Artificial intelligence assisted operative anatomy recognition in endoscopic pituitary surgery.

Pituitary tumours are surrounded by critical neurovascular structures and identification of these intra-operatively can be challenging. We have previously developed an AI model capable of sellar anatomy segmentation. This study aims to apply this model, and explore the impact of AI-assistance on clinician anatomy recognition.

Artificial Intelligence Assisted Surgical Scene Recognition: A Comparative Study Amongst Healthcare Professionals.

Objective: This study aimed to compare the ability of a deep-learning platform (the MACSSwin-T model) with healthcare professionals in detecting cerebral aneurysms from operative videos. Secondly, we aimed to compare the neurosurgical team's ability to detect cerebral aneurysms with and without AI-assistance.

Background: Modern microscopic surgery enables the capture of operative video data on an unforeseen scale.

Diagnosing Necrotizing Enterocolitis via Fine-Grained Visual Classification.

Necrotizing Enterocolitis (NEC) is a devastating condition affecting prematurely born neonates. Reviewing Abdominal X-rays (AXRs) is a key step in NEC diagnosis, staging and treatment decision-making, but poses significant challenges due to the subtle, difficult-to-identify radiological signs of the disease. In this paper, we propose AIDNEC - AI Diagnosis of NECrotizing enterocolitis, a deep learning method to automatically detect and stratify the severity (surgical or medical) of NEC from no pathology in AXRs.

M-VAAL: Multimodal Variational Adversarial Active Learning for Downstream Medical Image Analysis Tasks.

Acquiring properly annotated data is expensive in the medical field as it requires experts, time-consuming protocols, and rigorous validation. Active learning attempts to minimize the need for large annotated samples by actively sampling the most informative examples for annotation. These examples contribute significantly to improving the performance of supervised machine learning models, and thus, active learning can play an essential role in selecting the most appropriate information in deep learning-based diagnosis, clinical assessments, and treatment planning.

Video-Based Performance Analysis in Pituitary Surgery - Part 2: Artificial Intelligence Assisted Surgical Coaching.

Background: Superior surgical skill improves surgical outcomes in endoscopic pituitary adenoma surgery. Video-based coaching programs, pioneered in professional sports, have shown promise in surgical training. In this study, we developed and assessed a video-based coaching program using artificial intelligence (AI) assistance.

Video-Based Performance Analysis in Pituitary Surgery-Part 1: Surgical Outcomes.

Background: Endoscopic pituitary adenoma surgery has a steep learning curve, with varying surgical techniques and outcomes across centers. In other surgeries, superior performance is linked with superior surgical outcomes. This study aimed to explore the prediction of patient-specific outcomes using surgical video analysis in pituitary surgery.

Guided image generation for improved surgical image segmentation.

The lack of large datasets and high-quality annotated data often limits the development of accurate and robust machine-learning models within the medical and surgical domains. In the machine learning community, generative models have recently demonstrated that it is possible to produce novel and diverse synthetic images that closely resemble reality while controlling their content with various types of annotations. However, generative models have not been yet fully explored in the surgical domain, partially due to the lack of large datasets and due to specific challenges present in the surgical domain such as the large anatomical diversity.

MonoLoT: Self-Supervised Monocular Depth Estimation in Low-Texture Scenes for Automatic Robotic Endoscopy.

The self-supervised monocular depth estimation framework is well-suited for medical images that lack ground-truth depth, such as those from digestive endoscopes, facilitating navigation and 3D reconstruction in the gastrointestinal tract. However, this framework faces several limitations, including poor performance in low-texture environments, limited generalisation to real-world datasets, and unclear applicability in downstream tasks like visual servoing. To tackle these challenges, we propose MonoLoT, a self-supervised monocular depth estimation framework featuring two key innovations: point matching loss and batch image shuffle.

Hierarchical segmentation of surgical scenes in laparoscopy.

Purpose: Segmentation of surgical scenes may provide valuable information for real-time guidance and post-operative analysis. However, in some surgical video frames there is unavoidable ambiguity, leading to incorrect predictions of class or missed detections. In this work, we propose a novel method that alleviates this problem by introducing a hierarchy and associated hierarchical inference scheme that allows broad anatomical structures to be predicted when fine-grained structures cannot be reliably distinguished.

Ultrasound Plane Pose Regression: Assessing Generalized Pose Coordinates in the Fetal Brain.

In obstetric ultrasound (US) scanning, the learner's ability to mentally build a three-dimensional (3D) map of the fetus from a two-dimensional (2D) US image represents a significant challenge in skill acquisition. We aim to build a US plane localization system for 3D visualization, training, and guidance without integrating additional sensors. This work builds on top of our previous work, which predicts the six-dimensional (6D) pose of arbitrarily oriented US planes slicing the fetal brain with respect to a normalized reference frame using a convolutional neural network (CNN) regression network.

SimCol3D - 3D reconstruction during colonoscopy challenge.

Colorectal cancer is one of the most common cancers in the world. While colonoscopy is an effective screening technique, navigating an endoscope through the colon to detect polyps is challenging. A 3D map of the observed surfaces could enhance the identification of unscreened colon tissue and serve as a training platform.

DIPO: Differentiable Parallel Operation Blocks for Surgical Neural Architecture Search.

Deep learning has been used across a large number of computer vision tasks, however designing the network architectures for each task is time consuming. Neural Architecture Search (NAS) promises to automatically build neural networks, optimised for the given task and dataset. However, most NAS methods are constrained to a specific macro-architecture design which makes it hard to apply to different tasks (classification, detection, segmentation).

Autonomous control of an ultrasound probe for intra-operative ultrasonography using vision-based shape sensing of pneumatically attachable flexible rails.

Purpose: In robotic-assisted minimally invasive surgery, surgeons often use intra-operative ultrasound to visualise endophytic structures and localise resection margins. This must be performed by a highly skilled surgeon. Automating this subtask may reduce the cognitive load for the surgeon and improve patient outcomes.

Artificial intelligence in histopathological image analysis of central nervous system tumours: A systematic review.

The convergence of digital pathology and artificial intelligence could assist histopathology image analysis by providing tools for rapid, automated morphological analysis. This systematic review explores the use of artificial intelligence for histopathological image analysis of digitised central nervous system (CNS) tumour slides. Comprehensive searches were conducted across EMBASE, Medline and the Cochrane Library up to June 2023 using relevant keywords.

An investigation into augmentation and preprocessing for optimising X-ray classification in limited datasets: a case study on necrotising enterocolitis.

Purpose: Obtaining large volumes of medical images, required for deep learning development, can be challenging in rare pathologies. Image augmentation and preprocessing offer viable solutions. This work explores the case of necrotising enterocolitis (NEC), a rare but life-threatening condition affecting premature neonates, with challenging radiological diagnosis.

Objective assessment tools in laparoscopic or robotic-assisted gynecological surgery: A systematic review.

Introduction: There is a growing emphasis on proficiency-based progression within surgical training. To enable this, clearly defined metrics for those newly acquired surgical skills are needed. These can be formulated in objective assessment tools.

PitSurgRT: real-time localization of critical anatomical structures in endoscopic pituitary surgery.

Purpose: Endoscopic pituitary surgery entails navigating through the nasal cavity and sphenoid sinus to access the sella using an endoscope. This procedure is intricate due to the proximity of crucial anatomical structures (e.g.

Improving risk communication: a proof-of-concept randomised control trial assessing the impact of visual aids for neurosurgical consent.

Introduction: Informed consent is a fundamental component in the work-up for surgical procedures. Statistical risk information pertaining to a procedure is by nature probabilistic and challenging to communicate, especially to those with poor numerical literacy. Visual aids and audio/video tools have previously been shown to improve patients' understanding of statistical information.

The IDEAL framework for surgical robotics: development, comparative evaluation and long-term monitoring.

The next generation of surgical robotics is poised to disrupt healthcare systems worldwide, requiring new frameworks for evaluation. However, evaluation during a surgical robot's development is challenging due to their complex evolving nature, potential for wider system disruption and integration with complementary technologies like artificial intelligence. Comparative clinical studies require attention to intervention context, learning curves and standardized outcomes.