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Learning to use a surgical microscope is a fundamental step in otolaryngology training; however, there is currently no objective method to teach or assess this skill. Tympanostomy tube placement is a common otologic procedure that requires skilled use of a surgical microscope. This study was designed to (1) implement metrics capable of evaluating microscope use and (2) establish construct validity. This was a prospective cohort study. Eight otolaryngology trainees and three otolaryngology experts were asked to use a microscope to insert a tympanostomy tube into a cadaveric myringotomy in a standardized setting. Microscope movements were tracked in a three-dimensional space, and tracking metrics were applied to the data. The procedure was video-recorded and then analyzed by blinded experts using operational metrics. Results from both groups were compared, and discriminatory metrics were determined. The following tracking metrics were identified as discriminatory between the trainee and expert groups: total completion time, operation time, still time, and jitter (movement perturbation). Many operational metrics were found to be discriminatory between the two groups, including several positioning metrics, optical metrics, and procedural metrics. Performance metrics were implemented, and construct validity was established for a subset of the proposed metrics by discriminating between expert and novice participants. These discriminatory metrics could form the basis of an automated system for providing feedback to residents during training while using a myringotomy surgical simulator. Additionally, these metrics may be useful in guiding a standardized teaching and evaluation methodology for training in the use of surgical microscopes.

Conventional surgical methods are effective for treating lung tumors; however, they impose high trauma and pain to patients. Minimally invasive surgery is a safer alternative as smaller incisions are required to reach the lung; however, it is challenging due to inadequate intraoperative tumor localization. To address this issue, a mechatronic palpation device was developed that incorporates tactile and ultrasound sensors capable of acquiring surface and cross-sectional images of palpated tissue. Initial work focused on tactile image segmentation and fusion of position-tracked tactile images, resulting in a reconstruction of the palpated surface to compute the spatial locations of underlying tumors. This paper presents a computational model capable of analyzing orthogonally-paired tactile and ultrasound images to compute the surface circumference and depth margins of a tumor. The framework also integrates an error compensation technique and an algebraic model to align all of the image pairs and to estimate the tumor depths within the tracked thickness of a palpated tissue. For validation, an ex vivo experimental study was conducted involving the complete palpation of 11 porcine liver tissues injected with iodine-agar tumors of varying sizes and shapes. The resulting tactile and ultrasound images were then processed using the proposed model to compute the tumor margins and compare them to fluoroscopy based physical measurements. The results show a good negative correlation ( r = −0.783, p = 0.004) between the tumor surface margins and a good positive correlation ( r = 0.743, p = 0.009) between the tumor depth margins.