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Visual discrimination of tissue during surgery can be challenging since different tissues appear similar to the human eye. Hyperspectral imaging (HSI) removes this limitation by associating each pixel with high-dimensional spectral information. While previous work has shown its general potential to discriminate tissue, clinical translation has been limited due to the method’s current lack of robustness and generalizability. Specifically, the scientific community is lacking a comprehensive spectral tissue atlas, and it is unknown whether variability in spectral reflectance is primarily explained by tissue type rather than the recorded individual or specific acquisition conditions. The contribution of this work is threefold: (1) Based on an annotated medical HSI data set (9059 images from 46 pigs), we present a tissue atlas featuring spectral fingerprints of 20 different porcine organs and tissue types. (2) Using the principle of mixed model analysis, we show that the greatest source of variability related to HSI images is the organ under observation. (3) We show that HSI-based fully-automatic tissue differentiation of 20 organ classes with deep neural networks is possible with high accuracy (> 95%). We conclude from our study that automatic tissue discrimination based on HSI data is feasible and could thus aid in intraoperative decisionmaking and pave the way for context-aware computer-assisted surgery systems and autonomous robotics.

Immersive virtual reality (iVR) has emerged as a training method to prepare medical first responders (MFRs) for mass casualty incidents (MCIs) and disasters in a resource-efficient, flexible, and safe manner. However, systematic evaluations and validations of potential performance indicators for virtual MCI training are still lacking. This study aimed to investigate whether different performance indicators based on visual attention, triage performance, and information transmission can be effectively extended to MCI training in iVR by testing if they can discriminate between different levels of expertise. Furthermore, the study examined the extent to which such objective indicators correlate with subjective performance assessments. A total of 76 participants (mean age 25.54, SD 6.01 y; 45/76, 59% male) with different medical expertise (MFRs: paramedics and emergency physicians; non-MFRs: medical students, in-hospital nurses, and other physicians) participated in 5 virtual MCI scenarios of varying complexity in a randomized order. Tasks involved assessing the situation, triaging virtual patients, and transmitting relevant information to a control center. Performance indicators included eye-tracking-based visual attention, triage accuracy, triage speed, information transmission efficiency, and self-assessment of performance. Expertise was determined based on the occupational group (39/76, 51% MFRs vs 37/76, 49% non-MFRs) and a knowledge test with patient vignettes. Triage accuracy (d=0.48), triage speed (d=0.42), and information transmission efficiency (d=1.13) differentiated significantly between MFRs and non-MFRs. In addition, higher triage accuracy was significantly associated with higher triage knowledge test scores (Spearman ρ=0.40). Visual attention was not significantly associated with expertise. Furthermore, subjective performance was not correlated with any other performance indicator. iVR-based MCI scenarios proved to be a valuable tool for assessing the performance of MFRs. The results suggest that iVR could be integrated into current MCI training curricula to provide frequent, objective, and potentially (partly) automated performance assessments in a controlled environment. In particular, performance indicators, such as triage accuracy, triage speed, and information transmission efficiency, capture multiple aspects of performance and are recommended for integration. While the examined visual attention indicators did not function as valid performance indicators in this study, future research could further explore visual attention in MCI training and examine other indicators, such as holistic gaze patterns. Overall, the results underscore the importance of integrating objective indicators to enhance trainers' feedback and provide trainees with guidance on evaluating and reflecting on their own performance.

Exocrine and endocrine pancreas are interconnected anatomically and functionally, with vasculature facilitating bidirectional communication. Our understanding of this network remains limited, largely due to two-dimensional histology and missing combination with three-dimensional imaging. In this study, a multiscale 3D-imaging process was used to analyze a porcine pancreas. Clinical computed tomography, digital volume tomography, micro-computed tomography and Synchrotron-based propagation-based imaging were applied consecutively. Fields of view correlated inversely with attainable resolution from a whole organism level down to capillary structures with a voxel edge length of 2.0 µm. Segmented vascular networks from 3D-imaging data were correlated with tissue sections stained by immunohistochemistry and revealed highly vascularized regions to be intra-islet capillaries of islets of Langerhans. Generated 3D-datasets allowed for three-dimensional qualitative and quantitative organ and vessel structure analysis. Beyond this study, the method shows potential for application across a wide range of patho-morphology analyses and might possibly provide microstructural blueprints for biotissue engineering.

A bioartificial endocrine pancreas is proposed as a future alternative to current treatment options. Patients with insulin-secretion deficiency might benefit. This is the first systematic review that provides an overview of scaffold materials and techniques for insulin-secreting cells or cells to be differentiated into insulin-secreting cells. An electronic literature survey was conducted in PubMed/MEDLINE and Web of Science, limited to the past 10 years. A total of 197 articles investigating 60 different materials met the inclusion criteria. The extracted data on materials, cell types, study design, and transplantation sites were plotted into two evidence gap maps. Integral parts of the tissue engineering network such as fabrication technique, extracellular matrix, vascularization, immunoprotection, suitable transplantation sites, and the use of stem cells are highlighted. This systematic review provides an evidence-based structure for future studies. Accumulating evidence shows that scaffold-based tissue engineering can enhance the viability and function or differentiation of insulin-secreting cells both in vitro and in vivo.

Among advanced therapy medicinal products, tissue-engineered products have the potential to address the current critical shortage of donor organs and provide future alternative options in organ replacement therapy. The clinically available tissue-engineered products comprise bradytrophic tissue such as skin, cornea, and cartilage. A sufficient macro- and microvascular network to support the viability and function of effector cells has been identified as one of the main challenges in developing bioartificial parenchymal tissue. Three-dimensional bioprinting is an emerging technology that might overcome this challenge by precise spatial bioink deposition for the generation of a predefined architecture. Bioinks are printing substrates that may contain cells, matrix compounds, and signaling molecules within support materials such as hydrogels. Bioinks can provide cues to promote vascularization, including proangiogenic signaling molecules and cocultured cells. Both of these strategies are reported to enhance vascularization. We review pre-, intra-, and postprinting strategies such as bioink composition, bioprinting platforms, and material deposition strategies for building vascularized tissue. In addition, bioconvergence approaches such as computer simulation and artificial intelligence can support current experimental designs. Imaging-derived vascular trees can serve as blueprints. While acknowledging that a lack of structured evidence inhibits further meta-analysis, this review discusses an end-to-end process for the fabrication of vascularized, parenchymal tissue.

The COVID-19 pandemic has worldwide individual and socioeconomic consequences. Chest computed tomography has been found to support diagnostics and disease monitoring. A standardized approach to generate, collect, analyze, and share clinical and imaging information in the highest quality possible is urgently needed. We developed systematic, computer-assisted and context-guided electronic data capture on the FDA-approved mint Lesion TM software platform to enable cloud-based data collection and real-time analysis. The acquisition and annotation include radiological findings and radiomics performed directly on primary imaging data together with information from the patient history and clinical data. As proof of concept, anonymized data of 283 patients with either suspected or confirmed SARS-CoV-2 infection from eight European medical centers were aggregated in data analysis dashboards. Aggregated data were compared to key findings of landmark research literature. This concept has been chosen for use in the national COVID-19 response of the radiological departments of all university hospitals in Germany.

Abstract Background & Aims 3D-Bioprinting of an endocrine pancreas is a promising future curative treatment for selected patients with insulin secretion deficiency. In this study we present an end-to-end integrative, scalable concept extending from the molecular to the macroscopic level. Methods A hybrid scaffold device was manufactured by 3D-(bio)printing. INS-1 cells with/without endothelial cells were bioprinted in gelatin methacrylate blend hydrogel. Polycaprolactone was 3D-printed and heparin-functionalized as structural scaffold component. In vitro evaluation was performed by viability and growth assays, total mRNA sequencing, and glucose-stimulated insulin secretion. In vivo, xenotransplantation to fertilized chicken eggs was used to investigate vascularization and function, and finite element analysis modeling served to detect boundary conditions and applicability for human islets of Langerhans. Results Insulin-secreting pseudoislets were formed and resulted in a viable and proliferative experimental model. Transcriptomics revealed upregulation of proliferative and β-cell-specific signaling cascades, downregulation of apoptotic pathways, and overexpression of extracellular matrix proteins and VEGF induced by pseudoislet formation and 3D culture. Co-culture with human endothelial cells created a natural cellular niche resulting in enhanced insulin response after glucose stimulation. Survival and function of the pseudoislets after explantation and extensive scaffold vascularization of both the hydrogel and heparinized polycaprolactone components were demonstrated in ovo. Computer simulations of oxygen, glucose, and insulin flows were used to evaluate scaffold architectures and Langerhans islets at a future transplantation site along neurovascular structures. Conclusion A defined end-to-end process for multidisciplinary bioconvergence research on a bioartificial endocrine pancreas was developed. A modular, patient-specific device architecture is proposed for future research studies. Competing Interest Statement The authors have declared no competing interest. Footnotes * https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE166285

Objective: To optimize anastomotic technique and gastric conduit perfusion with hyperspectral imaging (HSI) for total minimally invasive esophagectomy (MIE) with linear stapled anastomosis. Summary Background Data: Esophagectomy is the mainstay of esophageal cancer treatment but anastomotic insufficiency related morbidity and mortality remain challenging for patient outcome. Methods: A live porcine model (n=50) for MIE was used with gastric conduit formation and linear stapled side-to-side esophagogastrostomy. Four main experimental groups differed in stapling length (3 vs. 6 cm) and anastomotic position on the conduit (cranial vs. caudal). Tissue oxygenation around the anastomotic site was evaluated using HSI and was validated with histopathology. Results: The tissue oxygenation (ΔStO2) after the anastomosis remained constant only for the short stapler in caudal position (-0.4±4.4%, n.s.) while it dropped markedly in the other groups (short-cranial: -15.6±11.5%, p=0.0002; long-cranial: -20.4±7.6%, p=0.0126; long-caudal: -16.1±9.4%, p<0.0001) Tissue samples from deoxygenated stomach as measured by HSI showed correspondent eosinophilic pre-necrotic changes in 35.7±9.7% of the surface area. Conclusions: Tissue oxygenation at the anastomotic site of the gastric conduit during MIE is influenced by stapling technique. Optimal oxygenation was achieved with a short stapler (3 cm) and sufficient distance of the anastomosis to the cranial end of the gastric conduit. HSI tissue deoxygenation corresponded to histopathologic necrotic tissue changes. These findings allow for optimization of gastric conduit perfusion and anastomotic technique in MIE. Competing Interest Statement The authors have declared no competing interest.

Hyperspectral Imaging (HSI) is a relatively new medical imaging modality that exploits an area of diagnostic potential formerly untouched. Although exploratory translational and clinical studies exist, no surgical HSI datasets are openly accessible to the general scientific community. To address this bottleneck, this publication releases HeiPorSPECTRAL ( https://www.heiporspectral.org ; https://doi.org/10.5281/zenodo.7737674 ), the first annotated high-quality standardized surgical HSI dataset. It comprises 5,758 spectral images acquired with the TIVITA ® Tissue and annotated with 20 physiological porcine organs from 8 pigs per organ distributed over a total number of 11 pigs. Each HSI image features a resolution of 480 × 640 pixels acquired over the 500–1000 nm wavelength range. The acquisition protocol has been designed such that the variability of organ spectra as a function of several parameters including the camera angle and the individual can be assessed. A comprehensive technical validation confirmed both the quality of the raw data and the annotations. We envision potential reuse within this dataset, but also its reuse as baseline data for future research questions outside this dataset. Measurement(s) Spectral Reflectance Technology Type(s) Hyperspectral Imaging Sample Characteristic - Organism Sus scrofa

Novel optical imaging techniques, such as hyperspectral imaging (HSI) combined with machine learning-based (ML) analysis, have the potential to revolutionize clinical surgical imaging. However, these novel modalities face a shortage of large-scale, representative clinical data for training ML algorithms, while preclinical animal data is abundantly available through standardized experiments and allows for controlled induction of pathological tissue states, which is not ethically possible in patients. To leverage this situation, we propose a novel concept called \"xeno-learning\", a cross-species knowledge transfer paradigm inspired by xeno-transplantation, where organs from a donor species are transplanted into a recipient species. Using a total of 11,268 HSI images from humans as well as porcine and rat models, we show that although spectral signatures of organs differ across species, shared pathophysiological mechanisms manifest as comparable relative spectral changes across species. Such changes learnt in one species can thus be transferred to a new species via a novel \"physiology-based data augmentation\" method, enabling the large-scale secondary use of preclinical animal data for humans. The resulting ethical, monetary, and performance benefits of the proposed knowledge transfer paradigm promise a high impact of the methodology on future developments in the field.