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Stereotype Content Model02:16

Stereotype Content Model

The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence categorization, a person will feel...

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Artificial Intelligence Segmentation Errors in Implant Planning Software Programs: An Overview.

Ghida Lawand1, Luiz Gonzaga1, Julien Issa2

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This summary is machine-generated.

AI segmentation in static computer-assisted implant surgery (s-CAIS) shows promise but faces errors from imaging issues and algorithms. Manual oversight remains crucial for accurate digital implant planning.

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Area of Science:

  • Digital dentistry
  • Artificial intelligence in medicine
  • Surgical planning

Background:

  • Static computer-assisted implant surgery (s-CAIS) relies on precise 3D anatomical models derived from CBCT and intraoral scans.
  • AI-driven segmentation offers potential to automate model creation and reduce manual segmentation workload.
  • Current AI segmentation faces technical and algorithmic limitations impacting accuracy.

Purpose of the Study:

  • Evaluate AI segmentation accuracy and limitations in dental implant planning software.
  • Identify common segmentation error sources and their clinical implications.
  • Explore strategies for mitigating segmentation errors in s-CAIS workflows.

Main Methods:

  • Narrative literature review and educational practice overview.
  • Qualitative analysis of segmentation error patterns (boundary, over-/under-segmentation, misidentification, partial volume effects).
  • Demonstration of editing functionalities across four implant planning systems (coDiagnostiX, BlueSkyPlan, Atomica, Relu).

Main Results:

  • AI segmentation errors stem from imaging artifacts, motion blur, anatomical variability, and algorithmic biases.
  • Errors can lead to inaccurate implant positioning, compromised surgical guides, and clinical complications.
  • Manual intervention is essential despite AI advancements; platform variability and limited editing tools pose challenges.

Conclusions:

  • Segmentation errors remain a significant barrier in s-CAIS, impacting digital implant planning safety and efficacy.
  • Improvements require enhanced imaging protocols, refined algorithms, and robust clinician oversight.
  • Regulatory transparency and standardized validation are crucial for advancing AI in implant surgery.