Modeling Performance of Open Surgical Cases.

Introduction: Previous efforts used digital video to develop computer-generated assessments of surgical hand motion economy and fluidity of motion. This study tests how well previously trained assessment models match expert ratings of suturing and tying video clips recorded in a new operating room (OR) setting.

Methods: Enabled through computer vision of the hands, this study tests the applicability of assessments born out of benchtop simulations to in vivo suturing and tying tasks recorded in the OR.

Quantifying surgeon maneuevers across experience levels through marker-less hand motion kinematics of simulated surgical tasks.

This paper compares clinician hand motion for common suturing tasks across a range of experience levels and tissue types. Medical students (32), residents (41), attending surgeons (10), and retirees (2) were recorded on digital video while suturing on one of: foam, pig feet, or porcine bowel. Depending on time in position, each medical student, resident, and attending participant was classified as junior or senior, yielding six experience categories.

Using Surgeon Hand Motions to Predict Surgical Maneuvers.

Objective: This study explores how common machine learning techniques can predict surgical maneuvers from a continuous video record of surgical benchtop simulations.

Background: Automatic computer vision recognition of surgical maneuvers (suturing, tying, and transition) could expedite video review and objective assessment of surgeries.

Method: We recorded hand movements of 37 clinicians performing simple and running subcuticular suturing benchtop simulations, and applied three machine learning techniques (decision trees, random forests, and hidden Markov models) to classify surgical maneuvers every 2 s (60 frames) of video.

Modeling Surgical Technical Skill Using Expert Assessment for Automated Computer Rating.

Objective: Computer vision was used to predict expert performance ratings from surgeon hand motions for tying and suturing tasks.

Summary Background Data: Existing methods, including the objective structured assessment of technical skills (OSATS), have proven reliable, but do not readily discriminate at the task level. Computer vision may be used for evaluating distinct task performance throughout an operation.

Visualizing stressful aspects of repetitive motion tasks and opportunities for ergonomic improvements using computer vision.

Patterns of physical stress exposure are often difficult to measure, and the metrics of variation and techniques for identifying them is underdeveloped in the practice of occupational ergonomics. Computer vision has previously been used for evaluating repetitive motion tasks for hand activity level (HAL) utilizing conventional 2D videos. The approach was made practical by relaxing the need for high precision, and by adopting a semi-automatic approach for measuring spatiotemporal characteristics of the repetitive task.

A marker-less technique for measuring kinematics in the operating room.

Background: Often in simulated settings, quantitative analysis of technical skill relies largely on specially tagged instruments or tracers on surgeons' hands. We investigated a novel, marker-less technique for evaluating technical skill during open operations and for differentiating tasks and surgeon experience level.

Methods: We recorded the operative field via in-light camera for open operations.

Evaluation of Simulated Clinical Breast Exam Motion Patterns Using Marker-Less Video Tracking.

Objective: This study investigates using marker-less video tracking to evaluate hands-on clinical skills during simulated clinical breast examinations (CBEs).

Background: There are currently no standardized and widely accepted CBE screening techniques.

Methods: Experienced physicians attending a national conference conducted simulated CBEs presenting different pathologies with distinct tumorous lesions.

The accuracy of conventional 2D video for quantifying upper limb kinematics in repetitive motion occupational tasks.

Marker-less 2D video tracking was studied as a practical means to measure upper limb kinematics for ergonomics evaluations. Hand activity level (HAL) can be estimated from speed and duty cycle. Accuracy was measured using a cross-correlation template-matching algorithm for tracking a region of interest on the upper extremities.

A frequency-duty cycle equation for the ACGIH hand activity level.

A new equation for predicting the hand activity level (HAL) used in the American Conference for Government Industrial Hygienists threshold limit value®(TLV®) was based on exertion frequency (F) and percentage duty cycle (D). The TLV® includes a table for estimating HAL from F and D originating from data in Latko et al. (Latko WA, Armstrong TJ, Foulke JA, Herrin GD, Rabourn RA, Ulin SS, Development and evaluation of an observational method for assessing repetition in hand tasks.

A hand speed-duty cycle equation for estimating the ACGIH hand activity level rating.

An equation was developed for estimating hand activity level (HAL) directly from tracked root mean square (RMS) hand speed (S) and duty cycle (D). Table lookup, equation or marker-less video tracking can estimate HAL from motion/exertion frequency (F) and D. Since automatically estimating F is sometimes complex, HAL may be more readily assessed using S.