Generation of a Realistic Synthetic Laryngeal Cancer Cohort for AI Applications.

BACKGROUND: Obtaining large amounts of real patient data involves great efforts and expenses, and processing this data is fraught with data protection concerns. Consequently, data sharing might not always be possible, particularly when large, open science datasets are needed, as for AI development. For such purposes, the generation of realistic synthetic data may be the solution. Our project aimed to generate realistic cancer data with the use case of laryngeal cancer. METHODS: We used the open-source software Synthea and programmed an additional module for development, treatment and follow-up for laryngeal cancer by using external, real-world (RW) evidence from guidelines and cancer registries from Germany. To generate an incidence-based cohort view, we randomly drew laryngeal cancer cases from the simulated population and deceased persons, stratified by the real-world age and sex distributions at diagnosis. RESULTS: A module with age- and stage-specific treatment and prognosis for laryngeal cancer was successfully implemented. The synthesized population reflects RW prevalence well, extracting a cohort of 50,000 laryngeal cancer patients. Descriptive data on stage-specific and 5-year overall survival were in accordance with published data. CONCLUSIONS: We developed a large cohort of realistic synthetic laryngeal cancer cases with Synthea. Such data can be shared and published open source without data protection issues.

Stroke survivors' preferences on assessing patient-reported outcome measures.

BACKGROUND: To assess quality of life and unmet needs after stroke, patient-reported outcome measures (PROMs) have gained increasing attention. However, patients' perspectives on assessing PROMs remain unclear, potentially hindering implementation into clinical practice. Therefore, this study explored patients' preferences on assessing PROMs after ischemic stroke. METHODS: A paper-based questionnaire was sent to stroke survivors treated at the Department of Neurology, University of Leipzig, Germany. Health-related quality of life (HRQoL, EQ-5D-5L) and preferences regarding different aspects of data collection to assess PROMs were investigated and linked to socio-demographic and medical characteristics. RESULTS: 158 persons were contacted and 80 replies were subsequently analyzed. Mean age was 70.16 years and mean HRQoL was 68.79 (visual analogue scale with a theoretical maximum of 100). Participants showed positive attitudes towards PROMs as they saw potential to improve care of other patients (n = 66/79; 83.54%) or to improve their own situation (n = 53/74; 71.62%). Participants preferred an annual interview after stroke (n = 39/80; 48.75%) and would preferably spend 15-30 min (n = 41/79; 51.90%) to answer a written survey (n = 69/80; 86.25%). The initially treating clinic was preferred as initiator of such surveys (n = 43/79; 54.43%). Stratification revealed that participants with more than 1 h of daily digital media usage preferred email as way of communication. CONCLUSIONS: For the first time, this study showed individual preferences on assessing PROMs after ischemic stroke, focusing on the way, time interval, duration, and initiation site of surveys. These insights might help to successfully implement PROMs after stroke and subsequently detect unmet needs and deficits in stroke care.

A comparison between a maximum care university hospital and an outpatient clinic - potential for optimization in arthroscopic workflows?

BACKGROUND: Due to the growing economic pressure, there is an increasing interest in the optimization of operational processes within surgical operating rooms (ORs). Surgical departments are frequently dealing with limited resources, complex processes with unexpected events as well as constantly changing conditions. In order to use available resources efficiently, existing workflows and processes have to be analyzed and optimized continuously. Structural and procedural changes without prior data-driven analyses may impair the performance of the OR team and the overall efficiency of the department. The aim of this study is to develop an adaptable software toolset for surgical workflow analysis and perioperative process optimization in arthroscopic surgery. METHODS: In this study, the perioperative processes of arthroscopic interventions have been recorded and analyzed subsequently. A total of 53 arthroscopic operations were recorded at a maximum care university hospital (UH) and 66 arthroscopic operations were acquired at a special outpatient clinic (OC). The recording includes regular perioperative processes (i.a. patient positioning, skin incision, application of wound dressing) and disruptive influences on these processes (e.g. telephone calls, missing or defective instruments, etc.). For this purpose, a software tool was developed ('s.w.an Suite Arthroscopic toolset'). Based on the data obtained, the processes of the maximum care provider and the special outpatient clinic have been analyzed in terms of performance measures (e.g. Closure-To-Incision-Time), efficiency (e.g. activity duration, OR resource utilization) as well as intra-process disturbances and then compared to one another. RESULTS: Despite many similar processes, the results revealed considerable differences in performance indices. The OC required significantly less time than UH for surgical preoperative (UH: 30:47 min, OC: 26:01 min) and postoperative phase (UH: 15:04 min, OC: 9:56 min) as well as changeover time (UH: 32:33 min, OC: 6:02 min). In addition, these phases result in the Closure-to-Incision-Time, which lasted longer at the UH (UH: 80:01 min, OC: 41:12 min). CONCLUSION: The perioperative process organization, team collaboration, and the avoidance of disruptive factors had a considerable influence on the progress of the surgeries. Furthermore, differences in terms of staffing and spatial capacities could be identified. Based on the acquired process data (such as the duration for different surgical steps or the number of interfering events) and the comparison of different arthroscopic departments, approaches for perioperative process optimization to decrease the time of work steps and reduce disruptive influences were identified.

In vitro evaluation of a computer-assisted decision support system for the primary care of polytrauma patients.

INTRODUCTION: The management of polytraumatized patients is set in a stressful environment with numerous critical decisions in a brief amount of time. Working along a standardised procedure can improve the outcome for these patients and reduce mortality. To help clinical practitioners, we developed "TraumaFlow", a workflow management system for the primary care of polytrauma patients based on the current treatment guidelines. This study sought to validate the system and investigate its effect on user performance and perceived workload. METHODS: The computer-assisted decision support system was tested in two scenarios in a trauma room of a level 1 trauma centre by 11 final-year medical students and 3 residents. In simulated polytrauma scenarios, the participants functioned as a trauma leader. The first scenario was performed without decision support and the second with support by "TraumaFlow" via tablet. During each scenario, the performance was evaluated in a standardized assessment. After each scenario, the participants answered a questionnaire on workload [NASA Raw Task Load Index (NASA RTLX)]. RESULTS: In total, 14 participants (mean 28 ± 4 years, 43% female) managed 28 scenarios. During the first scenario without computer-assisted support, the participants achieved a mean of 6.6 out of 12 points (SD 1.2, range 5 to 9). With the support of TraumaFlow, the mean performance score was significantly higher with 11.6 out of 12 points (SD 0.5, range 11 to 12, p < 0.001). In the 14 scenarios performed without support, there was no run in which no errors were made. In comparison, ten of the 14 scenarios performed with TraumaFlow ran free of relevant errors. The mean improvement in the performance score was 42%. There was a significant decrease in the mean self-reported mental stress level in scenarios with support of TraumaFlow (55, SD 24) as compared to scenarios without support (72, SD 13, p = 0.041). CONCLUSION: In a simulated environment, computer-assisted decision-making improved the performance of the trauma leader, helped to adhere to clinical guidelines, and reduced stress in a fast-acting environment. In reality, this may improve the treatment outcome for the patient.

Data Exchange Between Ambulance and Trauma Center: Interview Study About the Needs of Emergency Trauma Room Staff.

BACKGROUND: In emergency trauma room, adequate preparation of all resources prior to the patient's arrival is essential to ensure optimal continuation of the treatment. Therefore, a good transfer of information between pre-hospital and hospital is very important, for example through networking technologies. OBJECTIVES: The aim is to identify what pre-hospital information is needed to ensure that all necessary resources in the ETR are optimally prepared for the incoming trauma patient. METHODS: A qualitative, semi structured interview was conducted with physicians of ETR team at four trauma centers. RESULTS: Physicians mentioned similar requests for pre-hospital information. The workflow in ETRs differed in alerting of team members and transferring of pre-notification information. CONCLUSION: Clinical needs for pre-hospital information for future development of support systems in the networking of accident site and hospital could be identified.

Using Digital Twins to Support Multiple Stages of the Patient Journey.

New possibilities in personalized medicine need to be complemented by clinical decision support systems as well as context-specific applications to be used in clinical routine. We aim to implement a shared technical backend for a large variety of applications in personalized head-and-neck cancer treatment. The infrastructure is conceptualized as a multi-purpose digital twin for cancer treatment. A set of prototypes of clinical applications demonstrates the feasibility of using digital twins to support multiple stages of the patient journey.

Impact of Pre- and Post-Processing Steps for Supervised Classification of Colorectal Cancer in Hyperspectral Images.

BACKGROUND: Recent studies have shown that hyperspectral imaging (HSI) combined with neural networks can detect colorectal cancer. Usually, different pre-processing techniques (e.g., wavelength selection and scaling, smoothing, denoising) are analyzed in detail to achieve a well-trained network. The impact of post-processing was studied less. METHODS: We tested the following methods: (1) Two pre-processing techniques (Standardization and Normalization), with (2) Two 3D-CNN models: Inception-based and RemoteSensing (RS)-based, with (3) Two post-processing algorithms based on median filter: one applies a median filter to a raw predictions map, the other applies the filter to the predictions map after adopting a discrimination threshold. These approaches were evaluated on a dataset that contains ex vivo hyperspectral (HS) colorectal cancer records of 56 patients. RESULTS: (1) Inception-based models perform better than RS-based, with the best results being 92% sensitivity and 94% specificity; (2) Inception-based models perform better with Normalization, RS-based with Standardization; (3) Our outcomes show that the post-processing step improves sensitivity and specificity by 6.6% in total. It was also found that both post-processing algorithms have the same effect, and this behavior was explained. CONCLUSION: HSI combined with tissue classification algorithms is a promising diagnostic approach whose performance can be additionally improved by the application of the right combination of pre- and post-processing.

Laparoscopic Video Analysis Using Temporal, Attention, and Multi-Feature Fusion Based-Approaches.

Adapting intelligent context-aware systems (CAS) to future operating rooms (OR) aims to improve situational awareness and provide surgical decision support systems to medical teams. CAS analyzes data streams from available devices during surgery and communicates real-time knowledge to clinicians. Indeed, recent advances in computer vision and machine learning, particularly deep learning, paved the way for extensive research to develop CAS. In this work, a deep learning approach for analyzing laparoscopic videos for surgical phase recognition, tool classification, and weakly-supervised tool localization in laparoscopic videos was proposed. The ResNet-50 convolutional neural network (CNN) architecture was adapted by adding attention modules and fusing features from multiple stages to generate better-focused, generalized, and well-representative features. Then, a multi-map convolutional layer followed by tool-wise and spatial pooling operations was utilized to perform tool localization and generate tool presence confidences. Finally, the long short-term memory (LSTM) network was employed to model temporal information and perform tool classification and phase recognition. The proposed approach was evaluated on the Cholec80 dataset. The experimental results (i.e., 88.5% and 89.0% mean precision and recall for phase recognition, respectively, 95.6% mean average precision for tool presence detection, and a 70.1% F1-score for tool localization) demonstrated the ability of the model to learn discriminative features for all tasks. The performances revealed the importance of integrating attention modules and multi-stage feature fusion for more robust and precise detection of surgical phases and tools.

Alternative intraoperative optical imaging modalities for fluorescence angiography in gastrointestinal surgery: spectral imaging and imaging photoplethysmography.

INTRODUCTION: Intraoperative near-infrared fluorescence angiography with indocyanine green (ICG-FA) is a well-established modality in gastrointestinal surgery. Its main drawback is the application of a fluorescent agent with possible side effects for patients. The goal of this review paper is the presentation of alternative, non-invasive optical imaging methods and their comparison with ICG-FA. MATERIAL AND METHODS: The principles of ICG-FA, spectral imaging, imaging photoplethysmography (iPPG), and their applications in gastrointestinal surgery are described based on selected published works. RESULTS: The main applications of the three modalities are the evaluation of tissue perfusion, the identification of risk structures, and tissue segmentation or classification. While the ICG-FA images are mainly evaluated visually, leading to subjective interpretations, quantitative physiological parameters and tissue segmentation are provided in spectral imaging and iPPG. The combination of ICG-FA and spectral imaging is a promising method. CONCLUSIONS: Non-invasive spectral imaging and iPPG have shown promising results in gastrointestinal surgery. They can overcome the main drawbacks of ICG-FA, i.e. the use of contrast agents, the lack of quantitative analysis, repeatability, and a difficult standardization of the acquisition. Further technical improvements and clinical evaluations are necessary to establish them in daily clinical routine.

A Treatment Decision Support Model for Laryngeal Cancer Based on Bayesian Networks.

The increase in diagnostic and therapeutic procedures in the treatment of oncological diseases, as well as the limited capacity of experts to provide information, necessitates the development of therapy decision support systems (TDSS). We have developed a treatment decision model that integrates available patient information as well as tumor characteristics. They are assessed according to their relevance in evaluating the optimal therapy option. Our treatment model is based on Bayesian networks (BN) which integrate patient-specific data with expert-based implemented causalities to suggest the optimal therapy option and therefore potentially support the decision-making process for treatment of laryngeal carcinoma. To test the reliability of our model, we compared the calculations of our model with the documented therapy from our data set, which contained information on 97 patients with laryngeal carcinoma. Information on 92 patients was used in our analyses and the model suggested the correct treatment in 419 out of 460 treatment modalities (accuracy of 91%). However, unequally distributed clinical data in the test sets revealed weak spots in the model that require revision for future utilization.

Spectral Similarity Measures for In Vivo Human Tissue Discrimination Based on Hyperspectral Imaging.

PROBLEM: Similarity measures are widely used as an approved method for spectral discrimination or identification with their applications in different areas of scientific research. Even though a range of works have been presented, only a few showed slightly promising results for human tissue, and these were mostly focused on pathological and non-pathological tissue classification. METHODS: In this work, several spectral similarity measures on hyperspectral (HS) images of in vivo human tissue were evaluated for tissue discrimination purposes. Moreover, we introduced two new hybrid spectral measures, called SID-JM-TAN(SAM) and SID-JM-TAN(SCA). We analyzed spectral signatures obtained from 13 different human tissue types and two different materials (gauze, instruments), collected from HS images of 100 patients during surgeries. RESULTS: The quantitative results showed the reliable performance of the different similarity measures and the proposed hybrid measures for tissue discrimination purposes. The latter produced higher discrimination values, up to 6.7 times more than the classical spectral similarity measures. Moreover, an application of the similarity measures was presented to support the annotations of the HS images. We showed that the automatic checking of tissue-annotated thyroid and colon tissues was successful in 73% and 60% of the total spectra, respectively. The hybrid measures showed the highest performance. Furthermore, the automatic labeling of wrongly annotated tissues was similar for all measures, with an accuracy of up to 90%. CONCLUSION: In future work, the proposed spectral similarity measures will be integrated with tools to support physicians in annotations and tissue labeling of HS images.

Analysing multi-perspective patient-related data during laparoscopic gynaecology procedures.

Fusing data from different medical perspectives inside the operating room (OR) sets the stage for developing intelligent context-aware systems. These systems aim to promote better awareness inside the OR by keeping every medical team well informed about the work of other teams and thus mitigate conflicts resulting from different targets. In this research, a descriptive analysis of data collected from anaesthesiology and surgery was performed to investigate the relationships between the intra-abdominal pressure (IAP) and lung mechanics for patients during laparoscopic procedures. Data of nineteen patients who underwent laparoscopic gynaecology were included. Statistical analysis of all subjects showed a strong relationship between the IAP and dynamic lung compliance (r = 0.91). Additionally, the peak airway pressure was also strongly correlated to the IAP in volume-controlled ventilated patients (r = 0.928). Statistical results obtained by this study demonstrate the importance of analysing the relationship between surgical actions and physiological responses. Moreover, these results form the basis for developing medical decision support models, e.g., automatic compensation of IAP effects on lung function.

[BIOPASS hybrid navigation for endoscopic sinus surgery - an assistance system]. / BIOPASS-Hybrid-Navigation für die endoskopische Nasennebenhöhlenchirurgie ­ ein Assistenzsystem.

Previous navigation systems can determine the position of the "tracked" surgical instrument in CT images in the context of functional endoscopic sinus surgery (FESS), but do not provide any assistance directly in the video endoscopic image of the surgeon. Developing this direct assistance for intraoperative orientation and risk reduction was the goal of the BIOPASS project (Bild Ontologie und prozessgestütztes Assistenzsystem). The Project pursues the development of a novel navigation system for FESS without markers. BIOPASS describes a hybrid system that integrates various sensor data and makes it available. The goal is to abandon tracking and exclusively provide navigation information directly in the video image. This paper describes the first step of the development by collecting and structuring the surgical phases (workflows), the video endoscopic landmarks and a first clinical evaluation of the model version. The results provide the important basis and platform for the next step of the project.

A digital twin model for evidence-based clinical decision support in multiple myeloma treatment.

The treatment landscape for multiple myeloma (MM) has experienced substantial progress over the last decade. Despite the efficacy of new substances, patient responses tend to still be highly unpredictable. With increasing cognitive burden that is introduced through a complex and evolving treatment landscape, data-driven assistance tools are becoming more and more popular. Model-based approaches, such as digital twins (DT), enable simulation of probable responses to a set of input parameters based on retrospective observations. In the context of treatment decision-support, those mechanisms serve the goal to predict therapeutic outcomes to distinguish a favorable option from a potential failure. In the present work, we propose a similarity-based multiple myeloma digital twin (MMDT) that emphasizes explainability and interpretability in treatment outcome evaluation. We've conducted a requirement specification process using scientific literature from the medical and methodological domains to derive an architectural blueprint for the design and implementation of the MMDT. In a subsequent stage, we've implemented a four-layer concept where for each layer, we describe the utilized implementation procedure and interfaces to the surrounding DT environment. We further specify our solutions regarding the adoption of multi-line treatment strategies, the integration of external evidence and knowledge, as well as mechanisms to enable transparency in the data processing logic. Furthermore, we define an initial evaluation scenario in the context of patient characterization and treatment outcome simulation as an exemplary use case for our MMDT. Our derived MMDT instance is defined by 475 unique entities connected through 438 edges to form a MM knowledge graph. Using the MMRF CoMMpass real-world evidence database and a sample MM case, we processed a complete outcome assessment. The output shows a valid selection of potential treatment strategies for the integrated medical case and highlights the potential of the MMDT to be used for such applications. DT models face significant challenges in development, including availability of clinical data to algorithmically derive clinical decision support, as well as trustworthiness of the evaluated treatment options. We propose a collaborative approach that mitigates the regulatory and ethical concerns that are broadly discussed when automated decision-making tools are to be included into clinical routine.

Topics Mentioned by Stroke Patients during Supportive Phone Calls-Implications for Individualized Aftercare Programs.

By understanding stroke as a chronic disease, aftercare becomes increasingly important. For developing aftercare programs, the patients' perspective regarding, for example, stroke-related symptoms and interactions with the healthcare system is necessary. Records from a local stroke pilot program were used to extract relevant topics from the patients' perspective, as mentioned during a phone call two months after hospital discharge. Data from 157 patients with ischemic stroke or transient ischemic attack (TIA) were included. "Rehabilitation" was mentioned by 67.5% of patients, followed by "specialist physician", "symptoms", and "medication". Compared with severely disabled patients, those with no relevant disability at hospital discharge mentioned "symptoms" significantly more often. Regarding rehabilitation, "outpatient care" was mentioned more often by patients in an inpatient setting, and 11.8% without rehabilitation mentioned "depression". Patients in single-compared to multi-person households differed, for example, in the frequency of mentioning "specialist physicians" and gradually "outpatient care". A multivariate model yielded associations between the disability at discharge and the probability of mentioning relevant topics afterward. This study provided insights into the patients' perspective and identified topics that need attention while accompanying stroke and TIA patients after discharge. Further, the degree of disability at discharge might be helpful for planning individual aftercare.

Ontology-based surgical workflow recognition and prediction.

BACKGROUND: Surgical context-aware systems can adapt to the current situation in the operating room and thus provide computer-aided assistance functionalities and intraoperative decision-support. To interact with the surgical team perceptively and assist the surgical process, the system needs to monitor the intraoperative activities, understand the current situation in the operating room at any time, and anticipate the following possible situations. METHODS: A structured representation of surgical process knowledge is a prerequisite for any applications in the intelligent operating room. For this purpose, a surgical process ontology, which is formally based on standard medical terminology (SNOMED CT) and an upper-level ontology (GFO), was developed and instantiated for a neurosurgical use case. A new ontology-based surgical workflow recognition and a novel prediction method are presented utilizing ontological reasoning, abstraction, and explication. This way, a surgical situation representation with combined phase, high-level task, and low-level task recognition and prediction was realized based on the currently used instrument as the only input information. RESULTS: The ontology-based approach performed efficiently, and decent accuracy was achieved for situation recognition and prediction. Especially during situation recognition, the missing sensor information were reasoned based on the situation representation provided by the process ontology, which resulted in improved recognition results compared to the state-of-the-art. CONCLUSIONS: In this work, a reference ontology was developed, which provides workflow support and a knowledge base for further applications in the intelligent operating room, for instance, context-aware medical device orchestration, (semi-) automatic documentation, and surgical simulation, education, and training.

Design and validation of a medical robotic device system to control two collaborative robots for ultrasound-guided needle insertions.

The percutaneous biopsy is a critical intervention for diagnosis and staging in cancer therapy. Robotic systems can improve the efficiency and outcome of such procedures while alleviating stress for physicians and patients. However, the high complexity of operation and the limited possibilities for robotic integration in the operating room (OR) decrease user acceptance and the number of deployed robots. Collaborative systems and standardized device communication may provide approaches to overcome named problems. Derived from the IEEE 11073 SDC standard terminology of medical device systems, we designed and validated a medical robotic device system (MERODES) to access and control a collaborative setup of two KUKA robots for ultrasound-guided needle insertions. The system is based on a novel standard for service-oriented device connectivity and utilizes collaborative principles to enhance user experience. Implementing separated workflow applications allows for a flexible system setup and configuration. The system was validated in three separate test scenarios to measure accuracies for 1) co-registration, 2) needle target planning in a water bath and 3) in an abdominal phantom. The co-registration accuracy averaged 0.94 ± 0.42 mm. The positioning errors ranged from 0.86 ± 0.42 to 1.19 ± 0.70 mm in the water bath setup and from 1.69 ± 0.92 to 1.96 ± 0.86 mm in the phantom. The presented results serve as a proof-of-concept and add to the current state of the art to alleviate system deployment and fast configuration for percutaneous robotic interventions.

Comparison of image registration methods for combining laparoscopic video and spectral image data.

Laparoscopic procedures can be assisted by intraoperative modalities, such as quantitative perfusion imaging based on fluorescence or hyperspectral data. If these modalities are not available at video frame rate, fast image registration is needed for the visualization in augmented reality. Three feature-based algorithms and one pre-trained deep homography neural network (DH-NN) were tested for single and multi-homography estimation. Fine-tuning was used to bridge the domain gap of the DH-NN for non-rigid registration of laparoscopic images. The methods were validated on two datasets: an open-source record of 750 manually annotated laparoscopic images, presented in this work, and in-vivo data from a novel laparoscopic hyperspectral imaging system. All feature-based single homography methods outperformed the fine-tuned DH-NN in terms of reprojection error, Structural Similarity Index Measure, and processing time. The feature detector and descriptor ORB1000 enabled video-rate registration of laparoscopic images on standard hardware with submillimeter accuracy.

Web-Based Patient-Reported Outcomes for ENT Patients-Evaluation of the Status Quo, Patients' View, and Future Perspectives.

BACKGROUND: Patient-reported outcomes (PRO) assess disease burden and indicate unmet needs. Home-based electronic PRO measures (ePROMs) can support tumor aftercare (TAC). Creating an ePROM is the next step after implementing the software "OncoFunction" to assess PROs during TAC of head- and neck-cancer patients (HNC). Therefore, internet use and perception on ePROMs of ENT and TAC patients were evaluated. METHODS: From May-July 2020, ENT patients at a high-volume outpatient department aged >18 without need for emergency treatment were invited to complete a questionnaire concerning internet use and access, hardware, and opinion on the chances, requirements, and designs of ePROMs. RESULTS: 415 questionnaires were evaluated; 46.3% of the respondents visited the common consultation hour (CCH) and 44.3% TAC; 71.9% were internet users, being younger than non-internet users; and 36.4% of TAC patients were non-internet users and 16.3% of them were without a web-enabled device. Significant differences existed in age and assessment of future perspectives between internet-/non-internet users and TAC/CCH patients, respectively. Regarding the design of ePROMs, patients preferred quarterly and short surveys. Data safety and feedback were important. CONCLUSIONS: ePROMs are not suitable for everyone because of missing internet access and experience. A tailored approach to implement ePROMs in TAC is needed.

[Artificial intelligence and hyperspectral imaging for image-guided assistance in minimally invasive surgery]. / Künstliche Intelligenz und hyperspektrale Bildgebung zur bildgestützten Assistenz in der minimal-invasiven Chirurgie.

BACKGROUND: Intraoperative imaging assists surgeons during minimally invasive procedures. Hyperspectral imaging (HSI) is a noninvasive and noncontact optical technique with great diagnostic potential in medicine. The combination with artificial intelligence (AI) approaches to analyze HSI data is called intelligent HSI in this article. OBJECTIVE: What are the medical applications and advantages of intelligent HSI for minimally invasive visceral surgery? MATERIAL AND METHODS: Within various clinical studies HSI data from multiple in vivo tissue types and oncological resections were acquired using an HSI camera system. Different AI algorithms were evaluated for detection and discrimination of organs, risk structures and tumors. RESULTS: In an experimental animal study 20 different organs could be differentiated with high precision (> 95%) using AI. In vivo, the parathyroid glands could be discriminated from surrounding tissue with an F1 score of 47% and sensitivity of 75%, and the bile duct with an F1 score of 79% and sensitivity of 90%. Furthermore, ex vivo tumor tissue could be successfully detected with an area under the receiver operating characteristic (ROC) curve (AUC) larger than 0.91. DISCUSSION: This study demonstrates that intelligent HSI can automatically and accurately detect different tissue types. Despite great progress in the last decade intelligent HSI still has limitations. Thus, accurate AI algorithms that are easier to understand for the user and an extensive standardized and continuously growing database are needed. Further clinical studies should support the various medical applications and lead to the adoption of intelligent HSI in the clinical routine practice.