Machine learning-based preoperative analytics for the prediction of anastomotic leakage in colorectal surgery: a swiss pilot study.

BACKGROUND: Anastomotic leakage (AL), a severe complication following colorectal surgery, arises from defects at the anastomosis site. This study evaluates the feasibility of predicting AL using machine learning (ML) algorithms based on preoperative data. METHODS: We retrospectively analyzed data including 21 predictors from patients undergoing colorectal surgery with bowel anastomosis at four Swiss hospitals. Several ML algorithms were applied for binary classification into AL or non-AL groups, utilizing a five-fold cross-validation strategy with a 90% training and 10% validation split. Additionally, a holdout test set from an external hospital was employed to assess the models' robustness in external validation. RESULTS: Among 1244 patients, 112 (9.0%) suffered from AL. The Random Forest model showed an AUC-ROC of 0.78 (SD: ± 0.01) on the internal test set, which significantly decreased to 0.60 (SD: ± 0.05) on the external holdout test set comprising 198 patients, including 7 (3.5%) with AL. Conversely, the Logistic Regression model demonstrated more consistent AUC-ROC values of 0.69 (SD: ± 0.01) on the internal set and 0.61 (SD: ± 0.05) on the external set. Accuracy measures for Random Forest were 0.82 (SD: ± 0.04) internally and 0.87 (SD: ± 0.08) externally, while Logistic Regression achieved accuracies of 0.81 (SD: ± 0.10) and 0.88 (SD: ± 0.15). F1 Scores for Random Forest moved from 0.58 (SD: ± 0.03) internally to 0.51 (SD: ± 0.03) externally, with Logistic Regression maintaining more stable scores of 0.53 (SD: ± 0.04) and 0.51 (SD: ± 0.02). CONCLUSION: In this pilot study, we evaluated ML-based prediction models for AL post-colorectal surgery and identified ten patient-related risk factors associated with AL. Highlighting the need for multicenter data, external validation, and larger sample sizes, our findings emphasize the potential of ML in enhancing surgical outcomes and inform future development of a web-based application for broader clinical use.

Multimodal feedback systems for smart laser osteotomy: Depth control and tissue differentiation.

OBJECTIVES: The study aimed to improve the safety and accuracy of laser osteotomy (bone surgery) by integrating optical feedback systems with an Er:YAG laser. Optical feedback consists of a real-time visual feedback system that monitors and controls the depth of laser-induced cuts and a tissue sensor differentiating tissue types based on their chemical composition. The developed multimodal feedback systems demonstrated the potential to enhance the safety and accuracy of laser surgery. MATERIALS AND METHODS: The proposed method utilizes a laser-induced breakdown spectroscopy (LIBS) system and long-range Bessel-like beam optical coherence tomography (OCT) for tissue-specific laser surgery. The LIBS system detects tissue types by analyzing the plasma generated on the tissue by a nanosecond Nd:YAG laser, while OCT provides real-time monitoring and control of the laser-induced cut depth. The OCT system operates at a wavelength of 1288 ± 30 nm and has an A-scan rate of 104.17 kHz, enabling accurate depth control. Optical shutters are used to facilitate the integration of these multimodal feedback systems. RESULTS: The proposed system was tested on five specimens of pig femur bone to evaluate its functionality. Tissue differentiation and visual depth feedback were used to achieve high precision both on the surface and in-depth. The results showed successful real-time tissue differentiation and visualization without any visible thermal damage or carbonization. The accuracy of the tissue differentiation was evaluated, with a mean absolute error of 330.4 µm and a standard deviation of ±248.9 µm, indicating that bone ablation was typically stopped before reaching the bone marrow. The depth control of the laser cut had a mean accuracy of 65.9 µm with a standard deviation of ±45 µm, demonstrating the system's ability to achieve the pre-planned cutting depth. CONCLUSION: The integrated approach of combining an ablative laser, visual feedback (OCT), and tissue sensor (LIBS) has significant potential for enhancing minimally invasive surgery and warrants further investigation and development.

Towards phase-sensitive optical coherence tomography in smart laser osteotomy: temperature feedback.

Thermal effects during bone surgery pose a common challenge, whether using mechanical tools or lasers. An irrigation system is a standard solution to cool the tissue and reduce collateral thermal damage. In bone surgery using Er:YAG laser, insufficient irrigation raises the risk of thermal damage, while excessive water lowers ablation efficiency. This study investigated the potential of optical coherence tomography to provide feedback by relating the temperature rise with the photo-thermal expansion of the tissue. A phase-sensitive optical coherence tomography system (central wavelength of λ=1.288 µm, a bandwidth of 60.9 nm and a sweep rate of 104.17 kHz) was integrated with an Er:YAG laser using a custom-made dichromatic mirror. Phase calibration was performed by monitoring the temperature changes (thermal camera) and corresponding cumulative phase changes using the phase-sensitive optical coherence tomography system during laser ablation. In this experiment, we used an Er:YAG laser with 230 mJ per pulse at 10 Hz for ablation. Calibration coefficients were determined by fitting the temperature values to phase later and used to predict the temperature rise for subsequent laser ablations. Following the phase calibration step, we used the acquired values to predict the temperature rise of three different laser-induced cuts with the same parameters of the ablative laser. The average root-mean-square error for the three experiments was measured to be around 4 °C. In addition to single-point prediction, we evaluated this method's performance to predict the tissue's two-dimensional temperature rise during laser osteotomy. The findings suggest that the proposed principle could be used in the future to provide temperature feedback for minimally invasive laser osteotomy.

The impact of segmentation on whole-lung functional MRI quantification: Repeatability and reproducibility from multiple human observers and an artificial neural network.

PURPOSE: To investigate the repeatability and reproducibility of lung segmentation and their impact on the quantitative outcomes from functional pulmonary MRI. Additionally, to validate an artificial neural network (ANN) to accelerate whole-lung quantification. METHOD: Ten healthy children and 25 children with cystic fibrosis underwent matrix pencil decomposition MRI (MP-MRI). Impaired relative fractional ventilation (RFV ) and relative perfusion (RQ ) from MP-MRI were compared using whole-lung segmentation performed by a physician at two time-points (At1 and At2 ), by an MRI technician (B), and by an ANN (C). Repeatability and reproducibility were assess with Dice similarity coefficient (DSC), paired t-test and Intraclass-correlation coefficient (ICC). RESULTS: The repeatability within an observer (At1 vs At2 ) resulted in a DSC of 0.94 ± 0.01 (mean ± SD) and an unsystematic difference of -0.01% for RFV (P = .92) and +0.1% for RQ (P = .21). The reproducibility between human observers (At1 vs B) resulted in a DSC of 0.88 ± 0.02, and a systematic absolute difference of -0.81% (P < .001) for RFV and -0.38% (P = .037) for RQ . The reproducibility between human and the ANN (At1 vs C) resulted in a DSC of 0.89 ± 0.03 and a systematic absolute difference of -0.36% for RFV (P = .017) and -0.35% for RQ (P = .002). The ICC was >0.98 for all variables and comparisons. CONCLUSIONS: Despite high overall agreement, there were systematic differences in lung segmentation between observers. This needs to be considered for longitudinal studies and could be overcome by using an ANN, which performs as good as human observers and fully automatizes MP-MRI post-processing.

Itch reduction using immersive virtual reality-An experimental pilot study.

In a previous proof-of-concept study we have demonstrated that visual exposure to specific colors results in pruritic or antipruritic effects. To determine the effect of "antipruritic" colors when using immersive virtual reality (VR) and to assess whether psychometric values correlate with the response to the color exposure. In this cross-sectional interventional single-center study, itch patients were exposed to their subjective "antipruritic color" (defined by the Manchester Color Wheel) in a virtual monochromatic room for 10 min using a head-mounted display. Itch intensity rating (0-10 numerical rating scale [NRS]) was repeated at 1-min intervals. Additionally, dermatology life quality index, itch-related quality of life and the Hospital Anxiety and Depression Scale questionnaires were completed. Twenty-two patients (mean age 51.9 ± 23 years, 13 females) participated in the study. Following color exposure for 10 min itch intensity was significantly reduced compared to baseline (exact Wilcoxon signed-rank test, mdn-NRS 4.5 vs 3.0; z = -3.025, p = 0.001), confirmed by the area under the curve (z = -3.118; p = 0.001). No significant correlation between itch reduction and questionnaire scores was found (Spearman's Rho for all questionnaires). Visual exposure to the "antipruritic color" using immersive VR resulted in a significant decrease in itch intensity. This aligns with previous findings on the influence of colors on itch perception. The response of the intervention appeared independent of psychometric values. Thus, color exposure using immersive VR is a promising, low-cost, rapidly-acting, easily-applicable, non-pharmacological experimental antipruritic method.

Comparing the Bone Healing After Cold Ablation Robot-Guided Er:YAG Laser Osteotomy and Piezoelectric Osteotomy-A Pilot Study in a Minipig Mandible.

BACKGROUND AND OBJECTIVE: To take major advantage of erbium-doped yttrium aluminium garnet (Er:YAG) lasers in osteotomy-like freedom of cutting geometries and high accuracy-the integration and miniaturization of the robot, laser, and navigation technology was tried and applied to minipigs. The investigators hypothesized laser osteotomy would render acceptable bone healing based on the intraoperative findings and postoperative cut surface analysis. STUDY DESIGN/MATERIALS AND METHODS: We designed and implemented a comparative bone-cutting surgery in the minipig mandible with a cold ablation robot-guided Er:YAG laser osteotome (CARLO) and a piezoelectric (PZE) osteotome. The sample was composed of different patterns of defects in the mandibles of six grown-up female Goettingen minipigs. The predictor variable was Er:YAG osteotomy and PZE osteotomy. The outcome variable was the cut surface characteristics and bone healing at 4 and 8 weeks postoperatively. Descriptive and qualitative comparison was executed. RESULTS: The sample was composed of four kinds of bone defects on both sides of the mandibles of six minipigs. We observed more bleeding during the operation, open-cut surfaces, and a faster healing pattern with the laser osteotomy. There was a possible association between the intraoperative findings, postoperative cut surface analysis, and the bone healing pattern. CONCLUSIONS: The results of this study suggest that characteristic open-cut surfaces could explain favorable bone healing after laser osteotomy. Future studies will focus on the quantification of the early healing characteristics after laser osteotomy, its diverse application, and the safety feature. Lasers Surg. Med. © 2020 Wiley Periodicals LLC.

The price for reduced light toxicity: Do endoilluminator spectral filters decrease color contrast during Brilliant Blue G-assisted chromovitrectomy?

BACKGROUND: Vitreoretinal surgeons have been slow to adopt the use of spectral filters for endoillumination to reduce retinal light toxicity. This study shows that spectral filters can be used without a loss in color contrast during brilliant blue G chromovitrectomy. METHODS: To evaluate the influence of intra operative spectral light filters on perceivable contrast during Brilliant Blue G chromovitrectomy, a prospective, observational clinical study was carried out on 59 consecutive Brilliant Blue G chromovitrectomy interventions in 59 patients admitted for macular holes, macular pucker or vitreomacular traction syndromes. Subsequent to peeling of the internal limiting membrane, six different illumination modes were enabled consecutively: mercury vapor, mercury vapor/xenon, and xenon followed by xenon combined with an amber, green or yellow spectral filter. Main outcome measure was the chromaticity spread between stained internal limiting membrane and unstained retina as a measure for the color contrast perceived by the human eye. RESULTS: Mean chromaticity scores were similar for all light sources: mercury vapor 7.97, mercury vapor/xenon 7.96 (p = 0.96), and xenon 7.41 (p = 0.55). Compared to xenon, the additional use of endoillumination spectral filters did not change contrast recognizability: Chromaticity scores were 9.38 for the amber filter (p = 0.13), 6.63 for the green and 7.02 for the yellow filter (p = 0.37 and 0.64, respectively). When comparing the different filters head-to-head, the amber filter was superior to the green filter (p = 0.03), while the yellow was intermediate and not significantly different from either the amber (p = 0.08) or the green filter (p = 0.51). CONCLUSIONS: Color contrast perceptibility during Brilliant Blue G assisted chromovitrectomy is similar with mercury vapor, mercury vapor/xenon or xenon light sources. Spectral filters do not decrease color contrast recognizability. Head-to-head comparison shows a significant advantage for the amber over the green filter with respect to contrast generation, the yellow filter is intermediate. As spectral filters are known to greatly reduce retinal light toxicity, we suggest donor eye studies to validate whether the amber filter should be generally recommended for Brilliant Blue G chromovitrectomy.

Augmented reality for sentinel lymph node biopsy.

INTRODUCTION: Sentinel lymph node biopsy for oral and oropharyngeal squamous cell carcinoma is a well-established staging method. One variation is to inject a radioactive tracer near the primary tumor of the patient. After a few minutes, audio feedback from an external hand-held [Formula: see text]-detection probe can monitor the uptake into the lymphatic system. Such probes place a high cognitive load on the surgeon during the biopsy, as they require the simultaneous use of both hands and the skills necessary to correlate the audio signal with the location of tracer accumulation in the lymph nodes. Therefore, an augmented reality (AR) approach to directly visualize and thus discriminate nearby lymph nodes would greatly reduce the surgeons' cognitive load. MATERIALS AND METHODS: We present a proof of concept of an AR approach for sentinel lymph node biopsy by ex vivo experiments. The 3D position of the radioactive [Formula: see text]-sources is reconstructed from a single [Formula: see text]-image, acquired by a stationary table-attached multi-pinhole [Formula: see text]-detector. The position of the sources is then visualized using Microsoft's HoloLens. We further investigate the performance of our SLNF algorithm for a single source, two sources, and two sources with a hot background. RESULTS: In our ex vivo experiments, a single [Formula: see text]-source and its AR representation show good correlation with known locations, with a maximum error of 4.47 mm. The SLNF algorithm performs well when only one source is reconstructed, with a maximum error of 7.77 mm. For the more challenging case to reconstruct two sources, the errors vary between 2.23 mm and 75.92 mm. CONCLUSION: This proof of concept shows promising results in reconstructing and displaying one [Formula: see text]-source. Two simultaneously recorded sources are more challenging and require further algorithmic optimization.

Analysis of artificial intelligence in thyroid diagnostics and surgery: A scoping review.

BACKGROUND: Artificial Intelligence provides numerous applications in the healthcare sector. The main aim of this study is to evaluate the extent of the current application of artificial intelligence in thyroid diagnostics. METHODS: Our protocol was based on the Scoping Reviews extension of the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA-ScR). Information was gathered from PubMed, Cochrane, and EMBASE databases and Google Scholar. Eligible studies were published between 2017 and 2022. RESULTS: The search identified 133 records, after which 18 articles were included in the scoping review. All the publications were journal articles and discussed various ways that specialists in thyroid diagnostics and surgery have utilized artificial intelligence in their practice. CONCLUSIONS: The development and incorporation of Artificial Intelligence applications in thyroid diagnostics and surgery has been moderate yet promising. However, applications are currently inconsistent and further research is needed to delineate the true benefit and limitations in this field.

Patient-specific implants made of 3D printed bioresorbable polymers at the point-of-care: material, technology, and scope of surgical application.

BACKGROUND: Bioresorbable patient-specific additive-manufactured bone grafts, meshes, and plates are emerging as a promising alternative that can overcome the challenges associated with conventional off-the-shelf implants. The fabrication of patient-specific implants (PSIs) directly at the point-of-care (POC), such as hospitals, clinics, and surgical centers, allows for more flexible, faster, and more efficient processes, reducing the need for outsourcing to external manufacturers. We want to emphasize the potential advantages of producing bioresorbable polymer implants for cranio-maxillofacial surgery at the POC by highlighting its surgical applications, benefits, and limitations. METHODS: This study describes the workflow of designing and fabricating degradable polymeric PSIs using three-dimensional (3D) printing technology. The cortical bone was segmented from the patient's computed tomography data using Materialise Mimics software, and the PSIs were designed created using Geomagic Freeform and nTopology software. The implants were finally printed via Arburg Plastic Freeforming (APF) of medical-grade poly (L-lactide-co-D, L-lactide) with 30% ß-tricalcium phosphate and evaluated for fit. RESULTS: 3D printed implants using APF technology showed surfaces with highly uniform and well-connected droplets with minimal gap formation between the printed paths. For the plates and meshes, a wall thickness down to 0.8 mm could be achieved. In this study, we successfully printed plates for osteosynthesis, implants for orbital floor fractures, meshes for alveolar bone regeneration, and bone scaffolds with interconnected channels. CONCLUSIONS: This study shows the feasibility of using 3D printing to create degradable polymeric PSIs seamlessly integrated into virtual surgical planning workflows. Implementing POC 3D printing of biodegradable PSI can potentially improve therapeutic outcomes, but regulatory compliance must be addressed.

An analysis of virtual reality in abdominal surgery-A scoping review.

BACKGROUND: The integration of virtual reality (VR) in surgery has gained prominence as VR applications have increased in popularity. METHODS: A scoping review was undertaken, gathering the most relevant sources, utilising a detailed literature search of medical and academic databases including EMBASE, PubMed, Cochrane, IEEE, Google Scholar, and the Google search engine. RESULTS: Of the 18 articles included, 7 focused on VR in colon surgery, 5 addressed VR in pancreas surgery, and the remaining 6 concentrated on VR in liver surgery. All the articles concluded that VR has a promising future in abdominal surgery by facilitating precision, visualisation, and surgeon training. CONCLUSIONS: Adopting VR technology in abdominal surgery has the potential to improve preoperative planning, decrease perioperative anxiety among patients, and facilitate the training of surgeons, residents, and medical students. Additional supporting studies are necessary before VR can be widely implemented in surgical care delivery.

Time-Resolved Dynamic Optical Coherence Tomography for Retinal Blood Flow Analysis.

Purpose: Optical coherence tomography (OCT) representations in clinical practice are static and do not allow for a dynamic visualization and quantification of blood flow. This study aims to present a method to analyze retinal blood flow dynamics using time-resolved structural OCT. Methods: We developed novel imaging protocols to acquire video-rate time-resolved OCT B-scans (1024 × 496 pixels, 10 degrees field of view) at four different sensor integration times (integration time of 44.8 µs at a nominal A-scan rate of 20 kHz, 22.4 µs at 40 kHz, 11.2 µs at 85 kHz, and 7.24 µs at 125 kHz). The vessel centers were manually annotated for each B-scan and surrounding subvolumes were extracted. We used a velocity model based on signal-to-noise ratio (SNR) drops due to fringe washout to calculate blood flow velocity profiles in vessels within five optic disc diameters of the optic disc rim. Results: Time-resolved dynamic structural OCT revealed pulsatile SNR changes in the analyzed vessels and allowed the calculation of potential blood flow velocities at all integration times. Fringe washout was stronger in acquisitions with longer integration times; however, the ratio of the average SNR to the peak SNR inside the vessel was similar across all integration times. Conclusions: We demonstrated the feasibility of estimating blood flow profiles based on fringe washout analysis, showing pulsatile dynamics in vessels close to the optic nerve head using structural OCT. Time-resolved dynamic OCT has the potential to uncover valuable blood flow information in clinical settings.

Association of Spinal Cord Atrophy and Brain Paramagnetic Rim Lesions With Progression Independent of Relapse Activity in People With MS.

BACKGROUND AND OBJECTIVES: Progression independent of relapse activity (PIRA) is a crucial determinant of overall disability accumulation in multiple sclerosis (MS). Accelerated brain atrophy has been shown in patients experiencing PIRA. In this study, we assessed the relation between PIRA and neurodegenerative processes reflected by (1) longitudinal spinal cord atrophy and (2) brain paramagnetic rim lesions (PRLs). Besides, the same relationship was investigated in progressive MS (PMS). Last, we explored the value of cross-sectional brain and spinal cord volumetric measurements in predicting PIRA. METHODS: From an ongoing multicentric cohort study, we selected patients with MS with (1) availability of a susceptibility-based MRI scan and (2) regular clinical and conventional MRI follow-up in the 4 years before the susceptibility-based MRI. Comparisons in spinal cord atrophy rates (explored with linear mixed-effect models) and PRL count (explored with negative binomial regression models) were performed between: (1) relapsing-remitting (RRMS) and PMS phenotypes and (2) patients experiencing PIRA and patients without confirmed disability accumulation (CDA) during follow-up (both considering the entire cohort and the subgroup of patients with RRMS). Associations between baseline MRI volumetric measurements and time to PIRA were explored with multivariable Cox regression analyses. RESULTS: In total, 445 patients with MS (64.9% female; mean [SD] age at baseline 45.0 [11.4] years; 11.2% with PMS) were enrolled. Compared with patients with RRMS, those with PMS had accelerated cervical cord atrophy (mean difference in annual percentage volume change [MD-APC] -1.41; p = 0.004) and higher PRL load (incidence rate ratio [IRR] 1.93; p = 0.005). Increased spinal cord atrophy (MD-APC -1.39; p = 0.0008) and PRL burden (IRR 1.95; p = 0.0008) were measured in patients with PIRA compared with patients without CDA; such differences were also confirmed when restricting the analysis to patients with RRMS. Baseline volumetric measurements of the cervical cord, whole brain, and cerebral cortex significantly predicted time to PIRA (all p ≤ 0.002). DISCUSSION: Our results show that PIRA is associated with both increased spinal cord atrophy and PRL burden, and this association is evident also in patients with RRMS. These findings further point to the need to develop targeted treatment strategies for PIRA to prevent irreversible neuroaxonal loss and optimize long-term outcomes of patients with MS.

Quantification of contrast recognizability in sequential epiretinal membrane removal and internal limiting membrane peeling in trypan blue-assisted macular surgery.

PURPOSE: To evaluate the selectivity and strength of intraoperative trypan blue staining during removal of epiretinal membranes (ERMs) and the internal limiting membrane. METHODS: Based on intraoperative videos, 51 consecutive chromovitrectomies in 51 patients with macular holes, macular pucker, vitreomacular traction syndromes, or persistent macular edema were retrospectively studied. Fifteen subjects underwent trypan blue, 14 indocyanine green, and 22 brilliant blue G chromovitrectomy. The main outcome measure was the color contrast between stained internal limiting membrane or ERM and the underlying unstained tissue by means of objective, quantitative, semiautomated chromaticity difference measurements. RESULTS: Trypan blue stains both ERM and the internal limiting membrane (average chromaticity scores 8.51 and 7.09, respectively; P = 0.48). Internal limiting membrane chromaticity scores were similar for trypan blue (7.09) and brilliant blue G (6.81; P = 0.71) but clearly higher for indocyanine green (15.81; P = 2.45 × 10). CONCLUSION: Under the premises of our study, trypan blue stains both ERM and the internal limiting membrane. Trypan blue's staining capacity of the internal limiting membrane is similar to that of brilliant blue G but significantly inferior compared with indocyanine green. Trypan blue, thus, represents a useful vital dye for chromovitrectomy, particularly in the presence of ERM, where it allows a sequential approach.

Material properties of the internal limiting membrane and their significance in chromovitrectomy.

Intraoperative visualization of the internal limiting membrane (ILM), the choice of a point of vantage for lifting an initial flap, the precision with which the ILM is grasped, adhesion between the forceps and the ILM, thickness, stiffness and elasticity of the ILM as well as monitoring of the completeness of ILM removal are all important factors for safety and efficacy of a chromovitrectomy intervention. The understanding of the underlying physical features of the ILM, such as contrast behavior and bioanatomical and biomechanical properties represent, thus, useful prerequisites for successful macular surgery. New analytical tools, such as atomic force microscopy and chromaticity analysis, allow new insights into ILM material characteristics, permitting a systematic approach to refinement of surgical technique. .

Real-time closed-loop tissue-specific laser osteotomy using deep-learning-assisted optical coherence tomography.

This article presents a real-time noninvasive method for detecting bone and bone marrow in laser osteotomy. This is the first optical coherence tomography (OCT) implementation as an online feedback system for laser osteotomy. A deep-learning model has been trained to identify tissue types during laser ablation with a test accuracy of 96.28 %. For the hole ablation experiments, the average maximum depth of perforation and volume loss was 0.216 mm and 0.077 mm3, respectively. The contactless nature of OCT with the reported performance shows that it is becoming more feasible to utilize it as a real-time feedback system for laser osteotomy.

Improved distinct bone segmentation in upper-body CT through multi-resolution networks.

PURPOSE: Automated distinct bone segmentation from CT scans is widely used in planning and navigation workflows. U-Net variants are known to provide excellent results in supervised semantic segmentation. However, in distinct bone segmentation from upper-body CTs a large field of view and a computationally taxing 3D architecture are required. This leads to low-resolution results lacking detail or localisation errors due to missing spatial context when using high-resolution inputs. METHODS: We propose to solve this problem by using end-to-end trainable segmentation networks that combine several 3D U-Nets working at different resolutions. Our approach, which extends and generalizes HookNet and MRN, captures spatial information at a lower resolution and skips the encoded information to the target network, which operates on smaller high-resolution inputs. We evaluated our proposed architecture against single-resolution networks and performed an ablation study on information concatenation and the number of context networks. RESULTS: Our proposed best network achieves a median DSC of 0.86 taken over all 125 segmented bone classes and reduces the confusion among similar-looking bones in different locations. These results outperform our previously published 3D U-Net baseline results on the task and distinct bone segmentation results reported by other groups. CONCLUSION: The presented multi-resolution 3D U-Nets address current shortcomings in bone segmentation from upper-body CT scans by allowing for capturing a larger field of view while avoiding the cubic growth of the input pixels and intermediate computations that quickly outgrow the computational capacities in 3D. The approach thus improves the accuracy and efficiency of distinct bone segmentation from upper-body CT.

Automatic patient positioning based on robot rotational workspace for extended reality.

PURPOSE: Understanding the properties and aspects of the robotic system is essential to a successful medical intervention, as different capabilities and limits characterize each. Robot positioning is a crucial step in the surgical setup that ensures proper reachability to the desired port locations and facilitates docking procedures. This very demanding task requires much experience to master, especially with multiple trocars, increasing the barrier of entry for surgeons in training. METHODS: Previously, we demonstrated an Augmented Reality-based system to visualize the rotational workspace of the robotic system and proved it helps the surgical staff to optimize patient positioning for single-port interventions. In this work, we implemented a new algorithm to allow for an automatic, real-time robotic arm positioning for multiple ports. RESULTS: Our system, based on the rotational workspace data of the robotic arm and the set of trocar locations, can calculate the optimal position of the robotic arm in milliseconds for the positional and in seconds for the rotational workspace in virtual and augmented reality setups. CONCLUSIONS: Following the previous work, we extended our system to support multiple ports to cover a broader range of surgical procedures and introduced the automatic positioning component. Our solution can decrease the surgical setup time and eliminate the need to repositioning the robot mid-procedure and is suitable both for the preoperative planning step using VR and in the operating room-running on an AR headset.

Machine learning in pancreas surgery, what is new? literature review.

Background: Machine learning (ML) is an inquiry domain that aims to establish methodologies that leverage information to enhance performance of various applications. In the healthcare domain, the ML concept has gained prominence over the years. As a result, the adoption of ML algorithms has become expansive. The aim of this scoping review is to evaluate the application of ML in pancreatic surgery. Methods: We integrated the preferred reporting items for systematic reviews and meta-analyses for scoping reviews. Articles that contained relevant data specializing in ML in pancreas surgery were included. Results: A search of the following four databases PubMed, Cochrane, EMBASE, and IEEE and files adopted from Google and Google Scholar was 21. The main features of included studies revolved around the year of publication, the country, and the type of article. Additionally, all the included articles were published within January 2019 to May 2022. Conclusion: The integration of ML in pancreas surgery has gained much attention in previous years. The outcomes derived from this study indicate an extensive literature gap on the topic despite efforts by various researchers. Hence, future studies exploring how pancreas surgeons can apply different learning algorithms to perform essential practices may ultimately improve patient outcomes.

Robotic colorectal surgery: quality assessment of patient information available on the internet using webscraping.

The primary goal of this study is to assess current patient information available on the internet concerning robotic colorectal surgery. Acquiring this information will aid in patients understanding of robotic colorectal surgery. Data was acquired through a web-scraping algorithm. The algorithm used two Python packages: Beautiful Soup and Selenium. The long-chain keywords incorporated into Google, Bing and Yahoo search engines were 'Da Vinci Colon-Rectal Surgery', 'Colorectal Robotic Surgery' and 'Robotic Bowel Surgery'. 207 websites resulted, were sorted and evaluated according to the ensuring quality information for patients (EQIP) score. Of the 207 websites visited, 49 belonged to the subgroup of hospital websites (23.6%), 46 to medical centers (22.2%), 45 to practitioners (21.7%), 42 to health care systems (20,2%), 11 to news services (5.3%), 7 to web portals (3.3%), 5 to industry (2.4%), and 2 to patient groups (0.9%). Only 52 of the 207 websites received a high rating. The quality of available information on the internet concerning robotic colorectal surgery is low. The majority of information was inaccurate. Medical facilities involved in robotic colorectal surgery, robotic bowel surgery and related robotic procedures should develop websites with credible information to guide patient decisions.