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Abstract The eye and the heart are two closely interlinked organs, and many diseases affecting the cardiovascular system manifest in the eye. To contribute to the understanding of blood flow propagation towards the retina, we developed a method to acquire electrocardiogram (ECG) coupled time-resolved dynamic optical coherence tomography (OCT) images. This method allows for continuous synchronised monitoring of the cardiac cycle and retinal blood flow dynamics. The dynamic OCT measurements were used to calculate time-resolved blood flow profiles using fringe washout analysis. The relative fringe washout was computed to generate the flow velocity profiles within arterioles at the optic nerve head rim. We found that the blood column between the heart and the retina propagates within one cardiac cycle, denoting the arrival time as the heart-retina time (HRT). In a group of healthy subjects, the HRT was 144 ± 19 ms (mean ± SD). The HRT could provide a novel potential biomarker for cardiovascular health in direct relation to retinal perfusion.

IntroductionErgonomic issues are widespread among surgeons performing teleoperated robotic surgery. As the ergonomics of a teleoperation system depends on the controller handle, it needs to be designed wisely. While the importance of the controller handle in robot-assisted telemanipulation has been highlighted previously, most existing work on the usability of a human-robot system for surgery was of qualitative nature or did not focus on surgery-specific tasks.MethodsWe investigated the influence of nine different grasp-type telemanipulator handles on the usability of a lambda.6 haptic input device for a virtual six degrees of freedom peg-in-hole task. User performance with different handles was assessed through four usability metrics: i) task completion time, ii) dimensionless jerk, iii) collision forces, and iv) perceived workload. We compared these usability results with those of a prior study examining only the functional rotational workspace of the same human-robot system.ResultsThe linear mixed-effect model (LMM) analysis showed that all four usability metrics were dependent on the telemanipulator handle. Moreover, the LMM analysis showed an additional contribution of the hole accessibility to the usability of the human-robot system.DiscussionIn case contact forces between the follower end-effector and its surroundings are not critical, the fixed-hook-grasp handle showed the best results out of the nine tested handles. In case low contact forces are crucial, the tripod-grasp handle was most suitable. It can thus be deduced that different grasp-type telemanipulator handles affect system usability for a surgery-related, teleoperated six degrees of freedom placement task. Also, maximizing the functional rotational workspace can positively affect system usability.

BackgroundCraniosynostosis is a type of skull deformity caused by premature ossification of cranial sutures in children. Given its variability and anatomical complexity, three-dimensional visualization is crucial for effective teaching and understanding. We developed a VR database with 3D models to depict these deformities and evaluated its impact on teaching efficiency, motivation, and memorability.MethodsWe included all craniosynostosis cases with preoperative CT imaging treated at our institution from 2012 to 2022. Preoperative CT scans were imported into SpectoVR using a transfer function to visualize bony structures. Measurements, sub-segmentation, and anatomical teaching were performed in a fully immersive 3D VR experience using a headset. Teaching sessions were conducted in group settings where students and medical personnel explored and discussed the 3D models together, guided by a host. Participants’ experiences were evaluated with a questionnaire assessing understanding, memorization, and motivation on a scale from 1 (poor) to 5 (outstanding).ResultsThe questionnaire showed high satisfaction scores (mean 4.49 ± 0.25). Participants (n = 17) found the VR models comprehensible and navigable (mean 4.47 ± 0.62), with intuitive operation (mean 4.35 ± 0.79). Understanding pathology (mean 4.29 ± 0.77) and surgical procedures (mean 4.63 ± 0.5) was very satisfactory. The models improved anatomical visualization (mean 4.71 ± 0.47) and teaching effectiveness (mean 4.76 ± 0.56), with participants reporting enhanced comprehension and memorization, leading to an efficient learning process.ConclusionEstablishing a 3D VR database for teaching craniosynostosis shows advantages in understanding and memorization and increases motivation for the study process, thereby allowing for more efficient learning. Future applications in patient consent and teaching in other medical areas should be explored.

Abstract 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% β-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.

Abstract Fiber optic shape sensing is an innovative technology that has enabled remarkable advances in various navigation and tracking applications. Although the state-of-the-art fiber optic shape sensing mechanisms can provide sub-millimeter spatial resolution for off-axis strain measurement and reconstruct the sensor’s shape with high tip accuracy, their overall cost is very high. The major challenge in more cost-effective fiber sensor alternatives for providing accurate shape measurement is the limited sensing resolution in detecting shape deformations. Here, we present a data-driven technique to overcome this limitation by removing strain measurement, curvature estimation, and shape reconstruction steps. We designed an end-to-end convolutional neural network that is trained to directly predict the sensor’s shape based on its spectrum. Our fiber sensor is based on easy-to-fabricate eccentric fiber Bragg gratings and can be interrogated with a simple and cost-effective readout unit in the spectral domain. We demonstrate that our deep-learning model benefits from undesired bending-induced effects (e.g., cladding mode coupling and polarization), which contain high-resolution shape deformation information. These findings are the preliminary steps toward a low-cost yet accurate fiber shape sensing solution for detecting complex multi-bend deformations.

The detection of contrast-enhancing lesions (CELs) is fundamental for the diagnosis and monitoring of patients with multiple sclerosis (MS). This task is time-consuming and suffers from high intra- and inter-rater variability in clinical practice. However, only a few studies proposed automatic approaches for CEL detection. This study aimed to develop a deep learning model that automatically detects and segments CELs in clinical Magnetic Resonance Imaging (MRI) scans. A 3D UNet-based network was trained with clinical MRI from the Swiss Multiple Sclerosis Cohort. The dataset comprised 372 scans from 280 MS patients: 162 showed at least one CEL, while 118 showed no CELs. The input dataset consisted of T1-weighted before and after gadolinium injection, and FLuid Attenuated Inversion Recovery images. The sampling strategy was based on a white matter lesion mask to confirm the existence of real contrast-enhancing lesions. To overcome the dataset imbalance, a weighted loss function was implemented. The Dice Score Coefficient and True Positive and False Positive Rates were 0.76, 0.93, and 0.02, respectively. Based on these results, the model developed in this study might well be considered for clinical decision support.

Abstract Currently, most medical image data, such as optical coherence tomography (OCT) images, are displayed in two dimensions on a computer screen. Advances in computer information technology have contributed to the growing storage of these data in electronic form. However, the data are usually processed only locally on site. To overcome such hurdles, a cyberspace virtual reality (csVR) application was validated, in which interactive OCT data were presented simultaneously to geographically distant sites (Lucerne, London, and Barcelona) where three graders independently measured the ocular csVR OCT diameters. A total of 109 objects were measured, each three times, resulting in a total of 327 csVR measurements. A minor mean absolute difference of 5.3 µm was found among the 3 measurements of an object (standard deviation 4.2 µm, coefficient of variation 0.3% with respect to the mean object size). Despite the 5 h of online work, csVR was well tolerated and safe. Digital high-resolution OCT data can be remotely and collaboratively processed in csVR. With csVR, measurements and actions enhanced with spatial audio communication can be made consistently in near real time, even if the users are situated geographically far apart. The proposed visuo-auditory framework has the potential to further boost the convenience of digital medicine toward csVR precision and collaborative medicine.

AbstractThe 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.

In the above article [1], we found major issues with the data we used. Specifically, we received data from [2] for five distinct tissues, with ten specimens per tissue. However, upon closer examination, we realized that the data for these specimens were not unique; rather, they were scaled variations derived from a single specimen. As a result, our training, testing, and validation datasets were not independent, leading to an artificially high accuracy rate of 100%.

BackgroundMachine 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.MethodsWe 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.ResultsA 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.ConclusionThe 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.

Using a laser for cutting bones instead of the traditional saws improves a patient’s healing process. Additionally, the laser has the potential to reduce the collateral damage to the surrounding tissue if appropriately applied. This can be achieved by building additional sensing elements besides the laser itself into an endoscope. To this end, we use a microsecond pulsed Erbium-doped Yttrium Aluminium Garnet (Er:YAG) laser to cut bones. During ablation, each pulse emits an acoustic shock wave that is captured by an air-coupled transducer. In our research, we use the data from these acoustic waves to predict the depth of the cut during the ablation process. We use a Neural Network (NN) to estimate the depth, where we use one or multiple consecutive measurements of acoustic waves. The NN outperforms the base-line method that assumes a constant ablation rate with each pulse to predict the depth. The results are evaluated and compared against the ground-truth depth measurements from Optical Coherence Tomography (OCT) images that measure the depth in real-time during the ablation process.

Hospitals are burdened with predicting, calculating, and managing various cost-affecting parameters regarding patients and their treatments. Accuracy in cost prediction is further affected when a patient suffers from other health issues that hinder the traditional prognosis. This can lead to an unavoidable deficit in the final revenue of medical centers. This study aims to determine whether machine learning (ML) algorithms can predict cost factors based on patients undergoing colon surgery. For the forecasting, multiple predictors will be taken into the model to provide a tool that can be helpful for hospitals to manage their costs, ultimately leading to operating more cost-efficiently. This proof of principle will lay the groundwork for an efficient ML-based prediction tool based on multicenter data from a range of international centers in the subsequent phases of the study. With a mean absolute percentage error result of 18%–25.6%, our model's prediction showed decent results in forecasting the costs regarding various diagnosed factors and surgical approaches. There is an urgent need for further studies on predicting cost factors, especially for cases with anastomotic leakage, to minimize unnecessary hospital costs.

Today’s mechanical tools for bone cutting (osteotomy) lead to mechanical trauma that prolong the healing process. Medical device manufacturers continuously strive to improve their tools to minimize such trauma. One example of such a new tool and procedure is minimally invasive surgery with laser as the cutting element. This setup allows for tissue ablation using laser light instead of mechanical tools, which reduces the post-surgery healing time. During surgery, a reliable feedback system is crucial to avoid collateral damage to the surrounding tissues. Therefore, we propose a tissue classification method that analyzes the acoustic waves produced during laser ablation and show its applicability in an ex-vivo experiment. The ablation process with a microsecond pulsed Erbium-doped Yttrium Aluminium Garnet (Er:YAG) laser produces acoustic waves that we captured with an air-coupled transducer. Consequently, we used these captured waves to classify five porcine tissue types: hard bone, soft bone, muscle, fat, and skin tissue. For automated tissue classification of the measured acoustic waves, we propose three Neural Network (NN) approaches: A Fully-connected Neural Network (FcNN), a one-dimensional Convolutional Neural Network (CNN), and a Recurrent Neural Network (RNN). The time- and the frequency-dependent parts of the measured waves’ pressure variation were used as separate inputs to train and validate the designed NNs. In a final step, we used Grad-CAM to find the frequencies’ activation map and conclude that the low frequencies are the most important ones for this classification task. In our experiments, we achieved an accuracy of 100% for the five tissue types for all the proposed NNs. We tested the different classifiers for their robustness and concluded that using frequency-dependent data together with a FcNN is the most robust approach.

Abstract Background Squamous cell carcinoma in the head and neck region is one of the most widespread cancers with high morbidity. Classic treatment comprises the complete removal of the lymphatics together with the cancerous tissue. Recent studies have shown that such interventions are only required in 30% of the patients. Sentinel lymph node biopsy is an alternative method to stage the malignancy in a less invasive manner and to avoid overtreatment. In this paper, we present a novel approach that enables a future augmented reality device which improves the biopsy procedure by visual means. Methods We propose a co-calibration scheme for axis-aligned miniature cameras with pinholes of a gamma ray collimating and sensing device and show results gained by experiments, based on a calibration target visible for both modalities. Results Visual inspection and quantitative evaluation of the augmentation of optical camera images with gamma information are congruent with known gamma source landmarks. Conclusions Combining a multi-pinhole collimator with axis-aligned miniature cameras to augment optical images using gamma detector data is promising. As such, our approach might be applicable for breast cancer and melanoma staging as well, which are also based on sentinel lymph node biopsy.