Surgical Skills Classification Using Deep Learning Models: A Comprehensive Survey

In the medical domain, evaluating surgical skills is paramount for ensuring the safety and quality of services, yet current methods suffer from biases, inefficiencies, and subjective assessments. This study introduces an automated, objective approach utilizing advanced technologies, including deep learning, to assess and categorize surgeons' proficiency levels. Exploring various deep neural network models, the research aims to enhance efficiency, overcome subjectivity-related challenges, and improve the precision of skill ratings. By automating the assessment process, the initiative provides unbiased feedback to surgeons, reducing the likelihood of errors and ultimately elevating patient safety and treatment quality. The study's outcomes have the potential to revolutionize surgical training and assessment, aligning with the evolving healthcare environment to promote continuous improvement in patient outcomes. This paradigm shift underscores the significance of incorporating automation for unbiased evaluations, thereby positively impacting the healthcare sector.