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Introduction: Patient-specific 3D printed models have been widely used as a complement to 2D imaging for preoperative planning in orthopaedic surgery. This study discusses surgeons’ views on the use of custom-made 3D models for preoperative planning and intraoperative guiding through two prospective surveys. Materials and methods: 70 surgeons were required to rate on a scale of 1–5 the effectiveness of preoperative use of 3D models in 1) improving surgeons’ understanding of patients’ anatomy, 2) aiding in preoperative planning and 3) explaining the condition to others, and 4) portraying the anatomy accurately. 14 surgeons were required to evaluate on the same scale the ease of use of intraoperative jibs, effectiveness of the jigs in improving the ease, accuracy, and outcomes of surgery, and in reducing surgery time. Results and discussion: Over 90 % of surgeons rated 4 and above (agree and strongly agree) for all areas evaluated on preoperative use of 3D models. Surgeons with shorter years of practice post exit gave higher mean ratings for all areas evaluated. Over 80 % surgeons rated 4 and above for all aspects of intraoperative jigs evaluated except for “reducing surgery time”. We opine that these full-scale individualized models allow clear visualization of complex anatomy from various angles and planes, and simulation of specific surgical procedures. When used intra-operatively, these guides facilitate accurate cuts and positioning of implants, therefore improving the ease and outcome of surgery. Conclusion: In conclusion, surgeons have optimistic views on preoperative and intraoperative use of patient specific 3D models.

Background: Neurosurgery demands exceptional precision due to the brain’s complex and delicate structures, necessitating precise targeting of pathological targets. Achieving optimal outcomes depends on the surgeon’s ability to accurately differentiate between healthy and pathological tissues during operations. Raman spectroscopy (RS) has emerged as a promising innovation, offering real-time, in vivo non-invasive biochemical tissue characterization. This literature review evaluates the current research on RS applications in intraoperative neurosurgery, emphasizing its potential to enhance surgical precision and patient outcomes. Methods: Following PRISMA guidelines, a comprehensive systematic review was conducted using PubMed to extract relevant peer-reviewed articles. The inclusion criteria focused on original research discussing real-time RS applications with human tissue samples in or near the operating room, excluding retrospective studies, reviews, non-human research, and other non-relevant publications. Results: Our findings demonstrate that RS significantly improves tumor margin delineation, with handheld devices achieving high sensitivity and specificity. Stimulated Raman Histology (SRH) provides rapid, high-resolution tissue images comparable to traditional histopathology but with reduced time to diagnosis. Additionally, RS shows promise in identifying tumor types and grades, aiding precise surgical decision-making. RS techniques have been particularly beneficial in enhancing the accuracy of glioma surgeries, where distinguishing between tumor and healthy tissue is critical. By providing real-time molecular data, RS aids neurosurgeons in maximizing the extent of resection (EOR) while minimizing damage to normal brain tissue, potentially improving patient outcomes and reducing recurrence rates. Conclusions: This review underscores the transformative potential of RS in neurosurgery, advocating for continued innovation and research to fully realize its benefits. Despite its substantial potential, further research is needed to validate RS’s clinical utility and cost-effectiveness.

IntroductionThe utilisation of artificial intelligence (AI) augments intraoperative safety, surgical training, and patient outcomes. We introduce the term Surgeon-Machine Interface (SMI) to describe this innovative intersection between surgeons and machine inference. A custom deep computer vision (CV) architecture within a sparse labelling paradigm was developed, specifically tailored to conceptualise the SMI. This platform demonstrates the ability to perform instance segmentation on anatomical landmarks and tools from a single open spinal dural arteriovenous fistula (dAVF) surgery video dataset.MethodsOur custom deep convolutional neural network was based on SOLOv2 architecture for precise, instance-level segmentation of surgical video data. Test video consisted of 8520 frames, with sparse labelling of only 133 frames annotated for training. Accuracy and inference time, assessed using F1-score and mean Average Precision (mAP), were compared against current state-of-the-art architectures on a separate test set of 85 additionally annotated frames.ResultsOur SMI demonstrated superior accuracy and computing speed compared to these frameworks. The F1-score and mAP achieved by our platform were 17% and 15.2% respectively, surpassing MaskRCNN (15.2%, 13.9%), YOLOv3 (5.4%, 11.9%), and SOLOv2 (3.1%, 10.4%). Considering detections that exceeded the Intersection over Union threshold of 50%, our platform achieved an impressive F1-score of 44.2% and mAP of 46.3%, outperforming MaskRCNN (41.3%, 43.5%), YOLOv3 (15%, 34.1%), and SOLOv2 (9%, 32.3%). Our platform demonstrated the fastest inference time (88ms), compared to MaskRCNN (90ms), SOLOV2 (100ms), and YOLOv3 (106ms). Finally, the minimal amount of training set demonstrated a good generalisation performance –our architecture successfully identified objects in a frame that were not included in the training or validation frames, indicating its ability to handle out-of-domain scenarios.DiscussionWe present our development of an innovative intraoperative SMI to demonstrate the future promise of advanced CV in the surgical domain. Through successful implementation in a microscopic dAVF surgery, our framework demonstrates superior performance over current state-of-the-art segmentation architectures in intraoperative landmark guidance with high sample efficiency, representing the most advanced AI-enabled surgical inference platform to date. Our future goals include transfer learning paradigms for scaling to additional surgery types, addressing clinical and technical limitations for performing real-time decoding, and ultimate enablement of a real-time neurosurgical guidance platform.

Successful liver cancer resection requires a comprehensive pre- and intraoperative understanding of the spatial relationships between a patient’s cancer and intrahepatic anatomy. The recent literature has highlighted that patient-specific 3D-printed liver models (3DPLMs) reconstructed from medical imaging data may enhance the comprehension of patients’ liver anatomy and thereby provide a useful preoperative planning and intraoperative guidance tool for liver cancer resection (LCR). The purpose of this systematic review was to critically examine the utility and feasibility of 3DPLMs for LCR surgical planning and intraoperative guidance and explore whether these applications improve patient outcomes. Articles were retrieved from four electronic databases (Scopus, Embase, PubMed, and Curtin University Database) according to predetermined eligibility criteria. In total, 22 eligible articles were identified, including 11 original research articles and 11 case reports. Key concepts were synthesised using an inductive content analysis approach suitable for this heterogeneous body of literature. There is significant descriptive and case-report evidence that 3DPLMs strengthen pre- and intraoperative comprehension of patient liver and liver tumour anatomy and can enhance pre- and intraoperative surgical decision making for LCR. The analysis of these studies presents large variances in the times and costs necessary to produce 3DPLMs, as studies did not provide the full expenses of materials, software, and equipment. Production times were focused on different aspects of the 3D printing process and were not comparable. The review nonetheless demonstrates the potential value of 3DPLMs as preoperative planning and intraoperative guidance tools for LCR. Future studies should detail these economic data points to ensure 3DPLMs’ viability. Further experimental research and randomised controlled trials are also necessary to examine the relationship between 3DPLMs and patient’s intra- and postoperative outcomes.

Abstract Background Optical neuronavigation‐guided intracranial surgery has become increasingly common in veterinary medicine, but its use has not yet been described in horses. Objectives To determine the feasibility of optical neuronavigation‐guided intracranial biopsy procedures in the horse, compare the use of the standard fiducial array and anatomic landmarks for patient registration, and evaluate surgeon experience. Animals Six equine cadaver heads. Methods Computed tomography images of each specimen were acquired, with the fiducial array rigidly secured to the frontal bone. Six targets were selected in each specimen. Patient registration was performed separately for 3 targets using the fiducial array, and for 3 targets using anatomic landmarks. In lieu of biopsy, 1 mm diameter wire seeds were placed at each target. Postoperative images were coregistered with the planning scan to calculate Euclidian distance from the tip of the seed to the target. Results No statistical difference between registration techniques was identified. The impact of surgeon experience was examined for each technique using a Mann‐Whitney U test. The experienced surgeon was significantly closer to the intended target (median = 2.52 mm) than were the novice surgeons (median = 6.55 mm) using the fiducial array (P = .001). Although not statistically significant (P = .31), for the experienced surgeon the median distance to target was similar when registering with the fiducial array (2.47 mm) and anatomic landmarks (2.58 mm). Conclusions and Clinical Importance Registration using both fiducial arrays and anatomic landmarks for brain biopsy using optical neuronavigation in horses is feasible.

Hyperspectral imaging (HSI) is a novel optical imaging modality, which has recently found diverse applications in the medical field. HSI is a hybrid imaging modality, combining a digital photographic camera with a spectrographic unit, and it allows for a contactless and non-destructive biochemical analysis of living tissue. HSI provides quantitative and qualitative information of the tissue composition at molecular level in a contrast-free manner, hence making it possible to objectively discriminate between different tissue types and between healthy and pathological tissue. Over the last two decades, HSI has been increasingly used in the medical field, and only recently it has found an application in the operating room. In the last few years, several research groups have used this imaging modality as an intraoperative guidance tool within different surgical disciplines. Despite its great potential, HSI still remains far from being routinely used in the daily surgical practice, since it is still largely unknown to most of the surgical community. The aim of this study is to provide clinical surgeons with an overview of the capabilities, current limitations, and future directions of HSI for intraoperative guidance.