− HEALTH The FDA received a malfunction report involving Inogen one g5 oxygen concentrator, made by Inogen, Inc.. Rece…+ HEALTH Objective We systematically evaluated the diagnostic performance of artificial intelligence (AI)-assisted int…− HEALTH BackgroundFall-related injuries in older adults remain a major health concern, contributing to functional dep…− CLIMATE Background Chronic inflammation is a major driver of cardiovascular diseases, but mechanisms linking systemic…− HEALTH Disease macroecology relies on large, complex datasets to understand the biotic and abiotic factors shaping p…− HEALTH Background National Surgical Quality Improvement Program (NSQIP) data collection depends on labor-intensive m…+ HEALTH ObjectivePerceptual evaluation by speech-language pathologists (SLPs) is essential for initial evaluation of…− HEALTH Background Heart failure with preserved ejection fraction is a heterogeneous syndrome, and comorbid obstructi…
TruaceTracing the truth around AISaturday, September 19, 2026
Subscribe
TRV-2026-1140Certified recordPeer-reviewed

Artificial Intelligence-Based Hypernasality Diagnosis Using CAPS-A-AM Rated Speech Samples in Pediatric Velopharyngeal Dysfunction

ObjectivePerceptual evaluation by speech-language pathologists (SLPs) is essential for initial evaluation of velopharyngeal dysfunction (VPD). Machine learning (ML) offers a promising avenue for developing accessible speech assessment tools when SLP expertise is limited. We aimed to develop a workflow and preliminary algorithm for AI-based hypernasality detection based on the Cleft Audit Protocol for Speech-Augmented-Americleft Modification (CAPS-A-AM), a standardized framework for auditory perceptual speech ass…

Health · G Space — documented gain · certified 2026-09-19 · v1 · article view · machine-readable

Current reading — gain

Three ML approaches were evaluated: logistic regression (LR), Convolutional Neural Network (CNN) Attention-Multiple Instance Learning (MIL) (EfficientNet-V2-S), and a CNN-Extreme Gradient Boosting Hybrid (XGBoost).Patients/ParticipantsForty pediatric participants aged 2 to 17, including individuals with VPD, conditions associated with VPD, and healthy participants.Main Outcome Measure(s)Model performance was tested in binary hypernasality classification compared to SLP consensus at two CAPS-A-AM thresholds: absent (0) versus any hypernasality (1-4) and absent/borderline (0-1) versus mild-to-severe hypernasality (2-4).ResultsMultiple independent modeling approaches were able to detect clinically rated hypernasality.

Evidence

Reader signal

How should this claim be treated?

Cite this record

Truvace Impact Record TRV-2026-1140, v1: “Artificial Intelligence-Based Hypernasality Diagnosis Using CAPS-A-AM Rated Speech Samples in Pediatric Velopharyngeal Dysfunction.” Truvace, 2026-09-19. /record/TRV-2026-1140 (accessed at citation time). sha256 f05efbf3d0ba6f1d…

Calibration history

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

  1. Certifiedv1f05efbf3d0ba…

    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-2026-1140 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.