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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
Summary
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.
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.
