Shape completion in the dark: completing vertebrae morphology from 3D ultrasound.

PURPOSE: Ultrasound (US) imaging, while advantageous for its radiation-free nature, is challenging to interpret due to only partially visible organs and a lack of complete 3D information. While performing US-based diagnosis or investigation, medical professionals therefore create a mental map of the 3D anatomy. In this work, we aim to replicate this process and enhance the visual representation of anatomical structures. METHODS: We introduce a point cloud-based probabilistic deep learning (DL) method to complete occluded anatomical structures through 3D shape completion and choose US-based spine examinations as our application. To enable training, we generate synthetic 3D representations of partially occluded spinal views by mimicking US physics and accounting for inherent artifacts. RESULTS: The proposed model performs consistently on synthetic and patient data, with mean and median differences of 2.02 and 0.03 in Chamfer Distance (CD), respectively. Our ablation study demonstrates the importance of US physics-based data generation, reflected in the large mean and median difference of 11.8 CD and 9.55 CD, respectively. Additionally, we demonstrate that anatomical landmarks, such as the spinous process (with reconstruction CD of 4.73) and the facet joints (mean distance to ground truth (GT) of 4.96 mm), are preserved in the 3D completion. CONCLUSION: Our work establishes the feasibility of 3D shape completion for lumbar vertebrae, ensuring the preservation of level-wise characteristics and successful generalization from synthetic to real data. The incorporation of US physics contributes to more accurate patient data completions. Notably, our method preserves essential anatomical landmarks and reconstructs crucial injections sites at their correct locations.

From quantitative metrics to clinical success: assessing the utility of deep learning for tumor segmentation in breast surgery.

PURPOSE: Preventing positive margins is essential for ensuring favorable patient outcomes following breast-conserving surgery (BCS). Deep learning has the potential to enable this by automatically contouring the tumor and guiding resection in real time. However, evaluation of such models with respect to pathology outcomes is necessary for their successful translation into clinical practice. METHODS: Sixteen deep learning models based on established architectures in the literature are trained on 7318 ultrasound images from 33 patients. Models are ranked by an expert based on their contours generated from images in our test set. Generated contours from each model are also analyzed using recorded cautery trajectories of five navigated BCS cases to predict margin status. Predicted margins are compared with pathology reports. RESULTS: The best-performing model using both quantitative evaluation and our visual ranking framework achieved a mean Dice score of 0.959. Quantitative metrics are positively associated with expert visual rankings. However, the predictive value of generated contours was limited with a sensitivity of 0.750 and a specificity of 0.433 when tested against pathology reports. CONCLUSION: We present a clinical evaluation of deep learning models trained for intraoperative tumor segmentation in breast-conserving surgery. We demonstrate that automatic contouring is limited in predicting pathology margins despite achieving high performance on quantitative metrics.

Real-time integration between Microsoft HoloLens 2 and 3D Slicer with demonstration in pedicle screw placement planning.

PURPOSE: Up to date, there has been a lack of software infrastructure to connect 3D Slicer to any augmented reality (AR) device. This work describes a novel connection approach using Microsoft HoloLens 2 and OpenIGTLink, with a demonstration in pedicle screw placement planning. METHODS: We developed an AR application in Unity that is wirelessly rendered onto Microsoft HoloLens 2 using Holographic Remoting. Simultaneously, Unity connects to 3D Slicer using the OpenIGTLink communication protocol. Geometrical transform and image messages are transferred between both platforms in real time. Through the AR glasses, a user visualizes a patient's computed tomography overlaid onto virtual 3D models showing anatomical structures. We technically evaluated the system by measuring message transference latency between the platforms. Its functionality was assessed in pedicle screw placement planning. Six volunteers planned pedicle screws' position and orientation with the AR system and on a 2D desktop planner. We compared the placement accuracy of each screw with both methods. Finally, we administered a questionnaire to all participants to assess their experience with the AR system. RESULTS: The latency in message exchange is sufficiently low to enable real-time communication between the platforms. The AR method was non-inferior to the 2D desktop planner, with a mean error of 2.1 ± 1.4 mm. Moreover, 98% of the screw placements performed with the AR system were successful, according to the Gertzbein-Robbins scale. The average questionnaire outcomes were 4.5/5. CONCLUSIONS: Real-time communication between Microsoft HoloLens 2 and 3D Slicer is feasible and supports accurate planning for pedicle screw placement.

Use of Machine Learning to Assess Cataract Surgery Skill Level With Tool Detection.

Purpose: To develop a method for objective analysis of the reproducible steps in routine cataract surgery. Design: Prospective study; machine learning. Participants: Deidentified faculty and trainee surgical videos. Methods: Consecutive cataract surgeries performed by a faculty or trainee surgeon in an ophthalmology residency program over 6 months were collected and labeled according to degrees of difficulty. An existing image classification network, ResNet 152, was fine-tuned for tool detection in cataract surgery to allow for automatic identification of each unique surgical instrument. Individual microscope video frame windows were subsequently encoded as a vector. The relation between vector encodings and perceived skill using k-fold user-out cross-validation was examined. Algorithms were evaluated using area under the receiver operating characteristic curve (AUC) and the classification accuracy. Main Outcome Measures: Accuracy of tool detection and skill assessment. Results: In total, 391 consecutive cataract procedures with 209 routine cases were used. Our model achieved an AUC ranging from 0.933 to 0.998 for tool detection. For skill classification, AUC was 0.550 (95% confidence interval [CI], 0.547-0.553) with an accuracy of 54.3% (95% CI, 53.9%-54.7%) for a single snippet, AUC was 0.570 (0.565-0.575) with an accuracy of 57.8% (56.8%-58.7%) for a single surgery, and AUC was 0.692 (0.659-0.758) with an accuracy of 63.3% (56.8%-69.8%) for a single user given all their trials. Conclusions: Our research shows that machine learning can accurately and independently identify distinct cataract surgery tools in videos, which is crucial for comparing the use of the tool in a step. However, it is more challenging for machine learning to accurately differentiate overall and specific step skill to assess the level of training or expertise. Financial Disclosures: The author(s) have no proprietary or commercial interest in any materials discussed in this article.

The use of 3D digital anatomy model improves the communication with patients presenting with prostate disease: The first experience in Senegal.

OBJECTIVES: We hypothesized that the use of an interactive 3D digital anatomy model can improve the quality of communication with patients about prostate disease. METHODS: A 3D digital anatomy model of the prostate was created from an MRI scan, according to McNeal's zonal anatomy classification. During urological consultation, the physician presented the digital model on a computer and used it to explain the disease and available management options. The experience of patients and physicians was recorded in questionnaires. RESULTS: The main findings were as follows: 308 patients and 47 physicians participated in the study. In the patient group, 96.8% reported an improved level of understanding of prostate disease and 90.6% reported an improved ability to ask questions during consultation. Among the physicians, 91.5% reported improved communication skills and 100% reported an improved ability to obtain patient consent for subsequent treatment. At the same time, 76.6% of physicians noted that using the computer model lengthened the consultation. CONCLUSION: This exploratory study found that the use of a 3D digital anatomy model in urology consultations was received overwhelmingly favorably by both patients and physicians, and it was perceived to improve the quality of communication between patient and physician. A randomized study is needed to confirm the preliminary findings and further quantify the improvements in the quality of patient-physician communication.

Sensor-Based Automated Detection of Electrosurgical Cautery States.

In computer-assisted surgery, it is typically required to detect when the tool comes into contact with the patient. In activated electrosurgery, this is known as the energy event. By continuously tracking the electrosurgical tools' location using a navigation system, energy events can help determine locations of sensor-classified tissues. Our objective was to detect the energy event and determine the settings of electrosurgical cautery-robustly and automatically based on sensor data. This study aims to demonstrate the feasibility of using the cautery state to detect surgical incisions, without disrupting the surgical workflow. We detected current changes in the wires of the cautery device and grounding pad using non-invasive current sensors and an oscilloscope. An open-source software was implemented to apply machine learning on sensor data to detect energy events and cautery settings. Our methods classified each cautery state at an average accuracy of 95.56% across different tissue types and energy level parameters altered by surgeons during an operation. Our results demonstrate the feasibility of automatically identifying energy events during surgical incisions, which could be an important safety feature in robotic and computer-integrated surgery. This study provides a key step towards locating tissue classifications during breast cancer operations and reducing the rate of positive margins.

Bridging 3D Slicer and ROS2 for Image-Guided Robotic Interventions.

Developing image-guided robotic systems requires access to flexible, open-source software. For image guidance, the open-source medical imaging platform 3D Slicer is one of the most adopted tools that can be used for research and prototyping. Similarly, for robotics, the open-source middleware suite robot operating system (ROS) is the standard development framework. In the past, there have been several "ad hoc" attempts made to bridge both tools; however, they are all reliant on middleware and custom interfaces. Additionally, none of these attempts have been successful in bridging access to the full suite of tools provided by ROS or 3D Slicer. Therefore, in this paper, we present the SlicerROS2 module, which was designed for the direct use of ROS2 packages and libraries within 3D Slicer. The module was developed to enable real-time visualization of robots, accommodate different robot configurations, and facilitate data transfer in both directions (between ROS and Slicer). We demonstrate the system on multiple robots with different configurations, evaluate the system performance and discuss an image-guided robotic intervention that can be prototyped with this module. This module can serve as a starting point for clinical system development that reduces the need for custom interfaces and time-intensive platform setup.

Real-time automatic tumor segmentation for ultrasound-guided breast-conserving surgery navigation.

PURPOSE: Ultrasound-based navigation is a promising method in breast-conserving surgery, but tumor contouring often requires a radiologist at the time of surgery. Our goal is to develop a real-time automatic neural network-based tumor contouring process for intraoperative guidance. Segmentation accuracy is evaluated by both pixel-based metrics and expert visual rating. METHODS: This retrospective study includes 7318 intraoperative ultrasound images acquired from 33 breast cancer patients, randomly split between 80:20 for training and testing. We implement a u-net architecture to label each pixel on ultrasound images as either tumor or healthy breast tissue. Quantitative metrics are calculated to evaluate the model's accuracy. Contour quality and usability are also assessed by fellowship-trained breast radiologists and surgical oncologists. Additionally, the viability of using our u-net model in an existing surgical navigation system is evaluated by measuring the segmentation frame rate. RESULTS: The mean dice similarity coefficient of our u-net model is 0.78, with an area under the receiver-operating characteristics curve of 0.94, sensitivity of 0.95, and specificity of 0.67. Expert visual ratings are positive, with 93% of responses rating tumor contour quality at or above 7/10, and 75% of responses rating contour quality at or above 8/10. Real-time tumor segmentation achieved a frame rate of 16 frames-per-second, sufficient for clinical use. CONCLUSION: Neural networks trained with intraoperative ultrasound images provide consistent tumor segmentations that are well received by clinicians. These findings suggest that neural networks are a promising adjunct to alleviate radiologist workload as well as improving efficiency in breast-conserving surgery navigation systems.

System for Central Venous Catheterization Training Using Computer Vision-Based Workflow Feedback.

OBJECTIVE: To develop a system for training central venous catheterization that does not require an expert observer. We propose a training system that uses video-based workflow recognition and electromagnetic tracking to provide trainees with real-time instruction and feedback. METHODS: The system provides trainees with prompts about upcoming tasks and visual cues about workflow errors. Most tasks are recognized from a webcam video using a combination of a convolutional neural network and a recurrent neural network. We evaluated the system's ability to recognize tasks in the workflow by computing the percent of tasks that were recognized and the average signed transitional delay between the system and reviewers. We also evaluated the usability of the system using a participant questionnaire. RESULTS: The system was able to recognize 86.2% of tasks in the workflow. The average signed transitional delay was -0.7s. The average usability score on the questionnaire was 4.7 out of 5 for the system overall. The participants found the interactive task list to be the most useful component of the system with an average score of 4.8 out of 5. CONCLUSION: Overall, the participants' response to the system was positive. Participants perceived that the system would be useful for central venous catheterization training. Our system provides trainees with meaningful instruction and feedback without needing an expert observer to be present. SIGNIFICANCE: We are able to provide trainees with more opportunities to access instruction and meaningful feedback by using workflow recognition.

Training in soft tissue resection using real-time visual computer navigation feedback from the Surgery Tutor: A randomized controlled trial.

BACKGROUND: In competency-based medical education, surgery trainees are often required to learn procedural skills in a simulated setting before proceeding to the clinical environment. The Surgery Tutor computer navigation platform allows for real-time proctor-less assessment of open soft tissue resection skills; however, the use of this platform as an aid in acquisition of procedural skills is yet to be explored. METHODS: In this prospective randomized controlled trial, 20 final year medical students were randomized to receive either training with real-time computer navigation feedback (Intervention, n = 10) or simulation training without navigation feedback (Control, n = 10) during resection of simulated non-palpable soft tissue tumors. Real-time computer navigation feedback allowed participants to visualize the position of their scalpel relative to the tumor. Computer navigation feedback was removed for postintervention assessment. Primary outcome was positive margin rate. Secondary outcomes were procedure time, mass of tissue excised, number of scalpel motions, and distance traveled by the scalpel. RESULTS: Training with real-time computer navigation resulted in a significantly lower positive margin rate as compared to training without navigation feedback (0% vs 40%, P = .025). All other performance metrics were not significantly different between the 2 groups. Participants in the intervention group displayed significant improvement in positive margin rate from baseline to final assessment (80% vs 0%, P < .01), whereas participants in the Control group did not. CONCLUSION: Real-time visual computer navigation feedback from the Surgery Tutor resulted in superior acquisition of procedural skills as compared to training without navigation feedback.

Design of an Ultrasound-Navigated Prostate Cancer Biopsy System for Nationwide Implementation in Senegal.

This paper presents the design of NaviPBx, an ultrasound-navigated prostate cancer biopsy system. NaviPBx is designed to support an affordable and sustainable national healthcare program in Senegal. It uses spatiotemporal navigation and multiparametric transrectal ultrasound to guide biopsies. NaviPBx integrates concepts and methods that have been independently validated previously in clinical feasibility studies and deploys them together in a practical prostate cancer biopsy system. NaviPBx is based entirely on free open-source software and will be shared as a free open-source program with no restriction on its use. NaviPBx is set to be deployed and sustained nationwide through the Senegalese Military Health Service. This paper reports on the results of the design process of NaviPBx. Our approach concentrates on "frugal technology", intended to be affordable for low-middle income (LMIC) countries. Our project promises the wide-scale application of prostate biopsy and will foster time-efficient development and programmatic implementation of ultrasound-guided diagnostic and therapeutic interventions in Senegal and beyond.

Three-dimensional ultrasound for knee osteophyte depiction: a comparative study to computed tomography.

PURPOSE: Osteophytes are common radiographic markers of osteoarthritis. However, they are not accurately depicted using conventional imaging, thus hampering surgical interventions that rely on pre-operative images. Studies have shown that ultrasound (US) is promising at detecting osteophytes and monitoring the progression of osteoarthritis. Furthermore, three-dimensional (3D) ultrasound reconstructions may offer a means to quantify osteophytes. The purpose of this study was to compare the accuracy of osteophyte depiction in the knee joint between 3D US and conventional computed tomography (CT). METHODS: Eleven human cadaveric knees were pre-screened for the presence of osteophytes. Three osteoarthritic knees were selected, and then, 3D US and CT images were obtained, segmented, and digitally reconstructed in 3D. After dissection, high-resolution structured light scanner (SLS) images of the joint surfaces were obtained. Surface matching and root mean square (RMS) error analyses of surface distances were performed to assess the accuracy of each modality in capturing osteophytes. The RMS errors were compared between 3D US, CT and SLS models. RESULTS: Average RMS error comparisons for 3D US versus SLS and CT versus SLS models were 0.87 mm ± 0.33 mm (average ± standard deviation) and 0.95 mm ± 0.32 mm, respectively. No statistical difference was found between 3D US and CT. Comparative observations of imaging modalities suggested that 3D US better depicted osteophytes with cartilage and fibrocartilage tissue characteristics compared to CT. CONCLUSION: Using 3D US can improve the depiction of osteophytes with a cartilaginous portion compared to CT. It can also provide useful information about the presence and extent of osteophytes. Whilst algorithm improvements for automatic segmentation and registration of US are needed to provide a more robust investigation of osteophyte depiction accuracy, this investigation puts forward the potential application for 3D US in routine diagnostic evaluations and pre-operative planning of osteoarthritis.

Electromagnetic (EM) catheter path tracking in ultrasound-guided brachytherapy of the breast.

PURPOSE: To evaluate a novel navigation system for breast brachytherapy, based on ultrasound (US)-guided catheter needle implantations followed by electromagnetic (EM) tracking of catheter paths. METHODS: Breast phantoms were produced, containing US-visible tumors. Ultrasound was used to localize the tumor pose and volume within the phantom, followed by planning an optimal catheter pattern through the tumor using navigation software. An electromagnetic (EM)-tracked catheter needle was used to insert the catheters in the desired pattern. The inserted catheters were visualized on a post-implant CT, serving as ground truth. Electromagnetic (EM) tracking and reconstruction of the inserted catheter paths were performed by pulling a flexible EM guidewire through each catheter, performed in two clinical brachytherapy suites. The accuracy of EM catheter tracking was evaluated by calculating the Hausdorff distance between the EM-tracked and CT-based catheter paths. The accuracy and clinical feasibility of EM catheter tracking were also evaluated in three breast cancer patients, performed in a separate experiment room. RESULTS: A total of 71 catheter needles were implanted into 12 phantoms using US guidance and EM navigation, in an average ± SD time of 8.1 ± 2.9 min. The accuracy of EM catheter tracking was dependent on the brachytherapy suite: 2.0 ± 1.2 mm in suite 1 and 0.6 ± 0.2 mm in suite 2. EM catheter tracking was successfully performed in three breast brachytherapy patients. Catheter tracking typically took less than 5 min and had an average accuracy of 1.7 ± 0.3 mm. CONCLUSION: Our preliminary results show a potential role for US guidance and EM needle navigation for implantation of catheters for breast brachytherapy. EM catheter tracking can accurately assess the implant geometry in breast brachytherapy patients. This methodology has the potential to evaluate catheter positions directly after the implantation and during the several fractions of the treatment.

Navigated tissue characterization during skin cancer surgery.

PURPOSE: Basal cell carcinoma (BCC) is the most commonly diagnosed skin cancer and is treated by surgical resection. Incomplete tumor removal requires surgical revision, leading to significant healthcare costs and impaired cosmesis. We investigated the clinical feasibility of a surgical navigation system for BCC surgery, based on molecular tissue characterization using rapid evaporative ionization mass spectrometry (REIMS). METHODS: REIMS enables direct tissue characterization by analysis of cell-specific molecules present within surgical smoke, produced during electrocautery tissue resection. A tissue characterization model was built by acquiring REIMS spectra of BCC, healthy skin and fat from ex vivo skin cancer specimens. This model was used for tissue characterization during navigated skin cancer surgery. Navigation was enabled by optical tracking and real-time visualization of the cautery relative to a contoured resection volume. The surgical smoke was aspirated into a mass spectrometer and directly analyzed with REIMS. Classified BCC was annotated at the real-time position of the cautery. Feasibility of the navigation system, and tissue classification accuracy for ex vivo and intraoperative surgery were evaluated. RESULTS: Fifty-four fresh excision specimens were used to build the ex vivo model of BCC, normal skin and fat, with 92% accuracy. While 3 surgeries were successfully navigated without breach of sterility, the intraoperative performance of the ex vivo model was low (< 50%). Hypotheses are: (1) the model was trained on heterogeneous mass spectra that did not originate from a single tissue type, (2) during surgery mixed tissue types were resected and thus presented to the model, and (3) the mass spectra were not validated by pathology. CONCLUSION: REIMS-navigated skin cancer surgery has the potential to detect and localize remaining tumor intraoperatively. Future work will be focused on improving our model by using a precise pencil cautery tip for burning localized tissue types, and having pathology-validated mass spectra.

Automatic Spine Ultrasound Segmentation for Scoliosis Visualization and Measurement.

OBJECTIVE: Integrate tracked ultrasound and AI methods to provide a safer and more accessible alternative to X-ray for scoliosis measurement. We propose automatic ultrasound segmentation for 3-dimensional spine visualization and scoliosis measurement to address difficulties in using ultrasound for spine imaging. METHODS: We trained a convolutional neural network for spine segmentation on ultrasound scans using data from eight healthy adult volunteers. We tested the trained network on eight pediatric patients. We evaluated image segmentation and 3-dimensional volume reconstruction for scoliosis measurement. RESULTS: As expected, fuzzy segmentation metrics reduced when trained networks were translated from healthy volunteers to patients. Recall decreased from 0.72 to 0.64 (8.2% decrease), and precision from 0.31 to 0.27 (3.7% decrease). However, after finding optimal thresholds for prediction maps, binary segmentation metrics performed better on patient data. Recall decreased from 0.98 to 0.97 (1.6% decrease), and precision from 0.10 to 0.06 (4.5% decrease). Segmentation prediction maps were reconstructed to 3-dimensional volumes and scoliosis was measured in all patients. Measurement in these reconstructions took less than 1 minute and had a maximum error of 2.2° compared to X-ray. CONCLUSION: automatic spine segmentation makes scoliosis measurement both efficient and accurate in tracked ultrasound scans. SIGNIFICANCE: Automatic segmentation may overcome the limitations of tracked ultrasound that so far prevented its use as an alternative of X-ray in scoliosis measurement.

Real-time electromagnetic navigation for breast-conserving surgery using NaviKnife technology: A matched case-control study.

Breast-conserving surgery (BCS) is a mainstay in breast cancer treatment. For nonpalpable breast cancers, current strategies have limited accuracy, contributing to high positive margin rates. We developed NaviKnife, a surgical navigation system based on real-time electromagnetic (EM) tracking. The goal of this study was to confirm the feasibility of intraoperative EM navigation in patients with nonpalpable breast cancer and to assess the potential value of surgical navigation. We recruited 40 patients with ultrasound visible, single, nonpalpable lesions, undergoing BCS. Feasibility was assessed by equipment functionality and sterility, acceptable duration of the operation, and surgeon feedback. Secondary outcomes included specimen volume, positive margin rate, and reoperation outcomes. Study patients were compared to a control group by a matched case-control analysis. There was no equipment failure or breach of sterility. The median operative time was 66 (44-119) minutes with NaviKnife vs 65 (34-158) minutes for the control (P = .64). NaviKnife contouring time was 3.2 (1.6-9) minutes. Surgeons rated navigation as easy to setup, easy to use, and useful in guiding nonpalpable tumor excision. The mean specimen volume was 95.4 ± 73.5 cm3 with NaviKnife and 140.7 ± 100.3 cm3 for the control (P = .01). The positive margin rate was 22.5% with NaviKnife and 28.7% for the control (P = .52). The re-excision specimen contained residual disease in 14.3% for NaviKnife and 50% for the control (P = .28). Our results demonstrate that real-time EM navigation is feasible in the operating room for BCS. Excisions performed with navigation result in the removal of less breast tissue without compromising postive margin rates.

Machine learning methods for automated technical skills assessment with instructional feedback in ultrasound-guided interventions.

OBJECTIVE: Currently, there is a worldwide shift toward competency-based medical education. This necessitates the use of automated skills assessment methods during self-guided interventions training. Making assessment methods that are transparent and configurable will allow assessment to be interpreted into instructional feedback. The purpose of this work is to develop and validate skills assessment methods in ultrasound-guided interventions that are transparent and configurable. METHODS: We implemented a method based upon decision trees and a method based upon fuzzy inference systems for technical skills assessment. Subsequently, we validated these methods for their ability to predict scores of operators on a 25-point global rating scale in ultrasound-guided needle insertions and their ability to provide useful feedback for training. RESULTS: Decision tree and fuzzy rule-based assessment performed comparably to state-of-the-art assessment methods. They produced median errors (on a 25-point scale) of 1.7 and 1.8 for in-plane insertions and 1.5 and 3.0 for out-of-plane insertions, respectively. In addition, these methods provided feedback that was useful for trainee learning. Decision tree assessment produced feedback with median usefulness 7 out of 7; fuzzy rule-based assessment produced feedback with median usefulness 6 out of 7. CONCLUSION: Transparent and configurable assessment methods are comparable to the state of the art and, in addition, can provide useful feedback. This demonstrates their value in self-guided interventions training curricula.

Surgery Tutor for Computational Assessment of Technical Proficiency in Soft-Tissue Tumor Resection in a Simulated Setting.

BACKGROUND: In competency-based medical education, progression between milestones requires reliable and valid methods of assessment. Surgery Tutor is an open-source motion tracking platform developed to objectively assess technical proficiency during open soft-tissue tumor resections in a simulated setting. The objective of our study was to provide evidence in support of construct validity of the scores obtained by Surgery Tutor. We hypothesized that Surgery Tutor would discriminate between novice, intermediate, and experienced operators. METHODS: Thirty participants were assigned to novice, intermediate, or experienced groups, based on the number of prior soft-tissue resections performed. Each participant resected 2 palpable and 2 nonpalpable lesions from a soft-tissue phantom. Surgery Tutor was used to track hand and instrument motions, number of tumor breaches, and time to perform each resection. Mass of excised specimens and margin status were also recorded. RESULTS: Surgery Tutor scores demonstrated "moderate" to "good" internal structure (test-retest reliability) for novice, intermediate, and experienced groups (interclass correlation coefficient = 0.596, 0.569, 0.737; p < 0.001). Evidence in support of construct validity (consequences) was demonstrated by comparing scores of novice, intermediate, and experienced participantsfor number of hand and instrument motions (690 ± 190, 597 ± 169, 469 ± 110; p < 0.001), number of tumor breaches (29 ± 34, 16 ± 11, 9 ± 6; p < 0.001), time per resection (677 ± 331 seconds, 561 ± 210 seconds, 449 ± 148 seconds; p < 0.001), mass of completely excised specimens (22 ± 7g, 21 ± 11g, 17 ± 6 g; p = 0.035), and rate of positive margin (68%, 50%, 28%; p < 0.001). There was "strong" and "moderate" relationships between motion scores and Objective Structured Assessment of Technical Skill scores, and time per resection and Objective Structured Assessment of Technical Skill scores respectively (r = -0.60, p < 0.001; r = -0.54, p < 0.001). CONCLUSION: Surgery Tutor scores demonstrate evidenceof construct validity with regards to good internal structure, consequences, and relationship to other variables in the assessment of technical proficiency duringopen soft-tissue tumor resections in a simulated setting. Utilization of Surgery Tutor can provide formative feedback and objective assessment of surgical proficiency in a simulated setting.

Self-guided training for deep brain stimulation planning using objective assessment.

OBJECTIVE: Deep brain stimulation (DBS) is an increasingly common treatment for neurodegenerative diseases. Neurosurgeons must have thorough procedural, anatomical, and functional knowledge to plan electrode trajectories and thus ensure treatment efficacy and patient safety. Developing this knowledge requires extensive training. We propose a training approach with objective assessment of neurosurgeon proficiency in DBS planning. METHODS: To assess proficiency, we propose analyzing both the viability of the planned trajectory and the manner in which the operator arrived at the trajectory. To improve understanding, we suggest a self-guided training course for DBS planning using real-time feedback. To validate the proposed measures of proficiency and training course, two experts and six novices followed the training course, and we monitored their proficiency measures throughout. RESULTS: At baseline, experts planned higher quality trajectories and did so more efficiently. As novices progressed through the training course, their proficiency measures increased significantly, trending toward expert measures. CONCLUSION: We developed and validated measures which reliably discriminate proficiency levels. These measures are integrated into a training course, which quantitatively improves trainee performance. The proposed training course can be used to improve trainees' proficiency, and the quantitative measures allow trainees' progress to be monitored.

Utility of 3D Reconstruction of 2D Liver Computed Tomography/Magnetic Resonance Images as a Surgical Planning Tool for Residents in Liver Resection Surgery.

OBJECTIVE: A fundamental aspect of surgical planning in liver resections is the identification of key vessel tributaries to preserve healthy liver tissue while fully resecting the tumor(s). Current surgical planning relies primarily on the surgeon's ability to mentally reconstruct 2D computed tomography/magnetic resonance (CT/MR) images into 3D and plan resection margins. This creates significant cognitive load, especially for trainees, as it relies on image interpretation, anatomical and surgical knowledge, experience, and spatial sense. The purpose of this study is to determine if 3D reconstruction of preoperative CT/MR images will assist resident-level trainees in making appropriate operative plans for liver resection surgery. DESIGN: Ten preoperative patient CT/MR images were selected. Images were case-matched, 5 to 2D planning and 5 to 3D planning. Images from the 3D group were segmented to create interactive digital models that the resident can manipulate to view the tumor(s) in relation to landmark hepatic structures. Residents were asked to evaluate the images and devise a surgical resection plan for each image. The resident alternated between 2D and 3D planning, in a randomly generated order. The primary outcome was the accuracy of resident's plan compared to expert opinion. Time to devise each surgical plan was the secondary outcome. Residents completed a prestudy and poststudy questionnaire regarding their experience with liver surgery and the 3D planning software. SETTING AND PARTICIPANTS: Senior level surgical residents from the Queen's University General Surgery residency program were recruited to participate. RESULTS: A total of 14 residents participated in the study. The median correct response rate was 2 of 5 (40%; range: 0-4) for the 2D group, and 3 of 5 (60%; range: 1-5) for the 3D group (p < 0.01). The average time to complete each plan was 156 ± 107 seconds for the 2D group, and 84 ± 73 seconds for the 3D group (p < 0.01). A total 13 of 14 residents found the 3D model easier to use than the 2D. Most residents noticed a difference between the 2 modalities and found that the 3D model improved their confidence with the surgical plan proposed. CONCLUSIONS: The results of this study show that 3D reconstruction for liver surgery planning increases accuracy of resident surgical planning and decreases amount of time required. 3D reconstruction would be a useful model for improving trainee understanding of liver anatomy and surgical resection, and would serve as an adjunct to current 2D planning methods. This has the potential to be developed into a module for teaching liver surgery in a competency-based medical curriculum.