Measurement of fast neutron induced (n,γ) reaction cross-section of 68Zn, 96Zr, 121Sb and 123Sb in the energy range of 1 to 2 MeV

The (n,γ) reaction cross-section for the elements 68Zn, 96Zr, 121Sb and 123Sb, present in the reactor structural/shielding materials, was measured by neutron activation technique in the neutron energy region of 1-2 MeV as very limited data is available in this energy range. Further, the neutron spectrum peaks in this energy region for the fast breeder reactors and proposed accelerator driven sub-critical systems. The natural strontium (natSr) element was used as a neutron flux monitor by considering effective combined reaction cross-section for 86Sr(n,γ)87Srm and 87Sr(n,n')87Srm reactions. The pellets of mixture of sample and monitor were irradiated by a quasi-mono energetic fast neutron beam, generated by 7Li(p,n)7Be reaction at FOTIA, Bhabha Atomic Research Centre, Mumbai, India. The activity of activation products was measured by off-line gamma-ray spectrometry using High Purity Germanium Detector (HPGe). The present data with improved uncertainty and covariance analysis enhance the cross-section data base for better constraining the evaluated data and theoretical models. The theoretical (n,γ) reaction cross-sections were calculated using TALYS 1.96, which could reasonably explain the present data with the Fermi gas level density prescription.

Validity of task-specific metrics for assessment in perineal proctectomy

BACKGROUND

Perineal proctectomy is a complex procedure that requires advanced skills. Currently, there are no simulators for training in this procedure. As part of our objective of developing a virtual reality simulator, our goal was to develop and validate task-specific metrics for the assessment of performance for this procedure. We conducted a three-phase study to establish task-specific metrics, obtain expert consensus on the appropriateness of the developed metrics, and establish the discriminant validity of the developed metrics.

METHODS

In phase I, we utilized hierarchical task analysis to formulate the metrics. In phase II, a survey involving expert colorectal surgeons determined the significance of the developed metrics. Phase III was aimed at establishing the discriminant validity for novices (PGY1-3) and experts (PGY4-5 and faculty). They performed a perineal proctectomy on a rectal prolapse model. Video recordings were independently assessed by two raters using global ratings and task-specific metrics for the procedure. Total scores for both metrics were computed and analyzed using the Kruskal-Wallis test. A Mann-Whitney U test with Benjamini-Hochberg correction was used to evaluate between-group differences. Spearman's rank correlation coefficient was computed to assess the correlation between global and task-specific scores.

RESULTS

In phase II, a total of 23 colorectal surgeons were recruited and consensus was obtained on all the task-specific metrics. In phase III, participants (n = 22) included novices (n = 15) and experts (n = 7). There was a strong positive correlation between the global and task-specific scores (rs = 0.86; P < 0.001). Significant between-group differences were detected for both global (χ2 = 15.38; P < 0.001; df = 2) and task-specific (χ2 = 11.38; P = 0.003; df = 2) scores.

CONCLUSIONS

Using a biotissue rectal prolapse model, this study documented high IRR and significant discriminant validity evidence in support of video-based assessment using task-specific metrics.

Deep Learning for Video-Based Assessment in Surgery

This surgical innovation explains how applying deep neural networks could ensure the continued use of video-based assessment.

Observation of Interatomic Coulombic Decay Induced by Double Excitation of Helium in Nanodroplets

Interatomic Coulombic decay (ICD) plays a crucial role in weakly bound complexes exposed to intense or high-energy radiation. So far, neutral or ionic atoms or molecules have been prepared in singly excited electron or hole states that can transfer energy to neighboring centers and cause ionization and radiation damage. Here we demonstrate that a doubly excited atom, despite its extremely short lifetime, can decay by ICD; evidenced by high-resolution photoelectron spectra of He nanodroplets excited to the 2s2p+ state. We find that ICD proceeds by relaxation into excited He^{*}He^{+} atom-pair states, in agreement with calculations. The ability of inducing ICD by resonant excitation far above the single-ionization threshold opens opportunities for controlling radiation damage to a high degree of element specificity and spectral selectivity.

Mechanical behavior of full-thickness burn human skin is rate-independent

Skin tissue is recognized to exhibit rate-dependent mechanical behavior under various loading conditions. Here, we report that the full-thickness burn human skin exhibits rate-independent behavior under uniaxial tensile loading conditions. Mechanical properties, namely, ultimate tensile stress, ultimate tensile strain, and toughness, and parameters of Veronda-Westmann hyperelastic material law were assessed via uniaxial tensile tests. Univariate hypothesis testing yielded no significant difference (p > 0.01) in the distributions of these properties for skin samples loaded at three different rates of 0.3 mm/s, 2 mm/s, and 8 mm/s. Multivariate multiclass classification, employing a logistic regression model, failed to effectively discriminate samples loaded at the aforementioned rates, with a classification accuracy of only 40%. The median values for ultimate tensile stress, ultimate tensile strain, and toughness are computed as 1.73 MPa, 1.69, and 1.38 MPa, respectively. The findings of this study hold considerable significance for the refinement of burn care training protocols and treatment planning, shedding new light on the unique, rate-independent behavior of burn skin.

One-shot skill assessment in high-stakes domains with limited data via meta learning

Deep Learning (DL) has achieved robust competency assessment in various high-stakes fields. However, the applicability of DL models is often hampered by their substantial data requirements and confinement to specific training domains. This prevents them from transitioning to new tasks where data is scarce. Therefore, domain adaptation emerges as a critical element for the practical implementation of DL in real-world scenarios. Herein, we introduce A-VBANet, a novel meta-learning model capable of delivering domain-agnostic skill assessment via one-shot learning. Our methodology has been tested by assessing surgical skills on five laparoscopic and robotic simulators and real-life laparoscopic cholecystectomy. Our model successfully adapted with accuracies up to 99.5 % in one-shot and 99.9 % in few-shot settings for simulated tasks and 89.7 % for laparoscopic cholecystectomy. This study marks the first instance of a domain-agnostic methodology for skill assessment in critical fields setting a precedent for the broad application of DL across diverse real-life domains with limited data.

Validation of a Virtual Simulator With Haptic Feedback for Endotracheal Intubation Training

INTRODUCTION

Endotracheal intubation (ETI) is a procedure that varies in difficulty because of patient characteristics and clinical conditions. Existing physical simulators do not encompass these variations. The Virtual Airway Skills Trainer for Endotracheal Intubation (VAST-ETI) was developed to provide different patient characteristics and high-fidelity haptic feedback to improve training.

METHODS

We demonstrate the effectiveness of VAST-ETI as a training and evaluation tool for ETI. Construct validation was evaluated by scoring the performance of experts ( N = 15) and novices ( N = 15) on the simulator to ensure its ability to distinguish technical proficiency. Convergent and predictive validity were evaluated by performing a learning curve study, in which a group of novices ( N = 7) were trained for 2 weeks using VAST-ETI and then compared with a control group ( N = 9).

RESULTS

The VAST-ETI was able to distinguish between expert and novice based on mean simulator scores ( t [88] = -6.61, P < 0.0005). When used during repeated practice, individuals demonstrated a significant increase in their score on VAST-ETI over the learning period ( F [11,220] = 7206, P < 0.001); however when compared with a control group, there was not a significant interaction effect on the simulator score. There was a significant difference between the simulator-trained and control groups ( t [12.85] = -2.258, P = 0.042) when tested in the operating room.

CONCLUSIONS

Our results demonstrate the effectiveness of virtual simulation with haptic feedback for assessing performance and training of ETI. The simulator was not able to differentiate performance between more experienced trainees and experts because of limits in simulator difficulty.

Fragmentation of water clusters formed in helium nanodroplets by charge transfer and Penning ionization

Helium nanodroplets ("HNDs") are widely used for forming tailor-made clusters and molecular complexes in a cold, transparent, and weakly interacting matrix. The characterization of embedded species by mass spectrometry is often complicated by the fragmentation and trapping of ions in the HNDs. Here, we systematically study fragment ion mass spectra of HND-aggregated water and oxygen clusters following their ionization by charge transfer ionization ("CTI") and Penning ionization ("PEI"). While the efficiency of PEI of embedded clusters is lower than for CTI by about factor 10, both the mean sizes of detected water clusters and the relative yields of unprotonated cluster ions are significantly larger, making PEI a "soft ionization" scheme. However, the tendency of ions to remain bound to HNDs leads to a reduced detection efficiency for large HNDs containing >104 helium atoms. These results are instrumental in determining optimal conditions for mass spectrometry and photoionization spectroscopy of molecular complexes and clusters aggregated in HNDs.

Endoscopic sleeve gastroplasty: stomach location and task classification for evaluation using artificial intelligence

PURPOSE

We have previously developed grading metrics to objectively measure endoscopist performance in endoscopic sleeve gastroplasty (ESG). One of our primary goals is to automate the process of measuring performance. To achieve this goal, the repeated task being performed (grasping or suturing) and the location of the endoscopic suturing device in the stomach (Incisura, Anterior Wall, Greater Curvature, or Posterior Wall) need to be accurately recorded.

METHODS

For this study, we populated our dataset using screenshots and video clips from experts carrying out the ESG procedure on ex vivo porcine specimens. Data augmentation was used to enlarge our dataset, and synthetic minority oversampling (SMOTE) to balance it. We performed stomach localization for parts of the stomach and task classification using deep learning for images and computer vision for videos.

RESULTS

Classifying the stomach's location from the endoscope without SMOTE for images resulted in 89% and 84% testing and validation accuracy, respectively. For classifying the location of the stomach from the endoscope with SMOTE, the accuracies were 97% and 90% for images, while for videos, the accuracies were 99% and 98% for testing and validation, respectively. For task classification, the accuracies were 97% and 89% for images, while for videos, the accuracies were 100% for both testing and validation, respectively.

CONCLUSION

We classified the four different stomach parts manipulated during the ESG procedure with 97% training accuracy and classified two repeated tasks with 99% training accuracy with images. We also classified the four parts of the stomach with a 99% training accuracy and two repeated tasks with a 100% training accuracy with video frames. This work will be essential in automating feedback mechanisms for learners in ESG.

Brain-behavior analysis of transcranial direct current stimulation effects on a complex surgical motor task

Transcranial Direct Current Stimulation (tDCS) has demonstrated its potential in enhancing surgical training and performance compared to sham tDCS. However, optimizing its efficacy requires the selection of appropriate brain targets informed by neuroimaging and mechanistic understanding. Previous studies have established the feasibility of using portable brain imaging, combining functional near-infrared spectroscopy (fNIRS) with tDCS during Fundamentals of Laparoscopic Surgery (FLS) tasks. This allows concurrent monitoring of cortical activations. Building on these foundations, our study aimed to explore the multi-modal imaging of the brain response using fNIRS and electroencephalogram (EEG) to tDCS targeting the right cerebellar (CER) and left ventrolateral prefrontal cortex (PFC) during a challenging FLS suturing with intracorporeal knot tying task. Involving twelve novices with a medical/premedical background (age: 22-28 years, two males, 10 females with one female with left-hand dominance), our investigation sought mechanistic insights into tDCS effects on brain areas related to error-based learning, a fundamental skill acquisition mechanism. The results revealed that right CER tDCS applied to the posterior lobe elicited a statistically significant (q < 0.05) brain response in bilateral prefrontal areas at the onset of the FLS task, surpassing the response seen with sham tDCS. Additionally, right CER tDCS led to a significant (p < 0.05) improvement in FLS scores compared to sham tDCS. Conversely, the left PFC tDCS did not yield a statistically significant brain response or improvement in FLS performance. In conclusion, right CER tDCS demonstrated the activation of bilateral prefrontal brain areas, providing valuable mechanistic insights into the effects of CER tDCS on FLS peformance. These insights motivate future investigations into the effects of CER tDCS on error-related perception-action coupling through directed functional connectivity studies.

Distinguishing Laparoscopic Surgery Experts from Novices Using EEG Topographic Features

The study aimed to differentiate experts from novices in laparoscopic surgery tasks using electroencephalogram (EEG) topographic features. A microstate-based common spatial pattern (CSP) analysis with linear discriminant analysis (LDA) was compared to a topography-preserving convolutional neural network (CNN) approach. Expert surgeons (N = 10) and novice medical residents (N = 13) performed laparoscopic suturing tasks, and EEG data from 8 experts and 13 novices were analysed. Microstate-based CSP with LDA revealed distinct spatial patterns in the frontal and parietal cortices for experts, while novices showed frontal cortex involvement. The 3D CNN model (ESNet) demonstrated a superior classification performance (accuracy > 98%, sensitivity 99.30%, specificity 99.70%, F1 score 98.51%, MCC 97.56%) compared to the microstate based CSP analysis with LDA (accuracy ~90%). Combining spatial and temporal information in the 3D CNN model enhanced classifier accuracy and highlighted the importance of the parietal-temporal-occipital association region in differentiating experts and novices.

Cognitive Task Analysis of Escharotomy

INTRODUCTION

With the Army's emerging doctrine of prolonged field care, and with burns being a common injury among soldiers, non-expert providers must be trained to perform escharotomy when indicated. However, the existing physical simulators and training protocols are not sufficient for training non-experts for performing effective escharotomy. Hence, to provide guidance in developing realistic escharotomy simulators and effective training protocols suitable for prolonged field care, a cognitive task analysis (CTA) is needed. This work aims to obtain educative information from expert burn surgeons regarding escharotomy procedures via the CTA.

MATERIALS AND METHODS

The CTA was done by interviewing five subject matter experts with experience in performing escharotomy ranging from 20 to over 100 procedures and analyzing their responses. Interview questions were developed to obtain educative information from expert burn surgeons regarding the escharotomy procedure. A "gold standard protocol" was developed based on the CTA of each of the subject matter experts.

RESULTS

The CTA helped identify general themes, including objectives, conditions that mandate escharotomy, signs of successful escharotomy, precautions, challenges, decisions, and performance standards, and specific learning goals such as the use of equipment, vital signs, performing the procedure, and preoperative and postoperative care. A unique aspect of this CTA is that it identifies the background information and preparations that could be useful to the practitioners at various levels of expertise.

CONCLUSIONS

The CTA enabled us to compile a "gold standard protocol" for escharotomy that may serve as a guide for practitioners at various levels of expertise. This information will provide a framework for escharotomy training systems and simulators.

Split & Join: An Efficient Approach for Simulating Stapled Intestinal Anastomosis in Virtual Reality

Colorectal cancer is a life-threatening disease. It is the second leading cause of cancer-related deaths in the United States. Stapled anastomosis is a rapid treatment for colorectal cancer and other intestinal diseases and has become an integral part of routine surgical practice. However, to the best of our knowledge, there is no existing work simulating intestinal anastomosis that often involves sophisticated soft tissue manipulations such as cutting and stitching. In this paper, for the first time, we propose a novel split and join approach to simulate a side-to-side stapled intestinal anastomosis in virtual reality. We mimic the intestine model using a new hybrid representation - a grid-linked particles model for physics simulation and a surface mesh for rendering. The proposed split and join operations handle the updates of both the grid-linked particles model and the surface mesh during the anastomosis procedure. The simulation results demonstrate the feasibility of the proposed approach in simulating intestine models and the side-to-side anastomosis operation.

Assessment of Surgical Tasks Using Neuroimaging Dataset (ASTaUND)

Functional near-infrared spectroscopy (fNIRS) is a neuroimaging tool for studying brain activity in mobile subjects. Open-access fNIRS datasets are limited to simple and/or motion-restricted tasks. Here, we report a fNIRS dataset acquired on mobile subjects performing Fundamentals of Laparoscopic Surgery (FLS) tasks in a laboratory environment. Demonstrating competency in the FLS tasks is a prerequisite for board certification in general surgery in the United States. The ASTaUND data set was acquired over four different studies. We provide the relevant information about the hardware, FLS task execution protocols, and subject demographics to facilitate the use of this open-access data set. We also provide the concurrent FLS scores, a quantitative metric for surgical skill assessment developed by the FLS committee. This data set is expected to support the growing field of assessing surgical skills via neuroimaging data and provide an example of data processing pipeline for use in realistic, non-restrictive environments.

Review of management practices of sinogenic intracranial abscesses in children

OBJECTIVE

This study aimed to evaluate the management practices and outcomes in children with sinogenic intracranial suppuration.

METHOD

This was a retrospective cohort study in a single paediatric tertiary unit that included patients younger than 18 years with radiologically confirmed intracranial abscess, including subdural empyema and epidural or intraparenchymal abscess secondary to sinusitis. Main outcomes studied were rate of return to the operating theatre, length of hospital stay, death in less than 90 days and neurological disability at 6 months.

RESULTS

A cohort of 39 consecutive patients presenting between 2000 and 2020 were eligible for inclusion. Subdural empyema was the most common intracranial complication followed by extradural abscess and intraparenchymal abscess. Mean length of hospital stay was 42 days. Sixteen patients were managed with combined ENT and neurosurgical interventions, 15 patients underwent ENT procedures alone and 4 patients had only neurosurgical drainage. Four patients initially underwent non-operative management. The rates of return to the operating theatre, neurological deficits and 90-day mortality were 19, 9 and 3, respectively, and were comparable across the 4 treatment arms. In the univariate logistic regression, only the size of an intracranial abscess was found be associated with an increased likelihood of return to the operating theatre, whereas combined ENT and neurosurgical intervention did not result in improved outcomes.

CONCLUSION

Sinogenic intracranial abscesses are associated with significant morbidity and mortality. The size of an intracranial abscess has a strong association with a need for a revision surgery.

What are essential laparoscopic skills these days? Results of the SAGES Fundamentals of Laparoscopic Surgery (FLS) Committee technical skills survey

INTRODUCTION

The Fundamentals of Laparoscopic Surgery (FLS) program tests basic knowledge and skills required to perform laparoscopic surgery. Educational experiences in laparoscopic training and development of associated competencies have evolved since FLS inception, making it important to review the definition of fundamental laparoscopic skills. The Society of American Gastrointestinal and Endoscopic Surgeons (SAGES) assigned an FLS Technical Skills Working Group to characterize technical skills used in basic laparoscopic surgery in current practice contexts and their possible application to future FLS tests.

METHODS

A group of subject matter experts defined an inventory of 65 laparoscopic skills using a Nominal Group Technique. From these, a survey was developed rating these items for importance, frequency of use, and priority for testing for FLS certification. This survey was distributed to SAGES members, recent recipients of FLS certification, and members of the Association of Program Directors in Surgery (APDS). Results were collected using a secure web-based survey platform.

RESULTS

Complete data were available for 1742 surveys. Of these, 1143 comprised results for post-residency participants who performed advanced procedures. Seventeen competencies were identified for FLS testing prioritization by determining the proportion of respondents who identified them of highest priority, at median (50th percentile) of the maximum survey scale rating. These included basic peritoneal access, laparoscope and instrument use, tissue manipulation, and specific problem management skills. Sixteen could be used to show appropriateness of the domain construct by confirmatory factor analysis. Of these 8 could be characterized as manipulative tasks. Of these 5 mapped to current FLS tasks.

CONCLUSIONS

This survey-identified competencies, some of which are currently assessed in FLS, with a high level of priority for testing. Further work is needed to determine if this should prompt consideration of changes or additions to the FLS technical skills test component.

Efficient Indirect Interatomic Coulombic Decay Induced by Photoelectron Impact Excitation in Large Pure Helium Nanodroplets

Ionization of matter by energetic radiation generally causes complex secondary reactions that are hard to decipher. Using large helium nanodroplets irradiated by extreme ultraviolet (XUV) photons, we show that the full chain of processes ensuing primary photoionization can be tracked in detail by means of high-resolution electron spectroscopy. We find that elastic and inelastic scattering of photoelectrons efficiently induces interatomic Coulombic decay (ICD) in the droplets. This type of indirect ICD even becomes the dominant process of electron emission in nearly the entire XUV range in large droplets with radius ≳40  nm. Indirect ICD processes induced by electron scattering likely play an important role in other condensed-phase systems exposed to ionizing radiation as well, including biological matter.

Intelligent Virtual Operating Room for Enhancing Nontechnical Skills

This article discusses an intelligent immersive virtual operating room to enable teams to train in a distributed fashion wearing head-mounted displays.

Specular Reflection Removal for 3D Reconstruction of Tissues using Endoscopy Videos

Endoscopy is widely employed for diagnostic examination of the interior of organs and body cavities and numerous surgical interventions. Still, the inability to correlate individual 2D images with 3D organ morphology limits its applications, especially in intra-operative planning and navigation, disease physiology, cancer surveillance, etc. As a result, most endoscopy videos, which carry enormous data potential, are used only for real-time guidance and are discarded after collection. We present a complete method for the 3D reconstruction of inner organs that suggests image extraction techniques from endoscopic videos and a novel image pre-processing technique to reconstruct and visualize a 3D model of organs from an endoscopic video. We use advanced computer vision methods and do not require any modifications to the clinical-grade endoscopy hardware. We have also formalized an image acquisition protocol through experimentation with a calibrated test bed. We validate the accuracy and robustness of our reconstruction using a test bed with known ground truth. Our method can significantly contribute to endoscopy-based diagnostic and surgical procedures using comprehensive tissue and tumor 3D visualization.

Changes in functional neuroimaging measures as novices gain proficiency on the fundamentals of laparoscopic surgery suturing task

Significance

As trainees practice fundamental surgical skills, they typically rely on performance measures such as time and errors, which are limited in their sensitivity.

Aim

The goal of our study was to evaluate the use of portable neuroimaging measures to map the neural processes associated with learning basic surgical skills.

Approach

Twenty-one subjects completed 15 sessions of training on the fundamentals of laparoscopic surgery (FLS) suture with intracorporeal knot-tying task in a box trainer. Functional near infrared spectroscopy data were recorded using an optode montage that covered the prefrontal and sensorimotor brain areas throughout the task. Average oxy-hemoglobin (HbO) changes were determined for repetitions performed during the first week of training compared with the third week of training. Statistical differences between the time periods were evaluated using a general linear model of the HbO changes.

Results

Average performance scores across task repetitions increased significantly from the first day to the last day of training ( p < 0.01 ). During the first day of training, there was significant lateral prefrontal cortex (PFC) activation. On the final day, significant activation was observed in the PFC, as well as the sensorimotor areas. When comparing the two periods, significant differences in activation ( p < 0.05 ) were found for the right medial PFC and the right inferior parietal gyrus. While gaining proficiency, trainees activated the perception-action cycle to build a perceptual model and then apply the model to improve task execution.

Conclusions

Learners engaged the sensorimotor areas more substantially as they developed skill on the FLS suturing task. These findings are consistent with findings for the FLS pattern cutting task and contribute to the development of objective metrics for skill evaluation.

A statistical machine learning approach linking molecular conformational changes to altered mechanical characteristics of skin due to thermal injury

This article develops statistical machine learning models to predict the mechanical properties of skin tissue subjected to thermal injury based on the Raman spectra associated with conformational changes of the molecules in the burned tissue. Ex vivo porcine skin tissue samples were exposed to controlled burn conditions at 200 °F for five different durations: (i) 10s, (ii) 20s, (iii) 30s, (iv) 40s, and (v) 50s. For each burn condition, Raman spectra of wavenumbers 500-2000 cm^-1 were measured from the tissue samples, and tensile testing on the same samples yielded their material properties, including, ultimate tensile strain, ultimate tensile stress, and toughness. Partial least squares regression models were established such that the Raman spectra, describing conformational changes in the tissue, could accurately predict ultimate tensile stress, toughness, and ultimate tensile strain of the burned skin tissues with R² values of 0.8, 0.8, and 0.7, respectively, using leave-two-out cross validation scheme. An independent assessment of the resultant models showed that amino acids, proteins & lipids, and amide III components of skin tissue significantly influence the prediction of the properties of the burned skin tissue. In contrast, amide I has a lesser but still noticeable effect. These results are consistent with similar observations found in the literature on the mechanical characterization of burned skin tissue.

Skill-level classification and performance evaluation for endoscopic sleeve gastroplasty

BACKGROUND

We previously developed grading metrics for quantitative performance measurement for simulated endoscopic sleeve gastroplasty (ESG) to create a scalar reference to classify subjects into experts and novices. In this work, we used synthetic data generation and expanded our skill level analysis using machine learning techniques.

METHODS

We used the synthetic data generation algorithm SMOTE to expand and balance our dataset of seven actual simulated ESG procedures using synthetic data. We performed optimization to seek optimum metrics to classify experts and novices by identifying the most critical and distinctive sub-tasks. We used support vector machine (SVM), AdaBoost, K-nearest neighbors (KNN) Kernel Fisher discriminant analysis (KFDA), random forest, and decision tree classifiers to classify surgeons as experts or novices after grading. Furthermore, we used an optimization model to create weights for each task and separate the clusters by maximizing the distance between the expert and novice scores.

RESULTS

We split our dataset into a training set of 15 samples and a testing dataset of five samples. We put this dataset through six classifiers, SVM, KFDA, AdaBoost, KNN, random forest, and decision tree, resulting in 0.94, 0.94, 1.00, 1.00, 1.00, and 1.00 accuracy, respectively, for training and 1.00 accuracy for the testing results for SVM and AdaBoost. Our optimization model maximized the distance between the expert and novice groups from 2 to 53.72.

CONCLUSION

This paper shows that feature reduction, in combination with classification algorithms such as SVM and KNN, can be used in tandem to classify endoscopists as experts or novices based on their results recorded using our grading metrics. Furthermore, this work introduces a non-linear constraint optimization to separate the two clusters and find the most important tasks using weights.

Objective metrics for hand-sewn bowel anastomoses can differentiate novice from expert surgeons

BACKGROUND

Assessing performance automatically in a virtual reality trainer or from recorded videos is advantageous but needs validated objective metrics. The purpose of this study is to obtain expert consensus and validate task-specific metrics developed for assessing performance in double-layered end-to-end anastomosis.

MATERIALS AND METHODS

Subjects were recruited into expert (PGY 4-5, colorectal surgery residents, and attendings) and novice (PGY 1-3) groups. Weighted average scores of experts for each metric item, completion time, and the total scores computed using global and task-specific metrics were computed for assessment.

RESULTS

A total of 43 expert surgeons rated our task-specific metric items with weighted averages ranging from 3.33 to 4.5 on a 5-point Likert scale. A total of 20 subjects (10 novices and 10 experts) participated in validation study. The novice group completed the task significantly more slowly than the experienced group (37.67 ± 7.09 vs 25.47 ± 7.82 min, p = 0.001). In addition, both the global rating scale (23.47 ± 4.28 vs 28.3 ± 3.85, p = 0.016) and the task-specific metrics showed a significant difference in performance between the two groups (38.77 ± 2.83 vs 42.58 ± 4.56 p = 0.027) following partial least-squares (PLS) regression. Furthermore, PLS regression showed that only two metric items (Stay suture tension and Tool handling) could reliably differentiate the performance between the groups (20.41 ± 2.42 vs 24.28 ± 4.09 vs, p = 0.037).

CONCLUSIONS

Our study shows that our task-specific metrics have significant discriminant validity and can be used to evaluate the technical skills for this procedure.