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A deep learning based automatic two-dimensional digital templating model for total knee arthroplasty.

Jaeseok Park1, Sung Eun Kim2,3, Back Kim1

  • 1College of Medicine, Seoul National University, Seoul, South Korea.

Knee Surgery & Related Research
|November 28, 2024
PubMed
Summary
This summary is machine-generated.

This study developed an artificial intelligence (AI) model for automated implant size prediction in total knee arthroplasty (TKA). The AI tool demonstrated efficiency and satisfactory accuracy, potentially reducing clinical workload.

Keywords:
Artificial intelligenceImplant size predictionPreoperative templatingTotal knee arthroplasty

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Area of Science:

  • Orthopedic Surgery
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Preoperative templating is crucial for total knee arthroplasty (TKA) resource planning.
  • Current templating methods lack automation, increasing workload.
  • This study aimed to develop an AI model for automated implant size prediction.

Purpose of the Study:

  • To develop and validate an artificial intelligence (AI) model for automated implant size prediction in total knee arthroplasty (TKA).
  • To compare the accuracy and efficiency of the AI model against manual templating by an orthopedic specialist.

Main Methods:

  • Utilized 13,281 knee radiographs for AI model training and 2,302 for validation/testing.
  • Developed an AI pipeline integrating anteroposterior (AP) and lateral radiograph predictions, selecting the smaller size to prevent overhang.
  • Validated AI predictions against 81 TKA cases, comparing accuracy and measurement time with an orthopedic specialist.

Main Results:

  • The AI model achieved "exact" predictions for 39.5% of femoral and 43.2% of tibial components.
  • With a one-size margin, 88.9% of AI predictions were "accurate" for both components.
  • AI templating was significantly faster (48.7s) than manual templating (97.5s), with an average implant position error of <4mm.

Conclusions:

  • An AI-based templating tool for TKA was successfully developed.
  • The tool demonstrated satisfactory accuracy and efficiency in implant size prediction.
  • AI application can significantly reduce the clinical workload associated with TKA preparation.