Evolution of the digital operating room: the place of video technology in surgery

Privacy-proof Live Surgery Streaming: Development and Validation of a Low-cost, Real-time Robotic Surgery Anonymization Algorithm

OBJECTIVE

Develop a pioneer surgical anonymization algorithm for reliable and accurate real-time removal of out-of-body images validated across various robotic platforms.

BACKGROUND

The use of surgical video data has become a common practice in enhancing research and training. Video sharing requires complete anonymization, which, in the case of endoscopic surgery, entails the removal of all nonsurgical video frames where the endoscope can record the patient or operating room staff. To date, no openly available algorithmic solution for surgical anonymization offers reliable real-time anonymization for video streaming, which is also robotic-platform and procedure-independent.

METHODS

A data set of 63 surgical videos of 6 procedures performed on four robotic systems was annotated for out-of-body sequences. The resulting 496.828 images were used to develop a deep learning algorithm that automatically detected out-of-body frames. Our solution was subsequently benchmarked against existing anonymization methods. In addition, we offer a postprocessing step to enhance the performance and test a low-cost setup for real-time anonymization during live surgery streaming.

RESULTS

Framewise anonymization yielded a receiver operating characteristic area under the curve score of 99.46% on unseen procedures, increasing to 99.89% after postprocessing. Our Robotic Anonymization Network outperforms previous state-of-the-art algorithms, even on unseen procedural types, despite the fact that alternative solutions are explicitly trained using these procedures.

CONCLUSIONS

Our deep learning model, Robotic Anonymization Network, offers reliable, accurate, and safe real-time anonymization during complex and lengthy surgical procedures regardless of the robotic platform. The model can be used in real time for surgical live streaming and is openly available.

Scalpel and strife: Assessing the impact of Sudan's ongoing civil war on surgical practice and healthcare delivery

BACKGROUND

This study aims to shed light on the profound ramifications of the military conflict that started in April 2023 on surgical practice in Sudan.

METHODS

This is a survey-based study. The survey link was disseminated to Sudanese medical practitioners via various social media (WhatsApp, Telegram, X (previously twitter) and Facebook) channels. We included only responses from medical practitioners working in the surgical specialities.

RESULTS

A total of 90 responses have been collected. All participants were working in surgical service provision institutes. Sixty per cent of the responses were from the age group 25-35 years old, and two-thirds of the total cohort either left Sudan or was internally displaced because of the conflict. Moreover, 51% are no longer practising because they had to flee the conflict area (75%) or because the hospital is out of service (20%). There was a significant drop in the average number of emergency and elective lists.

CONCLUSION

The military conflict affected Sudan's already strained health system. There was a significant drop in the average number of emergency and elective lists with surgeons out of practice because they had to flee the conflict area and hospitals were out of service.

Adoption of routine surgical video recording: a nationwide freedom of information act request across England and Wales

Background

Surgical video contains data with significant potential to improve surgical outcome assessment, quality assurance, education, and research. Current utilisation of surgical video recording is unknown and related policies/governance structures are unclear.

Methods

A nationwide Freedom of Information (FOI) request concerning surgical video recording, technology, consent, access, and governance was sent to all acute National Health Service (NHS) trusts/boards in England/Wales between 20th February and 20th March 2023.

Findings

140/144 (97.2%) trusts/boards in England/Wales responded to the FOI request. Surgical procedures were routinely recorded in 22 trusts/boards. The median estimate of consultant surgeons routinely recording their procedures was 20%. Surgical video was stored on internal systems (n = 27), third-party products (n = 29), and both (n = 9). 32/140 (22.9%) trusts/boards ask for consent to record procedures as part of routine care. Consent for recording included non-clinical purposes in 55/140 (39.3%) trusts/boards. Policies for surgeon/patient access to surgical video were available in 48/140 (34.3%) and 32/140 (22.9%) trusts/boards, respectively. Surgical video was used for non-clinical purposes in 64/140 (45.7%) trusts/boards. Governance policies covering surgical video recording, use, and/or storage were available from 59/140 (42.1%) trusts/boards.

Interpretation

There is significant heterogeneity in surgical video recording practices in England and Wales. A minority of trusts/boards routinely record surgical procedures, with large variation in recording/storage practices indicating scope for NHS-wide coordination. Revision of surgical video consent, accessibility, and governance policies should be prioritised by trusts/boards to protect key stakeholders. Increased availability of surgical video is essential for patients and surgeons to maximally benefit from the ongoing digital transformation of surgery.

Funding

KL is supported by an NIHR Academic Clinical Fellowship and acknowledges infrastructure support for this research from the National Institute for Health Research (NIHR) Imperial Biomedical Research Centre (BRC).

Deep Learning-Assisted Segmentation and Classification of Brain Tumor Types on Magnetic Resonance and Surgical Microscope Images

OBJECTIVE

The primary aim of this research was to harness the capabilities of deep learning to enhance neurosurgical procedures, focusing on accurate tumor boundary delineation and classification. Through advanced diagnostic tools, we aimed to offer surgeons a more insightful perspective during surgeries, improving surgical outcomes and patient care.

METHODS

The study deployed the Mask R-convolutional neural network (CNN) architecture, leveraging its sophisticated features to process and analyze data from surgical microscope videos and preoperative magnetic resonance images. Resnet101 and Resnet50 backbone networks are used in the Mask R-CNN method, and experimental results are given. We subsequently tested its performance across various metrics, such as accuracy, precision, recall, dice coefficient (DICE), and Jaccard index. Deep learning models were trained from magnetic resonance imaging and surgical microscope images, and the classification result obtained for each patient was combined with the weighted average.

RESULTS

The algorithm exhibited remarkable capabilities in distinguishing among meningiomas, metastases, and high-grade glial tumors. Specifically, for the Mask R-CNN Resnet 101 architecture, precision, recall, DICE, and Jaccard index values were recorded as 96%, 93%, 91%, and 84%, respectively. Conversely, for the Mask R-CNN Resnet 50 architecture, these values stood at 94%, 89%, 89%, and 82%. Additionally, the model achieved an impressive DICE score range of 94%-95% and an accuracy of 98% in pathology estimation.

CONCLUSIONS

As illustrated in our study, the confluence of deep learning with neurosurgical procedures marks a transformative phase in medical science. The results are promising but underscore diverse data sets' significance for training and refining these deep learning models.

Implementation of a robotic surgical practice in inflammatory bowel disease

Robotics adoption has increased in colorectal surgery. While there are well-established advantages and standardised techniques for cancer patients, the use of robotic surgery in inflammatory bowel disease (IBD) has not been studied yet. To evaluate the feasibility and safety of robotic surgery for IBD patients. Prospectively data in IBD patients having robotic resection at Guy's and St Thomas' hospital. All resections performed by a single colorectal surgeon specialised in IBD, utilising DaVinci platform. July 2021 to January 2023, 59 robotic IBD cases performed, 14 ulcerative colitis (UC) and 45 Crohn's disease (CD). Average age; CD patients 35, UC 33 years. Average Body mass index (BMI); 23 for CD and 26.9 for UC patients. In total, we performed 31 ileo-caecal resections (ICR) with primary anastomosis (18 Kono-S anastomosis, 6 mechanical anastomosis and 7 ileo-colostomy), of those 4 had multivisceral resections (large bowel, bladder, ovary). Furthermore, 14 subtotal colectomy (1 emergency), 8 proctectomy, 3 panproctocolectomy and 3 ileoanal J pouch. 18 of the 45 patients (45.0%) with Crohn's disease had ongoing fistulating disease to other parts of the GI tract (small or large bowel). ICR were performed using different three ports position, depending on the anatomy established prior to surgery with magnetic resonance images (MRI). One patient had conversion to open due to anaesthetic problems and one patient required re-operation to refashion stoma. 98.0% cases completed robotically. Median Length of hospital stay (LOS) was 7 days for CD and 7 for UC cases, including LOS in patients on pre-operative parenteral nutrition. Robotic colorectal techniques can be safely used for patients with IBD, even with fistulating disease. Future research and collaborations are necessary to standardize technique within institutions.

Current Status of Auricular Reconstruction Strategy Development

Microtia has severe physical and psychological impacts on patients, and auricular reconstruction offers improved esthetics and function, alleviating psychological issues. Microtia is a congenital disease caused by a multifactorial interaction of environmental and genetic factors, with complex clinical manifestations. Classification assessment aids in determining treatment strategies. Auricular reconstruction is the primary treatment for severe microtia, focusing on the selection of auricular scaffold materials, the construction of auricular morphology, and skin and soft tissue scaffold coverage. Autologous rib cartilage and synthetic materials are both used as scaffold materials for auricular reconstruction, each with advantages and disadvantages. Methods for achieving skin and soft tissue scaffold coverage have been developed to include nonexpansion and expansion techniques. In recent years, the application of digital auxiliary technology such as finite element analysis has helped optimize surgical outcomes and reduce complications. Tissue-engineered cartilage scaffolds and 3-dimensional bioprinting technology have rapidly advanced in the field of ear reconstruction. This article discusses the prevalence and classification of microtia, the selection of auricular scaffolds, the evolution of surgical methods, and the current applications of digital auxiliary technology in ear reconstruction, with the aim of providing clinical physicians with a reference for individualized ear reconstruction surgery. The focus of this work is on the current applications and challenges of tissue engineering and 3-dimensional bioprinting technology in the field of ear reconstruction, as well as future prospects.

Surgical Phase Duration in Robot-Assisted Partial Nephrectomy: A Surgical Data Science Exploration for Clinical Relevance

(1) Background: Surgical phases form the basic building blocks for surgical skill assessment, feedback, and teaching. The phase duration itself and its correlation with clinical parameters at diagnosis have not yet been investigated. Novel commercial platforms provide phase indications but have not been assessed for accuracy yet. (2) Methods: We assessed 100 robot-assisted partial nephrectomy videos for phase durations based on previously defined proficiency metrics. We developed an annotation framework and subsequently compared our annotations to an existing commercial solution (Touch Surgery, Medtronic™). We subsequently explored clinical correlations between phase durations and parameters derived from diagnosis and treatment. (3) Results: An objective and uniform phase assessment requires precise definitions derived from an iterative revision process. A comparison to a commercial solution shows large differences in definitions across phases. BMI and the duration of renal tumor identification are positively correlated, as are tumor complexity and both tumor excision and renorrhaphy duration. (4) Conclusions: The surgical phase duration can be correlated with certain clinical outcomes. Further research should investigate whether the retrieved correlations are also clinically meaningful. This requires an increase in dataset sizes and facilitation through intelligent computer vision algorithms. Commercial platforms can facilitate this dataset expansion and help unlock the full potential, provided that the phase annotation details are disclosed.

A novel high accuracy model for automatic surgical workflow recognition using artificial intelligence in laparoscopic totally extraperitoneal inguinal hernia repair (TEP)

INTRODUCTION

Artificial intelligence and computer vision are revolutionizing the way we perceive video analysis in minimally invasive surgery. This emerging technology has increasingly been leveraged successfully for video segmentation, documentation, education, and formative assessment. New, sophisticated platforms allow pre-determined segments chosen by surgeons to be automatically presented without the need to review entire videos. This study aimed to validate and demonstrate the accuracy of the first reported AI-based computer vision algorithm that automatically recognizes surgical steps in videos of totally extraperitoneal (TEP) inguinal hernia repair.

METHODS

Videos of TEP procedures were manually labeled by a team of annotators trained to identify and label surgical workflow according to six major steps. For bilateral hernias, an additional change of focus step was also included. The videos were then used to train a computer vision AI algorithm. Performance accuracy was assessed in comparison to the manual annotations.

RESULTS

A total of 619 full-length TEP videos were analyzed: 371 were used to train the model, 93 for internal validation, and the remaining 155 as a test set to evaluate algorithm accuracy. The overall accuracy for the complete procedure was 88.8%. Per-step accuracy reached the highest value for the hernia sac reduction step (94.3%) and the lowest for the preperitoneal dissection step (72.2%).

CONCLUSIONS

These results indicate that the novel AI model was able to provide fully automated video analysis with a high accuracy level. High-accuracy models leveraging AI to enable automation of surgical video analysis allow us to identify and monitor surgical performance, providing mathematical metrics that can be stored, evaluated, and compared. As such, the proposed model is capable of enabling data-driven insights to improve surgical quality and demonstrate best practices in TEP procedures.