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.

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.

Background
The operating room (OR) department is one of the most energy-intensive departments of a hospital. The majority of ORs in the Netherlands have an air-handling installation with an ultra-clean ventilation system. However, not all surgeries require an ultra-clean OR.

Aim
To determine the effect of reducing the air change rate on the ventilation effectiveness in ultra-clean ORs.

Methods
Lower air volume ventilation effectiveness (VELv) of conventional ventilation (CV), controlled dilution ventilation (cDV), temperature-controlled airflow (TcAF) and unidirectional airflow (UDAF) systems were evaluated within a 4 × 4 m measuring grid of 1 × 1 m. The VELv was defined as the recovery degree (RD), cleanliness recovery rate (CRR) and air change effectiveness (ACE).

Findings
The CV, cDVLv and TcAFLv ventilation systems showed a comparable mixing character in all areas (A, B and AB) when reducing the air change rate to 20/h. Ventilation effectiveness decreased when the air change rate was reduced, with the exception of the ACE.
At all points for the UDAF-2Lv and at the centre point (C3) of the TcAFLv, higher RD10Lv and CRRLv were measured when compared with the other examined ventilation systems.

Conclusions
The ventilation effectiveness decreased when an ultra-clean OR with an ultra-clean ventilation air-supply system was switched to an air change rate of 20/h. Reducing the air change rate in the OR from an ultra-clean OR to a generic OR will reduce the recovery degree (RD10) by a factor of 10–100 and the local air change rate (CRR) by between 42% and 81%.

Background: Surgical instrument counting is a manual, attention-intensive task of the operating room (OR) nurse. Many labour-saving technologies have been proposed, but implementation remains challenging. Knowledge of current counting methods and staff preferences could guide future developments towards effective application. Approach: We observed OR nurses counting materials and instruments in 50 surgical procedures performed by various surgical specialties in a regional teaching hospital in Delft, The Netherlands. Additionally, we surveyed them on their preferences concerning the methods of counting. Key findings: Variations in approaches of surgical counting were observed, with OR nurses using multiple strategies and counting techniques to manage disruptions and limit workload. Interest in using supportive technology is limited to the preoperative and postoperative phase. Relevance: This research relates observational data to staff preferences. Our findings may guide future developments of labour-saving innovations regarding surgical counting towards developing more effective applications and to ensure successful implementation.

Introduction: This study focuses on the quantification of and current guidelines on the hazards related to needle positioning in prostate cancer treatment: (1) access restrictions to the prostate gland by the pubic arch, so-called Pubic Arch Interference (PAI) and (2) needle positioning errors. Next, we propose solution strategies to mitigate these hazards. Methods: The literature search was executed in the Embase, Medline ALL, Web of Science Core Collection*, and Cochrane Central Register of Controlled Trials databases. Results: The literature search resulted in 50 included articles. PAI was reported in patients with various prostate volumes. The level of reported PAI varied between 0 and 22.3 mm, depending on the patient’s position and the measuring method. Low-Dose-Rate Brachytherapy induced the largest reported misplacement errors, especially in the cranio-caudal direction (up to 10 mm) and the largest displacement errors were reported for High-Dose-Rate Brachytherapy in the cranio-caudal direction (up to 47 mm), generally increasing over time. Conclusions: Current clinical guidelines related to prostate volume, needle positioning accuracy, and maximum allowable PAI are ambiguous, and compliance in the clinical setting differs between institutions. Solutions, such as steerable needles, assist in mitigating the hazards and potentially allow the physician to proceed with the procedure. This systematic review was performed in accordance with the PRISMA guidelines. The review was registered at Protocols.io (DOI: dx.doi.org/10.17504/protocols.io.6qpvr89eplmk/v1).

In emergencies of suffocating patients, doctors typically perform an incision made through the skin and cricothyroid membrane to clear the patient’s airway. This type of procedure is called coniotomy. To master this procedure, doctors must undergo simulated practical training, sometimes under high stress. Current practical sessions often include models to simulate coniotomy that consists of the skin and the larynges with attached tracheae from butchered pigs, which are not always available, are expensive, and might be unsustainable to use. In this work, we present a reusable phantom larynx setup to facilitate a coniotomy procedure that is easier and cheaper. Our proposed setup comprises a 3D-printed polyurethane larynx with silicon rubber to simulate skin and accommodate multiple incisions. With this setup, we aim to provide visitors with an interactive and safe experience by performing the coniotomy procedure using a scalpel on the right spot and placing the ventilating tube.

Background: The objective of an operating room (OR) ultra-clean ventilation system is to eliminate or reduce the quantity of dust particles and colony-forming units per cubic meter of air (CFU/m³). To achieve this, ultra-clean goal high air change rates per hour are required to reduce the particle load and number of CFU/m³. Aim: To determine the air quality in an ultra-clean OR during surgery, in terms of the number and type of microorganism and quantity of dust particles in order to establish a benchmark. Methods: Number of CFUs and the quantity of dust particles were measured. For measuring the CFUs, sterile extraction hoses were positioned at the incision, the furthest away positioned instrument table, and the periphery. At these locations, air was extracted to determine the quantity of dust particles. Findings: The number of CFU/m³ and particles was on average at wound level ≤1 CFU/m³ resp. 852.679 particles, at instrument table ≤1 CFU/m³ resp. 3.797 particles and in the periphery ≤8 CFU/m³, resp. 4.355 particles. Conclusion: The number of CFUs in the ultra-clean area is below the defined ultra-clean level of ≤10 CFU/m³ for ultra-clean surgery. The quantity of dust particles measured during surgery was higher than the defined ISO 5.

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.

Prostate cancer patients with an enlarged prostate and/or excessive pubic arch interference (PAI) are generally considered non-eligible for high-dose-rate (HDR) brachytherapy (BT). Steerable needles have been developed to make these patients eligible again. This study aims to validate the dosimetric impact and performance of steerable needles within the conventional clinical setting. HDR BT treatment plans were generated, needle implantations were performed in a prostate phantom, with prostate volume > 55 cm³ and excessive PAI of 10 mm, and pre- and post-implant dosimetry were compared considering the dosimetric constraints: prostate V₁₀₀ > 95 % (13.50 Gy), urethra D_0.1cm³ < 115 % (15.53 Gy) and rectum D_1cm³ < 75 % (10.13 Gy). The inclusion of steerable needles resulted in a notable enhancement of the dose distribution and prostate V₁₀₀ compared to treatment plans exclusively employing rigid needles to address PAI. Furthermore, the steerable needle plan demonstrated better agreement between pre- and post-implant dosimetry (prostate V₁₀₀: 96.24 % vs. 93.74 %) compared to the rigid needle plans (79.13 % vs. 72.86 % and 87.70 % vs. 81.76 %), with no major changes in the clinical workflow and no changes in the clinical set-up. The steerable needle approach allows for more flexibility in needle positioning, ensuring a highly conformal dose distribution, and hence, HDR BT is a feasible treatment option again for prostate cancer patients with an enlarged prostate and/or excessive PAI.

Conformity of tumour volumes and dose plans in prostate brachytherapy (BT) can be constrained by unwanted needle deflections, needle access restrictions and visualisation limitations. This work validates the feasibility of teleoperated robotic control of an active steerable needle using magnetic resonance (MR) for guidance. With this system, perturbations can be counteracted and critical structures can be circumvented to access currently inaccessible areas. The system comprises of (1) a novel steerable needle, (2) the minimally invasive robotics in an MR environment (MIRIAM) system, and (3) the daVinci Research Kit (dVRK). MR scans provide visual feedback to the operator controlling the dVRK. Needle steering is performed along curved trajectories to avoid the urethra towards targets (representing tumour tissue) in a prostate phantom with a targeting error of 1.2 ± 1.0 mm. This work shows the potential clinical applicability of active needle steering for prostate BT with a teleoperated robotic system in an MR environment.

When making a decision on the operating room air handling installation and type of air supply system, it is relevant to know the expenditures of the different air handling installations and air supply systems. The aim of this study was to determine the capital and operational expenditures of air handling installations equipped with an ultra-clean or with a conventional system. To compare the technical requirements of Dutch air handling installations with European standards and guidelines, and evaluate the costs of surgical site infections in comparison with the capital expenditures. This study fills a gap in knowledge, detailed technical information and costs of air handling installations and air supply systems from multiple completed projects of 24 hospitals were collected, analyzed and compared. Per OR capital expenditures increase by €62,491 to €139,018 when an air handling installation with an ultra-clean system is compared to a conventional system, which is 3%–7% of the total construction costs of a completely new OR department. The yearly increase in operational expenditures per OR with an ultra-clean system compared to a conventional system was €673 to €1,896. The capital and operational expenditures of air handling installations with an ultra-clean system are higher than those with a conventional system. The technical specifications of the ORs studied in the Netherlands correspond to European standards and guidelines. When the impact on patient suffering and costs associated with surgical site infections are weighed against the investment required for an air handling installation with an ultra-clean system, it is worth considering.

Existing challenges in surgical education (See one, do one, teach one) as well as the COVID-19 pandemic make it necessary to develop new ways for surgical training. Therefore, this work describes the implementation of a scalable remote solution called “TeleSTAR” using immersive, interactive and augmented reality elements which enhances surgical training in the operating room. The system uses a full digital surgical microscope in the context of Ear–Nose–Throat surgery. The microscope is equipped with a modular software augmented reality interface consisting an interactive annotation mode to mark anatomical landmarks using a touch device, an experimental intraoperative image-based stereo-spectral algorithm unit to measure anatomical details and highlight tissue characteristics. The new educational tool was evaluated and tested during the broadcast of three live XR-based three-dimensional cochlear implant surgeries. The system was able to scale to five different remote locations in parallel with low latency and offering a separate two-dimensional YouTube stream with a higher latency. In total more than 150 persons were trained including healthcare professionals, biomedical engineers and medical students.

Literature proposes numerous initiatives for optimization of the Operating Room (OR). Despite multiple suggested strategies for the optimization of workflow on the OR, its patients and (medical) staff, no uniform description of ‘optimization’ has been adopted. This makes it difficult to evaluate the proposed optimization strategies. In particular, the metrics used to quantify OR performance are diverse so that assessing the impact of suggested approaches is complex or even impossible. To secure a higher implementation success rate of optimisation strategies in practice we believe OR optimisation and its quantification should be further investigated. We aim to provide an inventory of the metrics and methods used to optimise the OR by the means of a structured literature study. We observe that several aspects of OR performance are unaddressed in literature, and no studies account for possible interactions between metrics of quality and efficiency. We conclude that a systems approach is needed to align metrics across different elements of OR performance, and that the wellbeing of healthcare professionals is underrepresented in current optimisation approaches.

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: We systematically describe the morphology and accessibility of interspinous spaces across age groups of patients. Our primary goal was to objectively estimate if the maneuver space for a virtual spinal needle changes with age. Our secondary goal was to estimate if the optimal site and angle for midline neuraxial puncture change with age. Methods: Measurements were performed in mid-sagittal CT images. The CT images were retrospectively collected from the database of the Department of Radiology of our hospital. Three age groups were studied: 21-30 years (n=36, abbreviated Y(oung)), 51-60 years (n=43, abbreviated M(iddle-aged)) and older than 80 years (n=46, abbreviated Old). A needle trajectory is defined by the chosen puncture point and by the angle at which the needle is directed to its target. We define a Spinal Accessibility Index (SAI) by numerically integrating for an interspace all possible combinations of puncture points and angles that lead to a successful virtual puncture. Successful in this context means that the needle tip reaches the spinal or epidural space without bone contact. Reproducible calculation of the SAI was performed with the help of custom-made software. The larger the value of the SAI, the more possible successful needle trajectories exist that the practitioner may choose from. The optimal puncture point and optimal angle in an age group at a certain level of the spine are defined by the combination of these two, which generates the highest success rate of the entire sample of this age group. Results: At all levels of the spine, the median SAI differed significantly between age groups (independent-samples Kruskal-Wallis test, p<0.001-0.047). The SAI consistently decreased with increasing age. Post-hoc analyses using pairwise comparisons showed a significantly higher SAI in group Y versus Old at all levels (p<0.001 - 0.006) except at level thoracic (Th)1-Th2 (p=0.138). The SAI was significantly higher in group M versus Old at all levels (p<0.001-0.028) except at level Th1-Th2 (p=0.061), Th4-Th5 (p=0.083), Th9-Th10 (p=1.00) and Th10-Th11 (p=1.00). Conclusions: Needle maneuver space in midline neuraxial puncture significantly decreases with progressive age at all levels of the spine. Optimal puncture points and angles are similar between age groups.

Ultra-clean ventilation systems are used in the operating room (OR) to reduce the quantity of airborne bacteria in the ultra-clean area, and reduce the incidence of surgical site infections (SSIs). When the number of colony-forming units (CFUs) in the ultra-clean (protected) area is too high, this is considered a risk factor for SSIs.

Figures 5 and 6 in this article intended to cite the following work: Boulnois, JL. Photophysical processes in recent medical laser developments: A review. Laser Med Sci 1, 47–66 (1986). https://doi.org/10.1007/BF02030737.

Workflow analysis is a young research field that has been gaining traction in recent years. Work in this field aims to improve the efficiency and safety in operating rooms by analysing surgical processes and providing feedback or support, where observations are made and evaluated by algorithms rather than human experts. For our study, we mount five cameras from different angles in a Catheterization Laboratory (CathLab) to observe and analyse Cardiac Angiogram procedures. To automate the classification of workflow and personnel activities, we propose a pipeline that first automates the camera calibration of the 5-camera network then detect locations of medical equipment and track personnel activities...