# Virtual Rotator Cuff Arthroscopic Skill Trainer

> **NIH NIH R44** · KITWARE, INC. · 2020 · $749,998

## Abstract

ABSTRACT
 The aim of this proposal is to design, develop, and validate Virtual Rotator Cuff Arthroscopic Skill Trainer
(ViRCAST) to virtually simulate arthroscopic rotator cuff repair surgery and perform extensive validation studies
to demonstrate its effectiveness as a training platform. The platform will provide safe training environment to
reduce training time for acquisition of necessary skills and provide objective skill learning process with
quantitative measures. The ViRCAST will incorporate structured and customized learning (e.g. trainee specific
learning), which is expected to improve trainee's psychomotor skills while providing cognitive feedback. It will
also assist training with increasing difficulty scenarios. We hypothesize that the proposed platform would
significantly improve training with deliberate and individual specific learning.
 ViRCAST will be unique and innovative by incorporating; (1) High-fidelity visualization of the bone, tissue,
and instruments, (2) realistic physics-based soft tissue and bone interactions with instruments and fluid and
electrocautery simulation, (3) physics-based tissue and bone haptic (touch) sensation during the bone shaving
and cleaning tasks, (4) use of actual instruments with hardware interfaces (5) simulation of knot tying tasks (6)
cognitive skill training on various difficulty settings (7) rigorous validation studies (e.g. content, concurrent,
discriminant, deliberate learning assessment) with human subjects, (8) Objective metrics derived from the
clinical practice and (9) personalization in training to detect and eliminate the deficiencies and bad habits.
ViRCAST will incorporate personalized training (e.g. deliberate learning) that will pinpoint trainees' skill
deficiencies and aim to improve their skill levels.
 The ViRCAST will be providing standardized, quantified feedback for evaluation and self-assessment by
measuring (a) the accuracy and smoothness of surgeons' hand movements (hand-eye coordination), (b)
accuracy of the applied procedure (e.g. anchor angle, distribution of knots, drilling depth), (c) accidental
damage or errors (e.g. injuring anatomical structures), (d) performance in conducting a specific task within the
surgery (e.g. hemostasis duration), and (e) cognitive decision making process for the complex scenarios. With
such a novel training technology, it will also be possible to track trainees' performance trends over time and
personalize the training sequence by creating dynamic scenes to improve trainees' deficiency areas.

## Key facts

- **NIH application ID:** 10001678
- **Project number:** 4R44AR075481-02
- **Recipient organization:** KITWARE, INC.
- **Principal Investigator:** Venkata Sreekanth Arikatla
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $749,998
- **Award type:** 4N
- **Project period:** 2019-09-01 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10001678

## Citation

> US National Institutes of Health, RePORTER application 10001678, Virtual Rotator Cuff Arthroscopic Skill Trainer (4R44AR075481-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10001678. Licensed CC0.

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