Purpose
Perioperative staff shortages are a problem in hospitals worldwide. Keeping the staff content and motivated is a challenge in the busy hospital setting of today. New operating room technologies aim to increase safety and efficiency. This causes a shift from interaction with patients to interaction with technology. Objectively measuring this shift could aid the design of supportive technological products, or optimal planning for high-tech procedures.

Methods
35 Gynaecological procedures of three different technology levels are recorded: open- (OS), minimally invasive- (MIS) and robot-assisted (RAS) surgery. We annotate interaction between staff and the patient. An algorithm is proposed that detects interaction with the operating table from staff posture and movement. Interaction is expressed as a percentage of total working time.

Results
The proposed algorithm measures operating table interactions of 70.4%, 70.3% and 30.1% during OS, MIS and RAS. Annotations yield patient interaction percentages of 37.6%, 38.3% and 24.6%. Algorithm measurements over time show operating table and patient interaction peaks at anomalous events or workflow phase transitions.

Conclusions
The annotations show less operating table and patient interactions during RAS than OS and MIS. Annotated patient interaction and measured operating table interaction show similar differences between procedures and workflow phases. The visual complexity of operating rooms complicates pose tracking, deteriorating the algorithm input quality. The proposed algorithm shows promise as a component in context-aware event- or workflow phase detection.

Workflow insights can improve efficiency and safety in the Cardiac Catheterization Laboratory (Cath Lab). As manual analysis is labor-intensive, we aim for automation through camera monitoring. Literature shows that human poses are indicative of activities and therefore workflow. As a first exploration, we evaluate how marker-less multi-human pose estimators perform in the Cath Lab. We annotated poses in 2040 frames from ten multi-view coronary angiogram (CAG) recordings. Pose estimators AlphaPose, OpenPifPaf and OpenPose were run on the footage. Detection and tracking were evaluated separately for the Head, Arms, and Legs with Average Precision (AP), head-guided Percentage of Correct Keypoints (PCKh), Association Accuracy (AA), and Higher-Order Tracking Accuracy (HOTA). We give qualitative examples of results for situations common in the Cath Lab, with reflections in the monitor or occlusion of personnel. AlphaPose performed best on most mean Full-pose metrics with an AP from 0.56 to 0.82, AA from 0.55 to 0.71, and HOTA from 0.58 to 0.73. On PCKh OpenPifPaf scored highest, from 0.53 to 0.64. Arms, Legs, and the Head were detected best in that order, from the views which see the least occlusion. During tracking in the Cath Lab, AlphaPose tended to swap identities and OpenPifPaf merged different individuals. Results suggest that AlphaPose yields the most accurate confidence scores and limbs, and OpenPifPaf more accurate keypoint locations in the Cath Lab. Occlusions and reflection complicate pose tracking. The AP of up to 0.82 suggests that AlphaPose is a suitable pose detector for workflow analysis in the Cath Lab, whereas its HOTA of up to 0.73 here calls for another tracking solution.

Workflow insights can enable safety- and efficiency improvements in the Cardiac Catheterisation Laboratory (Cath Lab). Human pose tracklets from video footage can provide a source of workflow information. However, occlusions and visual similarity between personnel make the Cath Lab a challenging environment for the re-identification of individuals. We propose a human pose tracker that addresses these problems specifically, and test it on recordings of real coronary angiograms. This tracker uses no visual information for re-identification, and instead employs object keypoint similarity between detections and predictions from a third-order motion model. Algorithm performance is measured on Cath Lab footage using Higher-Order Tracking Accuracy (HOTA). To evaluate its stability during procedures, this is done separately for five different surgical steps of the procedure. We achieve up to 0.71 HOTA where tested state-of-the-art pose trackers score up to 0.65 on the used dataset. We observe that the pose tracker HOTA performance varies with up to 10 percentage point (Image 1) between workflow phases, where tested state-of-the-art trackers show differences of up to Image 2. In addition, the tracker achieves up to 22.5 frames per second, which is 9 frames per second faster than the current state-of-the-art on our setup in the Cath Lab. The fast and consistent short-term performance of the provided algorithm makes it suitable for use in workflow analysis in the Cath Lab and opens the door to real-time use-cases.

This study evaluates the performance of deep learning models in the prediction of the end time of procedures performed in the cardiac catheterization laboratory (cath lab). We employed only the clinical phases derived from video analysis as input to the algorithms. Our results show that InceptionTime and LSTM-FCN yielded the most accurate predictions. InceptionTime achieves Mean Absolute Error (MAE) values below 5 min and Symmetric Mean Absolute Percentage Error (SMAPE) under 6% at 60-s sampling intervals. In contrast, LSTM with attention mechanism and standard LSTM models have higher error rates, indicating challenges in handling both long-term and short-term dependencies. CNN-based models, especially InceptionTime, excel at feature extraction across different scales, making them effective for time-series predictions. We also analyzed training and testing times. CNN models, despite higher computational costs, significantly reduce prediction errors. The Transformer model has the fastest inference time, making it ideal for real-time applications. An ensemble model derived by averaging the two best performing algorithms reported low MAE and SMAPE, although needing longer training. Future research should validate these findings across different procedural contexts and explore ways to optimize training times without losing accuracy. Integrating these models into clinical scheduling systems could improve efficiency in cath labs. Our research demonstrates that the models we implemented can form the basis of an automated tool, which predicts the optimal time to call the next patient with an average error of approximately 30 s. These findings show the effectiveness of deep learning models, especially CNN-based architectures, in accurately predicting procedure end times.

Objective: Detecting the cancerous growth margin and achieving a negative margin is one of the challenges that surgeons face during cancer procedures. A smart electrosurgical knife with integrated optical fibers has been designed previously to enable real-time use of diffuse reflectance spectroscopy for intraoperative margin assessment. In this paper, the thermal effect of the electrosurgical knife on tissue sensing is investigated. Methods: Porcine tissues and phantoms were used to investigate the performance of the smart electrosurgical knife after electrosurgery. The fat-to-water content ratio (F/W-ratio) served as the discriminative parameter for distinguishing tissues and tissue mimicking phantoms with varying fat content. The F/W-ratio of tissues and phantoms was measured with the smart electrosurgical knife before and after 14 minutes of electrosurgery. Additionally, a layered porcine tissue and phantom were sliced and measured from top to bottom with the smart electrosurgical knife. Results: Mapping the thermal activity of the electrosurgical knife's electrode during animal tissue electrosurgery revealed temperatures exceeding 400 °C. Electrosurgery for 14 minutes had no impact on the device's accurate detection of the F/W-ratio. The smart electrosurgical knife enables real-time tissue detection and predicts the fat content of the next layer from 4 mm ahead. Conclusion: The design of the smart electrosurgical knife outlined in this paper demonstrates its potential utility for tissue detection during electrosurgery. Significance: In the future, the smart electrosurgical knife could be a valuable intraoperative margin assessment tool, aiding surgeons in detecting tumor borders and achieving negative margins.

Background
Evaluate imaging photoplethysmography (iPPG) as a novel noninvasive technique to assess flap perfusion in head and neck free flap reconstructive (FFR) surgeries.

Methods
Intraoperative iPPG was performed in 17 patients undergoing FFR surgery. Imaging consisted of a 30-s video from which perfusion maps were extracted, providing detailed information about blood flow and pulsatility in the flap microvasculature. During each procedure, iPPG acquisitions were acquired representing distinct perfusion conditions of the flap (fully perfused/ischemic/reperfused). When possible, postoperative measurements were performed to assess flap recovery during the critical time period (3 days) and long-term follow-up (30 days).

Results
Perfusion maps, displaying iPPG amplitude and delay times, correlated strongly (p < 0.001) with the perfusion status of the tissue. One case of postoperative thrombosis, leading to flap failure, was identified with iPPG. After surgical revision in this case, flap perfusion was restored and confirmed by iPPG. Postoperative follow-up imaging allowed for objective visualization of flap recovery short term (3 days) and up to 30 days after the surgical procedure.

Conclusions
This study shows that iPPG is suitable for objective and noninvasive assessment of flap perfusion in head and neck FFR surgery. In addition, postoperative monitoring shows potential for assessing flap perfusion in patients with increased risk of postoperative complications.

Our research highlights the potential of Diffuse Reflectance Spectroscopy (DRS) in detecting cortical breaches during pedicle screw placement. We propose a sideways-looking fiber-optic probe, integrating diffuse light emission with both forward and sideways light collection. Experiments on an optical tissue phantom validate the probe’s potential to distinguish bone tissues and provide real-time guidance for spine surgery. Our findings prove that DRS with diffuse emission can detect perpendicular breaches, and demonstrate how the integration of a 45^◦ slanted fiber coated with gold enables parallel breach detection, advancing spine surgery by allowing for accurate pedicle screw placement.

Deep learning-based object detectors, while offering exceptional performance, are data-dependent and can suffer from generalization issues. In this work, we investigated deep neural networks for detecting people and medical instruments for the vision-based workflow analysis system inside Catheterization Laboratories (Cath Labs). The central problem explored in this paper is the fact that the performance of the detector can degrade drastically if it is trained and tested on data from different Cath Labs. Our research aimed to investigate the underlying causes of this specific performance degradation and find solutions to mitigate this issue. We employed the YOLOv8 object detector and created datasets from clinical procedures recorded at Reinier de Graaf Hospital (RdGG) and Philips Best Campus, supplemented with publicly accessible images. Through a series of experiments complemented by data visualization, we discovered that the performance degradation primarily stems from data distribution shifts in the feature space. Notably, the object detector trained on non-sensitive online images can generalize to unseen Cath Labs, outperforming the model trained on a procedure recording from a different Cath Lab. The detector trained on the online images achieved an mAP@0.5 of 0.517 on the RdGG dataset. Furthermore, by switching to the most suitable camera for each object in the Cath Lab, the multi-camera system can further improve the detection performance significantly. An aggregated L-camera mAP@0.5 of 0.679 is achieved for single-object classes on the RdGG dataset.

The demographic shift has increased the demand for surgical interventions to address age-related degenerative diseases, such as spinal fusion. Accurate placement of pedicle screws, crucial for successful spinal fusion, varies widely with physician experience. Integrating tissue sensing into spine surgical instruments allows intraoperative examination of tissue properties, providing surgeons with additional information to prevent screw misplacement. This paper introduces a handheld fiber-optic tissue sensing device for real-time bone tissue differentiation during spine surgery using Diffuse Reflectance Spectroscopy (DRS). Our prototype employs laser diodes at two distinct wavelengths for tissue illumination, eliminating the need for a spectrometer and enabling direct light collection with a photodiode. The device includes a printed circuit board (PCB) with driver circuits that are adjustable for varying laser diode output power, and signal amplification to convert the photodiode current to a measurable voltage signal. Controlled by a microcontroller, the device computes a reflectance ratio from both laser diode signals to provide real-time audio feedback to surgeons across various healthcare settings. Despite challenges in coupling efficiencies from manual fiber-coupling of the diodes, our prototype is able to emit and collect light to distinguish bone tissues with DRS, demonstrating feasibility. It is compact, made of low-cost and readily available components, and offers fast, real-time feedback, thus serving as a successful proof-of-concept for enhancing surgical accuracy during spinal fusion procedures.

Diffuse Reflectance Spectroscopy (DRS) can provide tissue feedback for pedicle screw placement in spine surgery, yet the integration of fiber optics into the tip of the pedicle probe, a device used to pierce through bone, is challenging, since the optical probing depth and signal-to-noise ratio (SNR) are affected negatively compared to those of a blunt DRS probe. Through Monte Carlo simulations and optical phantom experiments, we show how differences in the shape of the instrument tip influence the acquired spectrum. Our findings demonstrate that a single bevel with an angle of 30∘ offers a solution to anticipate cortical breaches during pedicle screw placement. Compared to a blunt probe, the optical probing depth and SNR of a cone tip are reduced by 50%. The single bevel tip excels with 75% of the optical probing depth and a SNR remaining at approximately ⅔, facilitating the construction of a surgical instrument with integrated DRS.

Accuracy in spinal fusion varies greatly depending on the experience of the physician. Real-time tissue feedback with diffuse reflectance spectroscopy has been shown to provide cortical breach detection using a conventional probe with two parallel fibers. In this study, Monte Carlo simulations and optical phantom experiments were conducted to investigate how angulation of the emitting fiber affects the probed volume to allow for the detection of acute breaches. Difference in intensity magnitude between cancellous and cortical spectra increased with the fiber angle, suggesting that outward angulated fibers are beneficial in acute breach scenarios. Proximity to the cortical bone could be detected best with fibers angulated at θf = 45° for impending breaches between θp = 0° and θp = 45°. An orthopedic surgical device comprising a third fiber perpendicular to the device axis could thus cover the full impending breach range from θp = 0° to θp = 90°.

Introduction: Anastomotic leakage after gastrointestinal surgery has a high impact on patient's quality of life and its origin is associated with inadequate perfusion. Imaging photoplethysmography (iPPG) is a noninvasive imaging technique that measures blood-volume changes in the microvascular tissue bed and detects changes in tissue perfusion. Materials and methods: Intraoperative iPPG imaging was performed in 29 patients undergoing an open segment resection of the small intestine or colon. During each surgery, imaging was performed on fully perfused (true positives) and ischemic intestines (true negatives) and the anastomosis (unknowns). Imaging consisted of a 30-s video from which perfusion maps were extracted, providing detailed information about blood flow within the intestine microvasculature. To detect the predictive capabilities of iPPG, true positive and true negative perfusion conditions were used to develop two different perfusion classification methods. Results: iPPG-derived perfusion parameters were highly correlated with perfusion—perfused or ischemic—in intestinal tissues. A perfusion confidence map distinguished perfused and ischemic intestinal tissues with 96% sensitivity and 86% specificity. Anastomosis images were scored as adequately perfused in 86% of cases and 14% inconclusive. The cubic-Support Vector Machine achieved 90.9% accuracy and an area under the curve of 96%. No anastomosis-related postoperative complications were encountered in this study. Conclusions: This study shows that noninvasive intraoperative iPPG is suitable for the objective assessment of small intestine and colon anastomotic perfusion. In addition, two perfusion classification methods were developed, providing the first step in an intestinal perfusion prediction model.

In this paper, we aim to design an automatic camera pose estimation pipeline for clinical spaces such as catheterization laboratories. Our proposed pipeline exploits Scaled-YOLOv4 to detect fixed objects. We adopt the self-supervised key-point detector SuperPoint in combination with SuperGlue, a keypoint matching technique based on graph neural networks. Thus, we match key-points on input images with annotated reference points. Reference points are chosen on fixed objects in the scene, such as corners of door posts or windows. The point-correspondences between the image coordinates and the 3D coordinates are applied to the Perspective-n-Point algorithm to estimate the pose of each camera. Compared with other camera pose estimation methods, the proposed pipeline does not require the construction of 3D point-cloud model of the scene or placing a polyhedron object in the scene before each required calibration. Using videos from real procedures, we show that the pipeline can estimate the camera pose with high accuracy.

Introduction: Disposable instruments in healthcare have led to a significant increase of medical waste. The aim of this study is to validate the recycling of disposable Zamak laryngoscope blades into new medical products by using a new ‘all-in-one’ affordable reprocessing setup as alternative for die-casting. Methods: An “all-in-one” casting set-up was designed and built. Laryngoscope blades, recovered from two hospitals, were disinfected, melted and casted into dog-bones and into new instrument parts. The quality of the casted material was evaluated using X-ray fluorescence spectrometry. The mechanical properties were obtained by assessing the Ultimate Tensile Strength (UTS) and tensile tests. Results: A recovery of 93% Zamak was obtained using a melting temperature of 420 0C for three hours. The XRF Spectro data showed higher Zinc and silicon concentrations when compared with Virgin Zamak. The dog-bones tests resulted in an average UTS, Yield Strength (YS) and Young’s Modulus (YM) of 236 ±61 (MPa), 70 ±43 and 9 ±3, respectively, representing 82%, 103% and 64% of the UTS, YS and YM of standard Zamak. Functional instrument parts with extensions and inner chambers were casted with a maximal shrinkage percentage of 1±1%. Discussion: This study demonstrates that the created “all-in-one” reprocessing method can process contaminated disposable Zamak laryngoscope blades into new raw base material and new instrument parts. Although material and surface properties can deteriorate, reprocessed Zamak still has sufficient mechanical properties and can be used to cast complex parts with sufficient dimensional tolerances and minimal shrinkage. Conclusion: A circular micro reprocessing method was designed and used to turn disposed laryngoscope blades into new basis material and semi-finished products. Follow up studies are needed to scale and optimize this process towards a functional alternative for die casting. It should be further investigated how this process can contribute to further medical waste reduction and a circular healthcare economy.

Surgical excision is the golden standard for treatment of intestinal tumors. In this surgical procedure, inadequate perfusion of the anastomosis can lead to postoperative complications, such as anastomotic leakages. Imaging photoplethysmography (iPPG) can potentially provide objective and real-time feedback of the perfusion status of tissues. This feasibility study aims to evaluate an iPPG acquisition system during intestinal surgeries to detect the perfusion levels of the microvasculature tissue bed in different perfusion conditions. This feasibility study assesses three patients that underwent resection of a portion of the small intestine. Data was acquired from fully perfused, non-perfused and anastomosis parts of the intestine during different phases of the surgical procedure. Strategies for limiting motion and noise during acquisition were implemented. iPPG perfusion maps were successfully extracted from the intestine microvasculature, demonstrating that iPPG can be successfully used for detecting perturbations and perfusion changes in intestinal tissues during surgery. This study provides proof of concept for iPPG to detect changes in organ perfusion levels.

Background: Neurosurgical procedures are complex and require years of training and experience. Traditional training on human cadavers is expensive, requires facilities and planning, and raises ethical concerns. Therefore, the use of anthropomorphic phantoms could be an excellent substitute. The aim of the study was to design and develop a patient-specific 3D-skull and brain model with realistic CT-attenuation suitable for conventional and augmented reality (AR)-navigated neurosurgical simulations. Methods: The radiodensity of materials considered for the skull and brain phantoms were investigated using cone beam CT (CBCT) and compared to the radiodensities of the human skull and brain. The mechanical properties of the materials considered were tested in the laboratory and subsequently evaluated by clinically active neurosurgeons. Optimization of the phantom for the intended purposes was performed in a feedback cycle of tests and improvements. Results: The skull, including a complete representation of the nasal cavity and skull base, was 3D printed using polylactic acid with calcium carbonate. The brain was cast using a mixture of water and coolant, with 4 wt% polyvinyl alcohol and 0.1 wt% barium sulfate, in a mold obtained from segmentation of CBCT and T1 weighted MR images from a cadaver. The experiments revealed that the radiodensities of the skull and brain phantoms were 547 and 38 Hounsfield units (HU), as compared to real skull bone and brain tissues with values of around 1300 and 30 HU, respectively. As for the mechanical properties testing, the brain phantom exhibited a similar elasticity to real brain tissue. The phantom was subsequently evaluated by neurosurgeons in simulations of endonasal skull-base surgery, brain biopsies, and external ventricular drain (EVD) placement and found to fulfill the requirements of a surgical phantom. Conclusions: A realistic and CT-compatible anthropomorphic head phantom was designed and successfully used for simulated augmented reality-led neurosurgical procedures. The anatomic details of the skull base and brain were realistically reproduced. This phantom can easily be manufactured and used for surgical training at a low cost.

Introduction: Steering light is relevant to many medical applications that require tissue illumination, sensing, or modification. To control the propagation direction of light beams, a great variety of innovative fiber-optic medical devices have been designed. Areas covered: This review provides a comprehensive overview of the patent literature on light beam control in fiber-optic medical devices. The Web of Science Derwent Innovation Index database was scanned, and 81 patents on fiber-optic devices published in the last 20 years (2001–2021) were retrieved and categorized based on the working principle to steer light (refraction/reflection, scattering, diffraction) and the design strategy that was employed (within fiber, at fiber end, outside fiber). Expert opinion: Patents describing medical devices were found for all categories, except for generating diffraction at the fiber end surface. The insight in the different designs reveals that there are still several opportunities to design innovative devices that can collect light at an angle off-axis, reduce the angular distribution of light, or split light into multiple beams.

Emerging intraoperative tumor margin assessment techniques require the development of more complex and reliable organ phantoms to assess the performance of the technique before its translation into the clinic. In this work, electrically conductive tissue-mimicking materials (TMMs) based on fat, water and agar/gelatin were produced with tunable optical properties. The composition of the phantoms allowed for the assessment of tumor margins using diffuse reflectance spectroscopy, as the fat/water ratio served as a discriminating factor between the healthy and malignant tissue. Moreover, the possibility of using polyvinyl alcohol (PVA) or transglutaminase in combination with fat, water and gelatin for developing TMMs was studied. The diffuse spectral response of the developed phantom materials had a good match with the spectral response of porcine muscle and adipose tissue, as well as in vitro human breast tissue. Using the developed recipe, anatomically relevant heterogeneous breast phantoms representing the optical properties of different layers of the human breast were fabricated using 3D-printed molds. These TMMs can be used for further development of phantoms applicable for simulating the realistic breast conserving surgery workflow in order to evaluate the intraoperative optical-based tumor margin assessment techniques during electrosurgery.

The most important prognostic factor for the survival of advanced-stage epithelial ovarian cancer (EOC) is the completeness of cytoreductive surgery (CRS). Therefore, an intraoperative technique to detect microscopic tumors would be of great value. The aim of this pilot study is to assess the feasibility of near-infrared hyperspectral imaging (HSI) for EOC detection in ex vivo tissue samples. Images were collected during CRS in 11 patients in the wavelength range of 665–975 nm, and processed by calibration, normalization, and noise filtering. A linear support vector machine (SVM) was employed to classify healthy and tumorous tissue (defined as >50% tumor cells). Classifier performance was evaluated using leave-one-out cross-validation. Images of 26 tissue samples from 10 patients were included, containing 26,446 data points that were matched to histopathology. Tumorous tissue could be classified with an area under the curve of 0.83, a sensitivity of 0.81, a specificity of 0.70, and Matthew’s correlation coefficient of 0.41. This study paves the way to in vivo and intraoperative use of HSI during CRS. Hyperspectral imaging can scan a whole tissue surface in a fast and non-contact way. Our pilot study demonstrates that HSI and SVM learning can be used to discriminate EOC from surrounding tissue.

During oncological surgery, it can be challenging to identify the tumor and establish adequate resection margins. This study proposes a new two-layer approach in which diffuse reflectance spectroscopy (DRS) is used to predict the top layer thickness and classify the layers in two-layered phantom and animal tissue. Using wavelet-based and peak-based DRS spectral features, the proposed method could predict the top layer thickness with an accuracy of up to 0.35 mm. In addition, the tissue types of the first and second layers were classified with an accuracy of 0.95 and 0.99. Distinguishing multiple tissue layers during spectral analyses results in a better understanding of more complex tissue structures encountered in surgical practice.