+ HEALTH Aims Intramuscular fat (IMF) is increasingly recognized as a marker of ectopic adiposity and adverse cardiome…+ HEALTH Periapical lesions are challenging to detect on intraoral radiographs because of anatomical superimposition a…± HEALTH Ultrasound imaging is an indispensable diagnostic tool, yet its profound reliance on operator expertise inher…+ HEALTH Objectives We adapted the individualized polysocial risk score (iPsRS), a machine learning model originally d…± HEALTH Melioidosis is a life-threatening infectious disease caused by Burkholderia pseudomallei ( Bp ). Rapid diagno…+ HEALTH Classification is a core machine learning task, and ensemble voting methods are widely used to improve predic…+ CLIMATE Microplastic (MP) pollution poses escalating environmental risks, demanding efficient and reproducible tools…± HEALTH Medicinal chemistry sits at the center of modern drug discovery, yet translating molecular designs into appro…
TruaceTracing the truth around AIWednesday, August 26, 2026
Subscribe
TRV-2026-0900Certified recordPeer-reviewed

Intramuscular Fat Assessed by AI-Assisted Muscle Ultrasound: Association With Cardiometabolic Risk Factors and Diabetic Nephropathy in Diabetes Mellitus

Aims Intramuscular fat (IMF) is increasingly recognized as a marker of ectopic adiposity and adverse cardiometabolic outcomes. Artificial intelligence (AI)-assisted ultrasound of the rectus femoris (RF) offers a non-invasive approach for quantifying IMF. This study evaluated the association of IMF with diabetes-related complications (particularly diabetic nephropathy) and metabolic risk factors in patients with diabetes mellitus (DM). Materials and methods In this cross-sectional study, outpatients from a tertia…

Health · G Space — documented gain · certified 2026-08-26 · v1 · article view · machine-readable

Current reading — gain

AI-assisted ultrasound of the rectus femoris quantified intramuscular fat percentage (FATi), which was independently associated with diabetic nephropathy and adverse metabolic profiles in patients with diabetes.

What this doesn’t fix

Cross-sectional single-center design cannot establish causality or temporal role of IMF in complications; authors note need for longitudinal and multicentre validation.

Evidence

Reader signal

How should this claim be treated?

Cite this record

Truvace Impact Record TRV-2026-0900, v1: “Intramuscular Fat Assessed by AI-Assisted Muscle Ultrasound: Association With Cardiometabolic Risk Factors and Diabetic Nephropathy in Diabetes Mellitus.” Truvace, 2026-08-26. /record/TRV-2026-0900 (accessed at citation time). sha256 dc80624eac49ef35…

Calibration history

Every change to this record since certification, in the open. None yet — the reading has held since it entered the record.

  1. Certifiedv1dc80624eac49…

    Certified into the record

Verify this record
How to verify without trusting this page

Fetch the canonical text of any version from /api/record/TRV-2026-0900 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.