subtle cellular shifts that quietly raise cancer risk, this issue highlights how AI is uncovering early, non-obvious signals across the body. These aren’t replacements for clinicians but powerful new layers that could make screening, diagnosis, and care more proactive, accessible, and precise.

Summaries are for education, not medical advice. Always verify locally before clinical use.

A new open-access study trained AI on 12,000+ tongue images from 906 angiography patients to predict coronary artery disease using features like tongue color, fissures, coating, moisture, and red spots. The model reached up to ~80% accuracy for specific artery stenosis, suggesting a fast, non-invasive screening add-on especially in low-resource settings.

Image Source: Scientific Reports

Why it matters

  • Explores a low-cost, non-invasive screening signal beyond ECG and imaging

  • Standardizes “tongue exam” findings with computer vision instead of subjective scoring

  • Could enable faster triage and earlier risk detection, pending broader validation

MIT researchers found that prolonged high-fat diets push liver cells into a stem-like survival state. While this helps cells cope with chronic metabolic stress, it also strips away normal liver function, and quietly primes the tissue for cancer giving tumors a critical head start years before they appear.

Image Source: cell.com

Why it matters

  • Explains why fatty liver disease often precedes liver cancer

  • Shows how survival adaptations can unintentionally fuel tumors

  • Highlights new molecular targets for cancer prevention

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New research shows that warmth and cold don’t just register on the skin -they help the brain decide whether a body feels like “mine.” Temperature signals shape body ownership, emotional regulation, and mental well-being, with disruptions linked to depression, trauma, and stroke-related body disconnect.

Image Source: cell.com

Why it matters

  • Shows temperature as a core signal for body awareness and emotional safety

  • Connects altered thermal perception to mental health and neurological conditions

  • Inspires sensory-based therapies and more lifelike prosthetic design

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Researchers built SmartEM, a system that embeds machine learning inside electron microscopy scanning. It does a fast first pass, then automatically re-scans only the “hard” regions at higher quality—cutting acquisition time dramatically while preserving accuracy. Result: up to ~7× faster neural circuit mapping.

Image Source: Nature Methods

Why it matters

  • Makes connectomics faster without trading off reconstruction quality

  • Reduces wasted microscope time by focusing slow scans only where needed

  • Helps scale brain mapping projects that typically take months to years

🩺CES 2026 Spotlight: PointFit PF-ACE 

PointFit is bringing sweat intelligence to CES 2026. PF-ACE is a skin-worn sensor that reads your body through sweat - no needles, no labs, no guesswork.

The Device
A lightweight, skin-conformal sweat sensor designed for continuous, real-time biomarker tracking during training and daily activity.

Smart + Performance Features

  • Sweat-Based Biomarkers: Measures lactate, electrolytes, and sweat rate

  • Real-Time Feedback: Delivers live insights for pacing, hydration, and endurance

  • Non-Invasive Design: No blood, no pain, no disruption

  • Personalized Profiling: Supports lactate threshold–based endurance planning

Why it matters
Your sweat knows when to push—and when to back off. PF-ACE turns that signal into actionable data, helping athletes and everyday users train smarter, not harder.

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A large multicenter study shows an AI platform can automatically detect, count, and measure ovarian follicles from routine ultrasound scans during IVF. Trained on 5,500+ scans across four countries, the system matched expert sonographers while cutting annotation time by 2.5Ă—, supporting faster and more scalable fertility monitoring.

Image Source: Journal of Assisted Reproduction and Genetics

Why it matters

  • Delivers expert-level follicle measurements without manual workload

  • Reduces time and cost during ovarian stimulation monitoring

  • Helps standardize IVF care across clinics and ultrasound systems

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Which Biomarker Wins?

Image Source: GPT 5.2/AIHealthTech Insider

Which of these non-invasive biomarkers interests you most for clinical adoption?

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