Evaluation of colour parameters at different preparation depths and prediction of tooth colour using machine learning

This study aimed to analyze colour changes occurring at different preparation depths and predict tooth colour using machine learning algorithms.

Continue reading at PubMed

✦ The floor

Discussion

Signed responses from readers of the wire.

✦ Clinical Takeaway ✦

No actionable change — this study is entirely within dentistry (tooth colour and preparation depth) and has no relevance to audiology clinical practice.

✦ Why It Matters ✦

This article does not appear to be relevant to audiology; it concerns dental tooth-colour prediction and has no discernible connection to hearing science, hearing loss, or audiological practice.

✦ Key Points ✦

01Study published in BDJ Open examines tooth colour changes at varying preparation depths.
02Machine learning algorithms were applied to predict tooth colour outcomes.
03Sample consisted of 106 anterior (front) teeth.
04No audiology, hearing, or ear-related content is present in this article.

✦ Research metadata ✦

PMID: 42999002
DOI: 10.1038/s41405-026-00508-y.
Journal: BDJ Open
Publication type: research_article
Evidence level: 4
Sample size: 106
Population: 106 anterior (front) teeth — dental specimens
Intervention: Tooth preparation at varying depths with machine learning colour prediction

Primary outcomes

Colour parameter changes at different preparation depths; Accuracy of machine learning algorithms in predicting tooth colour

✦ Related stories ✦

PubMed·Journal article·AI & technology·4w ago

Data-Driven Prediction of Speech-in-Noise Performance: A Systematic Review and Meta-analysis of Machine Learning Models

Machine learning models show promise for predicting speech-in-noise performance, but until clinical validation benchmarks and standardised input features are established, audiologists should not replace behavioural speech-in-noise testing with AI-predicted scores.

PubMed·Journal article·AI & technology·6mo ago

A systematic review of machine learning techniques for real-time speech and noise classification in hearing aids

No immediate practice change warranted, but audiologists should monitor this fast-moving area as ML-based classifiers are approaching real-time clinical deployment and may soon influence device selection counseling.

PubMed·Journal article·AI & technology·7d ago

Statistical and Image-Based Deep Learning Modeling to Predict Hearing Preservation in Vestibular Schwannoma Surgery

Preliminary evidence that deep learning models using preoperative hearing scores and tumor size can predict hearing preservation in vestibular schwannoma surgery; not yet validated for clinical use but warrants monitoring as an emerging decision-support tool.

PubMed·Journal article·AI & technology·3mo ago

An Effective Machine Learning for Prioritizing Hearing Loss Screening in Older Adults: A Primary Care Approach

The machine learning screening prioritisation model is promising for primary care integration but requires external prospective validation before audiologists or GPs should adopt it to triage patients.

PubMed·Journal article·AI & technology·12d ago

Evaluating the Quality of Artificial Intelligence Chatbot Responses in Audiology Case Interpretation and Management: A Blinded Comparative Study

AI chatbots show some promise for audiology case support but should not replace clinical judgment until accuracy and reliability are better established — no practice change warranted from this single study alone.

PubMed·Journal article·AI & technology·2w ago