Robotics in Pediatric Otolaryngology-Head and Neck Surgery and Advanced Surgical Planning

Artificial Intelligence and Pediatric Otolaryngology

Artificial intelligence (AI) studies show how to program computers to simulate human intelligence and perform data interpretation, learning, and adaptive decision-making. Within pediatric otolaryngology, there is a growing body of evidence for the role of AI in diagnosis and triaging of acute otitis media and middle ear effusion, pediatric sleep disorders, and syndromic craniofacial anomalies. The use of automated machine learning with robotic devices intraoperatively is an evolving field of study, particularly in the realms of pediatric otologic surgery and computer-aided planning for maxillofacial reconstruction, and we will likely continue seeing novel applications of machine learning in otolaryngologic surgery.

Automated objective surgical planning for lateral skull base tumors

PURPOSE

Surgical removal of pathology at the lateral skull base is challenging because of the proximity of critical anatomical structures which can lead to significant morbidity when damaged or traversed. Pre-operative computed surgical approach planning has the potential to aid in selection of the optimal approach to remove pathology and minimize complications.

METHODS

We propose an automated surgical approach planning algorithm to derive the optimal approach to vestibular schwannomas in the internal auditory canal for hearing preservation surgery. The algorithm selects between the middle cranial fossa and retrosigmoid approach by utilizing a unique segmentation of each patient's anatomy and a cost function to minimize potential surgical morbidity.

RESULTS

Patients who underwent hearing preservation surgery for vestibular schwannoma resection (n = 9) were included in the cohort. Middle cranial fossa surgery was performed in 5 patients, and retrosigmoid surgery was performed in 4. The algorithm favored the performed surgical approach in 6 of 9 patients.

CONCLUSION

We developed a method for computing morbidity costs of surgical paths to objectively analyze surgical approaches at the lateral skull base. Computed pre-operative planning may assist in surgical decision making, trainee education, and improving clinical outcomes.