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Short Training Significantly Improves Ganglion Cell Detection Using an Algorithm-Assisted Approach.

Ariel Greenberg1, Benzion Samueli2, Ibrahim Fahoum1

  • 1From Institute of Pathology, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel (A Greenberg, Fahoum, O Greenberg, Zemser-Werner, Hagege, Hershkovitz).

Archives of Pathology & Laboratory Medicine
|June 23, 2022
PubMed
Summary
This summary is machine-generated.

A brief training significantly improved algorithm-assisted Hirschsprung disease (HSCR) diagnosis accuracy for pathologists. This approach enhances diagnostic performance for rare diseases, reducing the need for expert consultations.

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Area of Science:

  • Pathology
  • Medical Education
  • Diagnostic Support Systems

Background:

  • Medical education in pathology requires extensive sample experience.
  • Acquiring teaching materials for rare diseases like Hirschsprung disease (HSCR) is challenging.
  • A decision support system with an algorithm aids HSCR diagnosis.

Purpose of the Study:

  • To evaluate the impact of a brief training session on algorithm-assisted HSCR diagnosis.
  • To determine if training improves diagnostic accuracy and efficiency.

Main Methods:

  • Five pathologists reviewed 568 image sets (1704 images) from 50 HSCR cases.
  • Pathologists scored images for ganglion cells three times.
  • A short (<10 minutes) educational presentation was given between the second and third scoring rounds.

Main Results:

  • Correct diagnosis rates (true positives/negatives) significantly increased post-training.
  • The need for expert consultations decreased after the training.
  • No significant changes in false positive/negative rates were observed.

Conclusions:

  • A very short training session substantially enhances pathologist performance in algorithm-assisted HSCR diagnosis.
  • This training strategy shows promise for improving the diagnosis of other rare diseases.
  • Algorithm-assisted diagnosis combined with targeted training improves diagnostic outcomes.