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Image-guided surgery is growing in importance with each year. Various imaging technologies are used. The objective of this study was to test whether a new mixed reality navigation system (MRNS) improved percutaneous punctures. This system allowed to clearly visualize the needle tip, needle orientation, US probe and puncture target simultaneously with an interactive 3D computer user inferface. Prospective pre-clinical comparative study. An opaque ballistic gel phantom containing grapes of different sizes was used to simulate puncture targets. The evaluation consisted of ultrasound-guided (US-guided) needle punctures divided into two groups, standard group consisted of punctures using the standard approach (US-guided), and assisted navigation group consisted of punctures using MRNS. Once a puncture was completed, a computed tomography scan was made of the phantom and needle. The distance between the needle tip and the center of the target was measured. The time required to complete the puncture and puncture attempts was also calculated. Total participants was n = 23, between surgeons, medical technicians and radiologist. The participants were divided into novices (without experience, 69.6%) and experienced (with experience > 25 procedures, 30.4%). Each participant performed the puncture of six targets. For puncture completion time, the assisted navigation group was faster (42.1%) compared to the standard group (57.9%) (28.3 s ± 24.7 vs. 39.3 s ± 46.3—p 0.775). The total punctures attempts was lower in the assisted navigation group (35.4%) compared to the standard group (64.6%) (1.0 mm ± 0.2 vs. 1.8 mm ± 1.1—p 0.000). The assisted navigation group was more accurate than the standard group (4.2 ± 2.9 vs. 6.5 ± 4.7—p 0.003), observed in both novices and experienced groups. The use of MRNS improved ultrasound-guided percutaneous punctures parameters compared to the standard approach.

The quality of health care in Mesoamerica is influenced by its rich cultural diversity and characterized by social inequalities. Especially indigenous and rural communities confront diverse barriers to accessing formal health services, leading to often conflicting plurimedical systems. Fostering integrative medicine is a fundamental pillar for achieving universal health coverage (UHC) for marginalized populations. Recent developments toward health sovereignty in the region are concerned with assessing the role of traditional medicines, and particularly herbal medicines, to foster accessible and culturally pertinent healthcare provision models. In Mesoamerica, as in most regions of the world, a wealth of information on traditional and complementary medicine has been recorded. Yet these data are often scattered, making it difficult for policy makers to regulate and integrate traditionally used botanical products into primary health care. This critical review is based on a quantitative analysis of 28 survey papers focusing on the traditional use of botanical drugs in Mesoamerica used for the compilation of the \"Mesoamerican Medicinal Plant Database\" (MAMPDB), which includes a total of 12,537 use-records for 2188 plant taxa. Our approach presents a fundamental step toward UHC by presenting a pharmacological and toxicological review of the cross-culturally salient plant taxa and associated botanical drugs used in traditional medicine in Mesoamerica. Especially for native herbal drugs, data about safety and effectiveness are limited. Commonly used cross-culturally salient botanical drugs, which are considered safe but for which data on effectiveness is lacking constitute ideal candidates for treatment outcome studies.

BackgroundVisualization of key anatomical landmarks is required during surgical Trans Abdominal Pre Peritoneal repair (TAPP) of inguinal hernia. The Critical View of the MyoPectineal Orifice (CVMPO) was proposed to ensure correct dissection. An artificial intelligence (AI) system that automatically validates the presence of key and marks during the procedure is a critical step towards automatic dissection quality assessment and video-based competency evaluation. The aim of this study was to develop an AI system that automatically recognizes the TAPP key CVMPO landmarks in hernia repair videos. MethodsSurgical videos of 160 TAPP procedures were used in this single-center study. A deep neural network-based object detector was developed to automatically recognize the pubic symphysis, direct hernia orifice, Cooper’s ligament, the iliac vein, triangle of Doom, deep inguinal ring, and iliopsoas muscle. The system was trained using 130 videos, annotated and verified by two board-certified surgeons. Performance was evaluated in 30 videos of new patients excluded from the training data.ResultsPerformance was validated in 2 ways: first, single-image validation where the AI model detected landmarks in a single laparoscopic image (mean average precision (MAP) of 51.2%). The second validation is video evaluation where the model detected landmarks throughout the myopectineal orifice visual inspection phase (mean accuracy and F-score of 77.1 and 75.4% respectively). Annotation objectivity was assessed between 2 surgeons in video evaluation, showing a high agreement of 88.3%. ConclusionThis study establishes the first AI-based automated recognition of critical structures in TAPP surgical videos, and a major step towards automatic CVMPO validation with AI. Strong performance was achieved in the video evaluation. The high inter-rater agreement confirms annotation quality and task objectivity.

BackgroundPhase and step annotation in surgical videos is a prerequisite for surgical scene understanding and for downstream tasks like intraoperative feedback or assistance. However, most ontologies are applied on small monocentric datasets and lack external validation. To overcome these limitations an ontology for phases and steps of laparoscopic Roux-en-Y gastric bypass (LRYGB) is proposed and validated on a multicentric dataset in terms of inter- and intra-rater reliability (inter-/intra-RR).MethodsThe proposed LRYGB ontology consists of 12 phase and 46 step definitions that are hierarchically structured. Two board certified surgeons (raters) with > 10 years of clinical experience applied the proposed ontology on two datasets: (1) StraBypass40 consists of 40 LRYGB videos from Nouvel Hôpital Civil, Strasbourg, France and (2) BernBypass70 consists of 70 LRYGB videos from Inselspital, Bern University Hospital, Bern, Switzerland.To assess inter-RR the two raters’ annotations of ten randomly chosen videos from StraBypass40 and BernBypass70 each, were compared. To assess intra-RR ten randomly chosen videos were annotated twice by the same rater and annotations were compared.Inter-RR was calculated using Cohen’s kappa. Additionally, for inter- and intra-RR accuracy, precision, recall, F1-score, and application dependent metrics were applied.ResultsThe mean ± SD video duration was 108 ± 33 min and 75 ± 21 min in StraBypass40 and BernBypass70, respectively. The proposed ontology shows an inter-RR of 96.8 ± 2.7% for phases and 85.4 ± 6.0% for steps on StraBypass40 and 94.9 ± 5.8% for phases and 76.1 ± 13.9% for steps on BernBypass70. The overall Cohen’s kappa of inter-RR was 95.9 ± 4.3% for phases and 80.8 ± 10.0% for steps. Intra-RR showed an accuracy of 98.4 ± 1.1% for phases and 88.1 ± 8.1% for steps.ConclusionThe proposed ontology shows an excellent inter- and intra-RR and should therefore be implemented routinely in phase and step annotation of LRYGB.

Three-dimensional printing (3DP) has recently gained importance in the medical industry, especially in surgical specialties. It uses different techniques and materials based on patients’ needs, which allows bioprofessionals to design and develop unique pieces using medical imaging provided by computed tomography (CT) and magnetic resonance imaging (MRI). Therefore, the Department of Biology and Medicine and the Department of Physics and Engineering, at the Bioastronautics and Space Mechatronics Research Group, have managed and supervised an international cooperation study, in order to present a general review of the innovative surgical applications, focused on anatomical systems, such as the nervous and craniofacial system, cardiovascular system, digestive system, genitourinary system, and musculoskeletal system. Finally, the integration with augmented, mixed, virtual reality is analyzed to show the advantages of personalized treatments, taking into account the improvements for preoperative, intraoperative planning, and medical training. Also, this article explores the creation of devices and tools for space surgery to get better outcomes under changing gravity conditions.

BackgroundGastroparesis is a rapidly increasing problem with sometimes devastating consequences. While surgical treatments, particularly laparoscopic pyloroplasty, have recently gained popularity, they require general anesthesia, advanced skills, and can lead to leaks. Peroral pyloromyotomy is a less invasive alternative; however, this technique is technically demanding and not widely available. We describe a hybrid laparo-endoscopic collaborative approach using a novel gastric access device to allow endoluminal stapled pyloroplasty as an alternative treatment option for gastric outlet obstruction.MethodsUnder general anesthesia, six pigs (mean weight 33 kg) underwent endoscopic placement of intragastric ports using a technique similar to percutaneous endoscopic gastrostomy. A 5 mm laparoscope was used for visualization. A functional lumen imagine probe was used to measure the cross-sectional area (CSA) and diameter of the pylorus before, after, and at 1 week after intervention. Pyloroplasty was performed using a 5 mm articulating laparoscopic stapler. Gastrotomies were closed by endoscopic clips, endoscopic suture, or combination. After 6–8 days, a second evaluation was performed. At the end of the protocol, all animals were euthanized.ResultsSix pyloroplasties were performed. In all cases, this technique was effective in achieving significant pyloric dilatation. The median pre-pyloroplasty pyloric diameter (D) and cross-sectional area (CSA) were 8 mm (4.9–11.6 mm) and 58.6 mm2 (19–107 mm2), respectively. After the procedure, these values increased to 13.41 mm (9.8–17.6 mm) and 147.7 mm2 (76–244 mm2), respectively (p = 0.0152). No important intraoperative events were observed. Postoperatively, all animals did well, with adequate oral intake and no relevant complications. At follow-up endoscopy, all incisions were healed and the pylorus widely patent.ConclusionsHybrid endoluminal stapled pyloroplasty is a feasible, safe, and effective alternative method for the treatment of gastric outlet obstruction syndrome.

BackgroundThe growing interest in analysis of surgical video through machine learning has led to increased research efforts; however, common methods of annotating video data are lacking. There is a need to establish recommendations on the annotation of surgical video data to enable assessment of algorithms and multi-institutional collaboration.MethodsFour working groups were formed from a pool of participants that included clinicians, engineers, and data scientists. The working groups were focused on four themes: (1) temporal models, (2) actions and tasks, (3) tissue characteristics and general anatomy, and (4) software and data structure. A modified Delphi process was utilized to create a consensus survey based on suggested recommendations from each of the working groups.ResultsAfter three Delphi rounds, consensus was reached on recommendations for annotation within each of these domains. A hierarchy for annotation of temporal events in surgery was established.ConclusionsWhile additional work remains to achieve accepted standards for video annotation in surgery, the consensus recommendations on a general framework for annotation presented here lay the foundation for standardization. This type of framework is critical to enabling diverse datasets, performance benchmarks, and collaboration.

Leafy green vegetables have been a part of human diets throughout human history. Globally, they are gaining recognition since these wild foods could play an important role in food security. Quelites (the Mexican term for these resources) are dehydrated to produce “quelites pasados” by the Rarámuri in anticipation of the scarcity of food in winter. The diversity of quelites in the state of Chihuahua includes species of the widely consumed Amaranthus , as well as endemic, native, and introduced species that are eaten locally. The present work generated nutritional, sensory, and molecular information on four species that are consumed in the Sierra Tarahumara: Amaranthus palmeri , Amaranthus powellii , Arracacia edulis , and Phacelia platycarpa . Their nutritional analysis exhibited high protein values and a significant concentration of macro- and micronutrients. The acceptance by the public of the species of Amaranthus was high, while that of Arracacia edulis and Phacelia platycarpa was lower. Because of the morphological similarity within the two pairs of quelites, their DNA barcodes were generated as an identification tool which, together with the nutritional and sensory results, provides added value to the four “quelites pasados” of the Sierra Tarahumara. This study could be considered a starting point for sustainable use of native vegetables in future economic programs of regional agrobiodiversity, and even replicated globally.

Purpose Automatic recognition of surgical activities from intraoperative surgical videos is crucial for developing intelligent support systems for computer-assisted interventions. Current state-of-the-art recognition methods are based on deep learning where data augmentation has shown the potential to improve the generalization of these methods. This has spurred work on automated and simplified augmentation strategies for image classification and object detection on datasets of still images. Extending such augmentation methods to videos is not straightforward, as the temporal dimension needs to be considered. Furthermore, surgical videos pose additional challenges as they are composed of multiple, interconnected, and long-duration activities. Methods This work proposes a new simplified augmentation method, called TRandAugment , specifically designed for long surgical videos, that treats each video as an assemble of temporal segments and applies consistent but random transformations to each segment. The proposed augmentation method is used to train an end-to-end spatiotemporal model consisting of a CNN (ResNet50) followed by a TCN. Results The effectiveness of the proposed method is demonstrated on two surgical video datasets, namely Bypass40 and CATARACTS, and two tasks, surgical phase and step recognition. TRandAugment adds a performance boost of 1–6% over previous state-of-the-art methods, that uses manually designed augmentations. Conclusion This work presents a simplified and automated augmentation method for long surgical videos. The proposed method has been validated on different datasets and tasks indicating the importance of devising temporal augmentation methods for long surgical videos.

Purpose Most studies on surgical activity recognition utilizing artificial intelligence (AI) have focused mainly on recognizing one type of activity from small and mono-centric surgical video datasets. It remains speculative whether those models would generalize to other centers. Methods In this work, we introduce a large multi-centric multi-activity dataset consisting of 140 surgical videos (MultiBypass140) of laparoscopic Roux-en-Y gastric bypass (LRYGB) surgeries performed at two medical centers, i.e., the University Hospital of Strasbourg, France (StrasBypass70) and Inselspital, Bern University Hospital, Switzerland (BernBypass70). The dataset has been fully annotated with phases and steps by two board-certified surgeons. Furthermore, we assess the generalizability and benchmark different deep learning models for the task of phase and step recognition in 7 experimental studies: (1) Training and evaluation on BernBypass70; (2) Training and evaluation on StrasBypass70; (3) Training and evaluation on the joint MultiBypass140 dataset; (4) Training on BernBypass70, evaluation on StrasBypass70; (5) Training on StrasBypass70, evaluation on BernBypass70; Training on MultiBypass140, (6) evaluation on BernBypass70 and (7) evaluation on StrasBypass70. Results The model’s performance is markedly influenced by the training data. The worst results were obtained in experiments (4) and (5) confirming the limited generalization capabilities of models trained on mono-centric data. The use of multi-centric training data, experiments (6) and (7), improves the generalization capabilities of the models, bringing them beyond the level of independent mono-centric training and validation (experiments (1) and (2)). Conclusion MultiBypass140 shows considerable variation in surgical technique and workflow of LRYGB procedures between centers. Therefore, generalization experiments demonstrate a remarkable difference in model performance. These results highlight the importance of multi-centric datasets for AI model generalization to account for variance in surgical technique and workflows. The dataset and code are publicly available at https://github.com/CAMMA-public/MultiBypass140.