+ 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-2026-0899Certified recordPeer-reviewed

Stacked Ensemble Deep Learning Models for Accurate Detection and Size Stratification of Periapical Lesions on Intraoral Radiographs

Periapical lesions are challenging to detect on intraoral radiographs because of anatomical superimposition and reader variability. This study developed stacked deep learning ensembles for automated detection and radiographic size stratification of periapical lesions. In total, 146 radiographs comprising normal cases and three lesion-size categories were cropped around the root apex and augmented using predefined transformations. Five convolutional neural network backbones were trained, and their probability out…

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

Current reading — gain

Stacked ensembles combining five CNN backbones improved automated detection and size stratification of periapical lesions on cropped intraoral radiographs, reaching high accuracy and high sensitivity for very small lesions on internal testing.

What this doesn’t fix

Findings are based on a small, single-centre internal hold-out set, so performance estimates may be optimistic and need prospective multicentre external validation before clinical use.

Evidence

Reader signal

How should this claim be treated?

Cite this record

Truvace Impact Record TRV-2026-0899, v1: “Stacked Ensemble Deep Learning Models for Accurate Detection and Size Stratification of Periapical Lesions on Intraoral Radiographs.” Truvace, 2026-08-26. /record/TRV-2026-0899 (accessed at citation time). sha256 a6c305026e455fba…

Calibration history

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

  1. Certifiedv1a6c305026e45…

    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-2026-0899 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.