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Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
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A Novel Multimodal Deep Image Analysis Model for Predicting Extraction/Non-Extraction Decision.

Sunna Imtiaz Ahmad1, Jakub Olczyk1, Adriel S Araújo2

  • 1School of Dentistry, Indiana University, Indianapolis, Indiana, USA.

Orthodontics & Craniofacial Research
|November 6, 2025
PubMed
Summary
This summary is machine-generated.

Deep learning models accurately predict orthodontic extraction decisions using lateral cephalometric radiographs and intraoral scans. Combining these data sources, especially with cephalometric landmarks, yields superior diagnostic performance for clinicians.

Keywords:
artificial intelligenceclinical decision‐makingdeep learningorthodonticstooth extraction

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Area of Science:

  • Artificial Intelligence in Dentistry
  • Orthodontic Treatment Planning
  • Medical Image Analysis

Background:

  • Orthodontic treatment decisions, such as extraction vs. non-extraction, are critical.
  • Current decision-making relies on clinical expertise and radiographic analysis.
  • There is a need for advanced decision-support tools to aid orthodontists.

Purpose of the Study:

  • To develop a deep learning classifier for predicting orthodontic extraction decisions.
  • To utilize lateral cephalometric radiographs (LCRs) and intraoral scans (IOS) as input data.
  • To create a decision-support tool for orthodontists.

Main Methods:

  • A dataset of 617 patients' LCRs and IOS was used.
  • Features were extracted from IOS (arch measurements, tooth spatial features) and LCRs (CephNet landmarks, autoencoder, PCA).
  • Deep learning models were trained and evaluated using metrics like accuracy, sensitivity, specificity, and F1 score.

Main Results:

  • The combined IOS + Landmark (Land) model achieved the highest accuracy (77%) and F1 score (0.62).
  • This model also demonstrated strong specificity (83%) and positive predictive value (62%).
  • Multimodal models significantly outperformed single-modality models, particularly the autoencoder (AE) model.

Conclusions:

  • Deep learning models effectively predict the extraction/non-extraction decision from LCRs and IOS.
  • Multimodal approaches integrating IOS with cephalometric landmarks offer superior diagnostic performance.
  • These models can serve as valuable decision-support tools in orthodontics.