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Video Commentary & Machine Learning: Tell Me What You See, I Tell You Who You Are.

Mohamed S Baloul¹, Vicky J-H Yeh¹, Fareeda Mukhtar²

¹Department of Surgery, Mayo Clinic, Rochester, Minnesota.

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Machine learning (ML) can predict surgical residents' training levels using video commentary assessments. This approach offers a 40% improvement over traditional methods, enhancing medical education evaluation.

Keywords:

Video commentary artificial intelligence assessment machine learning medical education surgical curriculum

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Area of Science:

Medical Education Technology
Machine Learning Applications
Surgical Training Assessment

Background:

Complex problem-solving assessment in medical education is challenging.
Machine Learning (ML) integration offers potential for revolutionizing teaching and assessment.
This study applies ML to Video Commentary (VC) assessments to predict resident training levels.

Purpose of the Study:

To demonstrate ML applications in medical education.
To utilize ML for predicting surgical resident training levels based on VC assessments.
To compare ML predictive accuracy against traditional statistical analysis.

Main Methods:

Secondary analysis of a multi-institutional study involving 81 surgical residents (PGY 1-5).
Utilized a structured Video Commentary (VC) assessment with 13 operative video clips, scored in real-time by proctors.
Developed a supervised ML model using TensorFlow and Keras, with individual VC clip scores as inputs.

Main Results:

Individual VC clip scores strongly correlated with Postgraduate Year (PGY) level (p=0.001).
Certain video clips significantly influenced total scores and PGY level prediction.
The ML model improved PGY level prediction by 40% compared to traditional statistical analysis, achieving a lower Mean Absolute Error (MAE).

Conclusions:

Higher performance in select video clips is key to total score but doesn't always indicate higher PGY level.
Relying solely on total scores may miss deeper insights into trainee performance.
The developed ML model shows promise for accurately gauging resident levels in extensive assessments like VC, warranting further investigation with larger datasets.