Technical skill assessment in minimally invasive surgery using artificial intelligence: a systematic review.

BACKGROUND Technical skill assessment in surgery relies on expert opinion. Therefore, it is time-consuming, costly, and often lacks objectivity. Analysis of intraoperative data by artificial intelligence (AI) has the potential for automated technical skill assessment. The aim of this systematic review was to analyze the performance, external validity, and generalizability of AI models for technical skill assessment in minimally invasive surgery. METHODS A systematic search of Medline, Embase, Web of Science, and IEEE Xplore was performed to identify original articles reporting the use of AI in the assessment of technical skill in minimally invasive surgery. Risk of bias (RoB) and quality of the included studies were analyzed according to Quality Assessment of Diagnostic Accuracy Studies criteria and the modified Joanna Briggs Institute checklists, respectively. Findings were reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. RESULTS In total, 1958 articles were identified, 50 articles met eligibility criteria and were analyzed. Motion data extracted from surgical videos (n = 25) or kinematic data from robotic systems or sensors (n = 22) were the most frequent input data for AI. Most studies used deep learning (n = 34) and predicted technical skills using an ordinal assessment scale (n = 36) with good accuracies in simulated settings. However, all proposed models were in development stage, only 4 studies were externally validated and 8 showed a low RoB. CONCLUSION AI showed good performance in technical skill assessment in minimally invasive surgery. However, models often lacked external validity and generalizability. Therefore, models should be benchmarked using predefined performance metrics and tested in clinical implementation studies

Proposal and multicentric validation of a laparoscopic Roux-en-Y gastric bypass surgery ontology.

BACKGROUND Phase and step annotation in surgical videos is a prerequisite for surgical scene understanding and for downstream tasks like intraoperative feedback or assistance. However, most ontologies are applied on small monocentric datasets and lack external validation. To overcome these limitations an ontology for phases and steps of laparoscopic Roux-en-Y gastric bypass (LRYGB) is proposed and validated on a multicentric dataset in terms of inter- and intra-rater reliability (inter-/intra-RR). METHODS The proposed LRYGB ontology consists of 12 phase and 46 step definitions that are hierarchically structured. Two board certified surgeons (raters) with > 10 years of clinical experience applied the proposed ontology on two datasets: (1) StraBypass40 consists of 40 LRYGB videos from Nouvel Hôpital Civil, Strasbourg, France and (2) BernBypass70 consists of 70 LRYGB videos from Inselspital, Bern University Hospital, Bern, Switzerland. To assess inter-RR the two raters' annotations of ten randomly chosen videos from StraBypass40 and BernBypass70 each, were compared. To assess intra-RR ten randomly chosen videos were annotated twice by the same rater and annotations were compared. Inter-RR was calculated using Cohen's kappa. Additionally, for inter- and intra-RR accuracy, precision, recall, F1-score, and application dependent metrics were applied. RESULTS The mean ± SD video duration was 108 ± 33 min and 75 ± 21 min in StraBypass40 and BernBypass70, respectively. The proposed ontology shows an inter-RR of 96.8 ± 2.7% for phases and 85.4 ± 6.0% for steps on StraBypass40 and 94.9 ± 5.8% for phases and 76.1 ± 13.9% for steps on BernBypass70. The overall Cohen's kappa of inter-RR was 95.9 ± 4.3% for phases and 80.8 ± 10.0% for steps. Intra-RR showed an accuracy of 98.4 ± 1.1% for phases and 88.1 ± 8.1% for steps. CONCLUSION The proposed ontology shows an excellent inter- and intra-RR and should therefore be implemented routinely in phase and step annotation of LRYGB

ClipAssistNet: bringing real-time safety feedback to operating rooms

Purpose: Cholecystectomy is one of the most common laparoscopic procedures. A critical phase of laparoscopic cholecystectomy consists in clipping the cystic duct and artery before cutting them. Surgeons can improve the clipping safety by ensuring full visibility of the clipper, while enclosing the artery or the duct with the clip applier jaws. This can prevent unintentional interaction with neighboring tissues or clip misplacement. In this article, we present a novel real-time feedback to ensure safe visibility of the instrument during this critical phase. This feedback incites surgeons to keep the tip of their clip applier visible while operating. Methods: We present a new dataset of 300 laparoscopic cholecystectomy videos with frame-wise annotation of clipper tip visibility. We further present ClipAssistNet, a neural network-based image classifier which detects the clipper tip visibility in single frames. ClipAssistNet ensembles predictions from 5 neural networks trained on different subsets of the dataset. Results: Our model learns to classify the clipper tip visibility by detecting its presence in the image. Measured on a separate test set, ClipAssistNet classifies the clipper tip visibility with an AUROC of 0.9107, and 66.15% specificity at 95% sensitivity. Additionally, it can perform real-time inference (16 FPS) on an embedded computing board; this enables its deployment in operating room settings. Conclusion: This work presents a new application of computer-assisted surgery for laparoscopic cholecystectomy, namely real-time feedback on adequate visibility of the clip applier. We believe this feedback can increase surgeons' attentiveness when departing from safe visibility during the critical clipping of the cystic duct and artery

Surgical Phase Recognition: From Public Datasets to Real-World Data

Automated recognition of surgical phases is a prerequisite for computer-assisted analysis of surgeries. The research on phase recognition has been mostly driven by publicly available datasets of laparoscopic cholecystectomy (Lap Chole) videos. Yet, videos observed in real-world settings might contain challenges, such as additional phases and longer videos, which may be missing in curated public datasets. In this work, we study (i) the possible data distribution discrepancy between videos observed in a given medical center and videos from existing public datasets, and (ii) the potential impact of this distribution difference on model development. To this end, we gathered a large, private dataset of 384 Lap Chole videos. Our dataset contained all videos, including emergency surgeries and teaching cases, recorded in a continuous time frame of five years. We observed strong differences between our dataset and the most commonly used public dataset for surgical phase recognition, Cholec80. For instance, our videos were much longer, included additional phases, and had more complex transitions between phases. We further trained and compared several state-of-the-art phase recognition models on our dataset. The models’ performances greatly varied across surgical phases and videos. In particular, our results highlighted the challenge of recognizing extremely under- represented phases (usually missing in public datasets); the major phases were recognized with at least 76 percent recall. Overall, our results highlighted the need to better understand the distribution of the video data phase that recognition models are trained on

Work Characteristics of Acute Care Surgeons at a Swiss Tertiary Care Hospital: A Prospective One-Month Snapshot Study.

BACKGROUND Multiple acute care surgery (ACS) working models have been implemented. To optimize resources and on-call rosters, knowledge about work characteristics is required. Therefore, this study aimed to investigate the daily work characteristics of ACS surgeons at a Swiss tertiary care hospital. METHODS Single-center prospective snapshot study. In February 2020, ACS fellows prospectively recorded their work characteristics, case volume and surgical case mix for 20 day shifts and 16 night shifts. Work characteristics were categorized in 11 different activities and documented in intervals of 30 min. Descriptive statistics were applied. RESULTS A total of 432.5 working hours (h) were documented and characterized. The three main activities 'surgery,' 'patient consultations' and 'administrative work' ranged from 30.8 to 35.9% of the documented working time. A total of 46 surgical interventions were performed. In total, during day shifts, there were 16 elective and 15 emergency interventions, during night shifts 15 emergency interventions. For surgery, two peaks between 10:00 a.m.-02:00 p.m. and 08:00 p.m.-11:00 p.m. were observed. A total of 225 patient were consulted, with a first peak between 08:00 a.m. and 11:00 a.m. and a second, wider peak between 02:00 p.m. and 02:00 a.m. CONCLUSION The three main activities 'surgery,' 'patient consultations' and 'administrative work' were comparable with approximately one third of the working time each. There was a bimodal temporal distribution for both surgery and patient consultations. These results may help to improve hospital resources and on-call rosters of ACS services

Immunization coverage and risk factors for failure to immunize within the Expanded Programme on Immunization in Kenya after introduction of new Haemophilus influenzae type b and hepatitis b virus antigens

Background: Kenya introduced a pentavalent vaccine including the DTP, Haemophilus influenzae type b and hepatitis b virus antigens in Nov 2001 and strengthened immunization services. We estimated immunization coverage before and after introduction, timeliness of vaccination and risk factors for failure to immunize in Kilifi district, Kenya. Methods: In Nov 2002 we performed WHO cluster-sample surveys of > 200 children scheduled for vaccination before or after introduction of pentavalent vaccine. In Mar 2004 we conducted a simple random sample (SRS) survey of 204 children aged 9 - 23 months. Coverage was estimated by inverse Kaplan-Meier survival analysis of vaccine- card and mothers' recall data and corroborated by reviewing administrative records from national and provincial vaccine stores. The contribution to timely immunization of distance from clinic, seasonal rainfall, mother's age, and family size was estimated by a proportional hazards model. Results: Immunization coverage for three DTP and pentavalent doses was 100% before and 91% after pentavalent vaccine introduction, respectively. By SRS survey, coverage was 88% for three pentavalent doses. The median age at first, second and third vaccine dose was 8, 13 and 18 weeks. Vials dispatched to Kilifi District during 2001 - 2003 would provide three immunizations for 92% of the birth cohort. Immunization rate ratios were reduced with every kilometre of distance from home to vaccine clinic (HR 0.95, CI 0.91 - 1.00), rainy seasons ( HR 0.73, 95% CI 0.61 - 0.89) and family size, increasing progressively up to 4 children ( HR 0.55, 95% CI 0.41 - 0.73). Conclusion: Vaccine coverage was high before and after introduction of pentavalent vaccine, but most doses were given late. Coverage is limited by seasonal factors and family siz

V-band Doppler backscattering diagnostic in the TCV tokamak

A variable configuration V-band heterodyne Doppler back-scattering diagnostic has been recently made operational in the tokamak a configuration variable. This article describes the hardware setup options, flexible quasi-optical launcher antenna, data-analysis techniques, and first data. The diagnostic uses a fast arbitrary waveform generator as the main oscillator and commercial vector network analyzer extension modules as the main mm-wave hardware. It allows sweepable single or multi-frequency operation. A flexible quasi-optical launcher antenna allows 3D poloidal (10 degrees - 58 degrees) and toroidal (-180 degrees to 180 degrees) steering of the beam with 0.2 degrees accuracy. A pair of fast HE11 miter-bend polarizers allow flexible coupling to either O or X mode and programmable polarization changes during the shot. These have been used to measure the magnetic-field pitch angle in the edge of the plasma by monitoring the backscattered signal power. Ray-tracing simulations reveal an available k(perpendicular to) range between 3 and 16 cm(-1) with a resolution of 2-4 cm(-1). Perpendicular rotation velocity estimates compare well against ExB plasma poloidal rotation estimates from charge exchange recombination spectroscopy