6G in medical robotics: development of network allocation strategies for a telerobotic examination system.

PURPOSE: Healthcare systems around the world are increasingly facing severe challenges due to problems such as staff shortage, changing demographics and the reliance on an often strongly human-dependent environment. One approach aiming to address these issues is the development of new telemedicine applications. The currently researched network standard 6G promises to deliver many new features which could be beneficial to leverage the full potential of emerging telemedical solutions and overcome the limitations of current network standards. METHODS: We developed a telerobotic examination system with a distributed robot control infrastructure to investigate the benefits and challenges of distributed computing scenarios, such as fog computing, in medical applications. We investigate different software configurations for which we characterize the network traffic and computational loads and subsequently establish network allocation strategies for different types of modular application functions (MAFs). RESULTS: The results indicate a high variability in the usage profiles of these MAFs, both in terms of computational load and networking behavior, which in turn allows the development of allocation strategies for different types of MAFs according to their requirements. Furthermore, the results provide a strong basis for further exploration of distributed computing scenarios in medical robotics. CONCLUSION: This work lays the foundation for the development of medical robotic applications using 6G network architectures and distributed computing scenarios, such as fog computing. In the future, we plan to investigate the capability to dynamically shift MAFs within the network based on current situational demand, which could help to further optimize the performance of network-based medical applications and play a role in addressing the increasingly critical challenges in healthcare.

Role of the serosa in intestinal anastomotic healing: insights from in-depth histological analysis of human and murine anastomoses.

BACKGROUND: Anastomotic leakage following colorectal surgery remains a significant complication despite advances in surgical techniques. Recent findings on serosal injury repair in coelomic cavities, such as the peritoneum, challenge the current understanding of the cellular origins and mechanisms underlying intestinal anastomotic healing. Understanding the contribution of each layer of the intestinal wall during anastomotic healing is needed to find new therapeutic strategies to prevent anastomotic leakage. The aim of this experimental study was to investigate the role of the serosal layer of the intestinal wall in anastomotic healing. MATERIALS AND METHODS: Comprehensive histologic analysis of human and murine anastomoses was performed to elucidate histologic changes in the different intestinal layers during anastomotic healing. In vivo staining of the extracellular matrix (ECM) in the serosal layer was performed using a fluorophore-conjugated N-hydroxysuccinimide-ester before anastomosis surgery in a murine model. RESULTS: Histological examination of both human and murine anastomoses revealed that closure of the serosal layer occurred first during the healing process. In vivo serosal ECM staining demonstrated that a significant portion of the newly formed ECM within the anastomosis was indeed deposited onto the serosal layer. Furthermore, mesenchymal cells within the anastomotic scar were positive for mesothelial cell markers, podoplanin and Wilms tumour protein. CONCLUSIONS: In this experimental study, the results suggest that serosal scar formation is an important mechanism for anastomotic integrity in intestinal anastomoses. Mesothelial cells may significantly contribute to scar formation during anastomotic healing through epithelial-to-mesenchymal transition, potentially suggesting a novel therapeutic target to prevent anastomotic leakage by enhancing physiological healing processes.

Development of a human machine interface for robotically assisted surgery optimized for laparoscopic workflows.

INTRODUCTION: In robotic-assisted surgery (RAS), the input device is the primary site for the flow of information between the user and the robot. Most RAS systems remove the surgeon's console from the sterile surgical site. Beneficial for performing lengthy procedures with complex systems, this ultimately lacks the flexibility that comes with the surgeon being able to remain at the sterile site. METHODS: A prototype of an input device for RAS is constructed. The focus lies on intuitive control for surgeons and a seamless integration into the surgical workflow within the sterile environment. The kinematic design is translated from the kinematics of laparoscopic surgery. The input device uses three degrees of freedom from a flexible instrument as input. The prototype's performance is compared to that of a commercially available device in an evaluation. Metrics are used to evaluate the surgeons' performance with the respective input device in a virtual environment implemented for the evaluation. RESULTS: The evaluation of the two input devices shows statistically significant differences in the performance metrics. With the proposed prototype, the surgeons perform the tasks faster, more precisely, and with fewer errors. CONCLUSION: The prototype is an efficient and intuitive input device for surgeons with laparoscopic experience. The placement in the sterile working area allows for seamless integration into the surgical workflow and can potentially enable new robotic approaches.

Robotic scrub nurse to anticipate surgical instruments based on real-time laparoscopic video analysis.

BACKGROUND: Machine learning and robotics technologies are increasingly being used in the healthcare domain to improve the quality and efficiency of surgeries and to address challenges such as staff shortages. Robotic scrub nurses in particular offer great potential to address staff shortages by assuming nursing tasks such as the handover of surgical instruments. METHODS: We introduce a robotic scrub nurse system designed to enhance the quality of surgeries and efficiency of surgical workflows by predicting and delivering the required surgical instruments based on real-time laparoscopic video analysis. We propose a three-stage deep learning architecture consisting of a single frame-, temporal multi frame-, and informed model to anticipate surgical instruments. The anticipation model was trained on a total of 62 laparoscopic cholecystectomies. RESULTS: Here, we show that our prediction system can accurately anticipate 71.54% of the surgical instruments required during laparoscopic cholecystectomies in advance, facilitating a smoother surgical workflow and reducing the need for verbal communication. As the instruments in the left working trocar are changed less frequently and according to a standardized procedure, the prediction system works particularly well for this trocar. CONCLUSIONS: The robotic scrub nurse thus acts as a mind reader and helps to mitigate staff shortages by taking over a great share of the workload during surgeries while additionally enabling an enhanced process standardization.

Staff shortages in healthcare are an emerging problem leading to undersupply of medical experts such as scrub nurses in the operating room. The absence of these scrub nurses, who are responsible for providing surgical instruments, means that surgeries must be postponed or canceled. Robotic technologies and artificial intelligence offer great potential to address staff shortages in the operating room. We developed a robotic scrub nurse system that is able to take over the tasks of a human scrub nurse by delivering the required surgical tools. To maintain the pace of the surgery, our robotic scrub nurse system is also capable of predicting these required surgical tools in advance using artificial intelligence. The system is tested on laparoscopic cholecystectomies, a surgery, where the gallbladder is removed in a minimally invasive technique. We show that our prediction system can predict the majority of surgical instruments for this specific surgery facilitating a smoother surgical workflow and reducing the need for verbal communication. With further development, our system may help to cover the need for surgery while streamlining the surgical process through predictive support, potentially improving patient outcomes.

Towards multimodal graph neural networks for surgical instrument anticipation.

PURPOSE: Decision support systems and context-aware assistance in the operating room have emerged as the key clinical applications supporting surgeons in their daily work and are generally based on single modalities. The model- and knowledge-based integration of multimodal data as a basis for decision support systems that can dynamically adapt to the surgical workflow has not yet been established. Therefore, we propose a knowledge-enhanced method for fusing multimodal data for anticipation tasks. METHODS: We developed a holistic, multimodal graph-based approach combining imaging and non-imaging information in a knowledge graph representing the intraoperative scene of a surgery. Node and edge features of the knowledge graph are extracted from suitable data sources in the operating room using machine learning. A spatiotemporal graph neural network architecture subsequently allows for interpretation of relational and temporal patterns within the knowledge graph. We apply our approach to the downstream task of instrument anticipation while presenting a suitable modeling and evaluation strategy for this task. RESULTS: Our approach achieves an F1 score of 66.86% in terms of instrument anticipation, allowing for a seamless surgical workflow and adding a valuable impact for surgical decision support systems. A resting recall of 63.33% indicates the non-prematurity of the anticipations. CONCLUSION: This work shows how multimodal data can be combined with the topological properties of an operating room in a graph-based approach. Our multimodal graph architecture serves as a basis for context-sensitive decision support systems in laparoscopic surgery considering a comprehensive intraoperative operating scene.

Extra-abdominal trocar and instrument detection for enhanced surgical workflow understanding.

PURPOSE: Video-based intra-abdominal instrument tracking for laparoscopic surgeries is a common research area. However, the tracking can only be done with instruments that are actually visible in the laparoscopic image. By using extra-abdominal cameras to detect trocars and classify their occupancy state, additional information about the instrument location, whether an instrument is still in the abdomen or not, can be obtained. This can enhance laparoscopic workflow understanding and enrich already existing intra-abdominal solutions. METHODS: A data set of four laparoscopic surgeries recorded with two time-synchronized extra-abdominal 2D cameras was generated. The preprocessed and annotated data were used to train a deep learning-based network architecture consisting of a trocar detection, a centroid tracker and a temporal model to provide the occupancy state of all trocars during the surgery. RESULTS: The trocar detection model achieves an F1 score of 95.06 ± 0.88 % . The prediction of the occupancy state yields an F1 score of 89.29 ± 5.29 % , providing a first step towards enhanced surgical workflow understanding. CONCLUSION: The current method shows promising results for the extra-abdominal tracking of trocars and their occupancy state. Future advancements include the enlargement of the data set and incorporation of intra-abdominal imaging to facilitate accurate assignment of instruments to trocars.

Model-based individual life-spanning documentation in visceral surgery: a proof of concept.

INTRODUCTION: Surgical documentation has many implications. However, its primary function is to transfer information about surgical procedures to other medical professionals. Thereby, written reports describing procedures in detail are the current standard, impeding comprehensive understanding of patient-individual life-spanning surgical course, especially if surgeries are performed at a timely distance and in diverse facilities. Therefore, we developed a novel model-based approach for documentation of visceral surgeries, denoted as 'Surgical Documentation Markup-Modeling' (SDM-M). MATERIAL AND METHODS: For scientific evaluation, we developed a web-based prototype software allowing for creating hierarchical anatomical models that can be modified by individual surgery-related markup information. Thus, a patient's cumulated 'surgical load' can be displayed on a timeline deploying interactive anatomical 3D models. To evaluate the possible impact on daily clinical routine, we performed an evaluation study with 24 surgeons and advanced medical students, elaborating on simulated complex surgical cases, once with classic written reports and once with our prototypical SDM-M software. RESULTS: Leveraging SDM-M in an experimental environment reduced the time needed for elaborating simulated complex surgical cases from 354 ± 85 s with the classic approach to 277 ± 128 s. (p = 0.00109) The perceived task load measured by the Raw NASA-TLX was reduced significantly (p = 0.00003) with decreased mental (p = 0.00004) and physical (p = 0.01403) demand. Also, time demand (p = 0.00041), performance (p = 0.00161), effort (p = 0.00024), and frustration (p = 0.00031) were improved significantly. DISCUSSION: Model-based approaches for life-spanning surgical documentation could improve the daily clinical elaboration and understanding of complex cases in visceral surgery. Besides reduced workload and time sparing, even a more structured assessment of individual surgical cases could foster improved planning of further surgeries, information transfer, and even scientific evaluation, considering the cumulative 'surgical load.' CONCLUSION: Life-spanning model-based documentation of visceral surgical cases could significantly improve surgery and workload.

Audio-based event detection in the operating room.

PURPOSE: Even though workflow analysis in the operating room has come a long way, current systems are still limited to research. In the quest for a robust, universal setup, hardly any attention has been given to the dimension of audio despite its numerous advantages, such as low costs, location, and sight independence, or little required processing power. METHODOLOGY: We present an approach for audio-based event detection that solely relies on two microphones capturing the sound in the operating room. Therefore, a new data set was created with over 63 h of audio recorded and annotated at the University Hospital rechts der Isar. Sound files were labeled, preprocessed, augmented, and subsequently converted to log-mel-spectrograms that served as a visual input for an event classification using pretrained convolutional neural networks. RESULTS: Comparing multiple architectures, we were able to show that even lightweight models, such as MobileNet, can already provide promising results. Data augmentation additionally improved the classification of 11 defined classes, including inter alia different types of coagulation, operating table movements as well as an idle class. With the newly created audio data set, an overall accuracy of 90%, a precision of 91% and a F1-score of 91% were achieved, demonstrating the feasibility of an audio-based event recognition in the operating room. CONCLUSION: With this first proof of concept, we demonstrated that audio events can serve as a meaningful source of information that goes beyond spoken language and can easily be integrated into future workflow recognition pipelines using computational inexpensive architectures.

The sound of surgery-development of an acoustic trocar system enabling laparoscopic sound analysis.

PURPOSE: Acoustic information can contain viable information in medicine and specifically in surgery. While laparoscopy depends mainly on visual information, our goal is to develop the means to capture and process acoustic information during laparoscopic surgery. METHODS: To achieve this, we iteratively developed three prototypes that will overcome the abdominal wall as a sound barrier and can be used with standard trocars. We evaluated them in terms of clinical applicability and sound transmission quality. Furthermore, the applicability of each prototype for sound classification based on machine learning was evaluated. RESULTS: Our developed prototypes for recording airborne sound from the intraperitoneal cavity represent a promising solution suitable for real-world clinical usage All three prototypes fulfill our set requirements in terms of clinical applicability (i.e., air-tightness, invasiveness, sterility) and show promising results regarding their acoustic characteristics and the associated results on ML-based sound classification. CONCLUSION: In summary, our prototypes for capturing acoustic information during laparoscopic surgeries integrate seamlessly with existing procedures and have the potential to augment the surgeon's perception. This advancement could change how surgeons interact with and understand the surgical field.

Fractal analysis of extracellular matrix for observer-independent quantification of intestinal fibrosis in Crohn's disease.

Prevention of intestinal fibrosis remains an unresolved problem in the treatment of Crohn's disease (CD), as specific antifibrotic therapies are not yet available. Appropriate analysis of fibrosis severity is essential for assessing the therapeutic efficacy of potential antifibrotic drugs. The aim of this study was to develop an observer-independent method to quantify intestinal fibrosis in surgical specimens from patients with CD using structural analysis of the extracellular matrix (ECM). We performed fractal analysis in fibrotic and control histological sections of patients with surgery for CD (n = 28). To specifically assess the structure of the collagen matrix, polarized light microscopy was used. A score to quantify collagen fiber alignment and the color of the polarized light was established. Fractal dimension as a measure for the structural complexity correlated significantly with the histological fibrosis score whereas lacunarity as a measure for the compactness of the ECM showed a negative correlation. Polarized light microscopy to visualize the collagen network underlined the structural changes in the ECM network in advanced fibrosis. In conclusion, observer-independent quantification of the structural complexity of the ECM by fractal analysis is a suitable method to quantify the degree of intestinal fibrosis in histological samples from patients with CD.

Development of an image-based Random Forest classifier for prediction of surgery duration of laparoscopic sigmoid resections.

PURPOSE: Sigmoid diverticulitis is a disease with a high socioeconomic burden, accounting for a high number of left-sided colonic resections worldwide. Modern surgical scheduling relies on accurate prediction of operation times to enhance patient care and optimize healthcare resources. This study aims to develop a predictive model for surgery duration in laparoscopic sigmoid resections, based on preoperative CT biometric and demographic patient data. METHODS: This retrospective single-center cohort study included 85 patients who underwent laparoscopic sigmoid resection for diverticular disease. Potentially relevant procedure-specific anatomical parameters recommended by a surgical expert were measured in preoperative CT imaging. After random split into training and test set (75% / 25%) multiclass logistic regression was performed and a Random Forest classifier was trained on CT imaging parameters, patient age, and sex in the training cohort to predict categorized surgery duration. The models were evaluated in the test cohort using established performance metrics including receiver operating characteristics area under the curve (AUROC). RESULTS: The Random Forest model achieved a good average AUROC of 0.78. It allowed a very good prediction of long (AUROC = 0.89; specificity 0.71; sensitivity 1.0) and short (AUROC = 0.81; specificity 0.77; sensitivity 0.56) procedures. It clearly outperformed the multiclass logistic regression model (AUROC: average = 0.33; short = 0.31; long = 0.22). CONCLUSION: A Random Forest classifier trained on demographic and CT imaging biometric patient data could predict procedure duration outliers of laparoscopic sigmoid resections. Pending validation in a multicenter study, this approach could potentially improve procedure scheduling in visceral surgery and be scaled to other procedures.

Autonomous swab robot for naso- and oropharyngeal COVID-19 screening.

The COVID-19 outbreak has triggered a global health and economic crisis, necessitating widespread testing to control viral spread amidst rising cases and fatalities. The recommended testing method, a combined naso- and oropharyngeal swab, poses risks and demands limited protective gear. In response to the COVID-19 pandemic, we developed and tested the first autonomous swab robot station for Naso- and Oropharyngeal Coronavirus Screening (SR-NOCS). A force-sensitive robot running under a Cartesian impedance controller is employed to drive the swab to the sampling area. This groundbreaking device underwent two clinical studies-one conducted during the initial pandemic lockdown in Europe (early 2021) and the other, more recently, in a public place after the pandemic had subsided earlier in the year 2023. In total, 52 patients suspected of COVID-19 infection were included in these clinical studies. The results revealed a complete positive correlation between autonomous and manual sampling. The test subjects exhibited a high acceptance rate, all expressing a willingness to undergo future tests with SR-NOCS. Based on our findings, such systems could enhance testing capabilities, potentially conducting up to 300 tests per robot per day with consistent precision. The tests can be carried out with minimal supervision, reducing infection risks and effectively safeguarding patients and healthcare workers.

Toward telemedical diagnostics-clinical evaluation of a robotic examination system for emergency patients.

Introduction: The SARS-CoV-2 pandemic has affected global public healthcare for several years. Numerous medical professionals have been infected since the outbreak in 2019, resulting in a shortage of healthcare providers. Since traditional personal protective wear was insufficient to eliminate the virus transmission reliably, new strategies to avoid cross-infection were imperative while enabling high-quality medical care. In the project ProteCT, we investigated the potential of robotic-assisted examination in providing medical examination via a telemedical approach. Material and Methods: We constructed a fully functional examination cabin equipped with cameras, microphones, screens and robotic arms to evaluate usability and perception. Therefore, we conducted a preliminary study with 10 healthy volunteers and 10 physicians to gain first insights and optimize the setup. In a second step, we performed telemedical examinations of actual patients from the local emergency department to compare the robotic approach with the classical method of measuring vital signs, auscultation, palpation and percussion. Results: The preliminary study identified basic requirements, such as the need for force-feedback and telemedical training for physicians. In the main study, acceptance was high and most patients indicated they would use a telemedical system again. Our setup enabled the physician to make the same diagnoses as by classic examination in the emergency department in most cases. Discussion: The potential acceptance of a telemedical system such as ProteCT is high. Robotic telemedical approaches could complement future healthcare beyond the Corona pandemic to reach rural areas or even war zones. Moreover, the daily clinical use of robotic telemedicine could improve patients' safety, the quality of perioperative management and the workflow in any medical facility. Conclusion: The development of telemedical and telerobotic systems is a multidisciplinary and complex challenge. However, acceptance of the proposed system was high among patients and physicians, indicating the potential use of similar systems for future healthcare.

[Mobile C-arm-Radiation exposure and workflow killer? : Potential of an innovative assistance system for intraoperative positioning]. / Mobiler C-Bogen ­ Strahlenbelastung und Workflow-Killer? : Potenzial eines innovativen Assistenzsystems für die intraoperative Positionierung.

Despite its versatile applicability the intraoperative use of a mobile C­arm is often problematic and potentially associated with increased radiation exposure for both the patient and the personnel. In particular, the correct positioning for adequate imaging can become a problem as the nonsterile circulating nurse has to coordinate the various maneuvers together with the surgeon without having a good view of the surgical field. The sluggishness of the equipment and the intraoperative setting (sterile borders, additional hardware, etc.) pose further challenges. A light detection and ranging (LIDAR)-based assistance system shows promise to provide accurate and intuitive repositioning support as part of an initial series of experimental trials. For this purpose, the sensors are attached to the C­arm base unit and enable navigation of the device in the operating room to a stored target position using a simultaneous localization and mapping (SLAM) algorithm. An improvement of the workflow as well as a reduction of radiation exposure represent the possible potential of this system. The advantages over other experimental approaches are the lack of external hardware and the ease of use without isolating the operator from the rest of the operating room environment; however, the suitability for daily use in the presence of additional interfering factors should be verified in further studies.

Versatile end effector for laparoscopic robotic scrub nurse.

PURPOSE: Integrating robotic scrub nurses in the operating room has the potential to help overcome staff shortages and limited use of available operating capacities in hospitals. Existing approaches of robotic scrub nurses are mainly focused on open surgical procedures, neglecting laparoscopic procedures. Laparoscopic interventions offer great potential for the context-sensitive integration of robotic systems due to possible standardization. However, the first step is to ensure the safe manipulation of laparoscopic instruments. METHODS: A robotic platform with a universal gripper system was designed to pick up and place laparoscopic as well as da Vinci[Formula: see text] instruments in an efficient workflow. The robustness of the gripper system was studied using a test protocol, which included a force absorption test to determine the operational safety limits of the design and a grip test to determine the system performance. RESULTS: The test protocol shows results regarding force and torque absorption capabilities of the end effector, which are essential when transferring an instrument to the surgeon to enable a robust handover. The grip tests show that the laparoscopic instruments can be safely picked up, manipulated and returned independent of unexpected positional deviations. The gripper system also enables the manipulation of da Vinci[Formula: see text] instruments, opening the door for robot-robot interaction. CONCLUSION: Our evaluation tests have shown that our robotic scrub nurse with the universal gripper system can safely and robustly manipulate laparoscopic and da Vinci[Formula: see text] instruments. The system design will continue with the integration of context-sensitive capabilities.

Demographic overview of pilonidal sinus carcinoma: updated insights into the incidence.

PURPOSE: There are only rough estimates of the worldwide incidence of pilonidal sinus carcinoma. The purpose of the study is to explore the demographic characteristics of this disease and to provide more precise information about its incidence. METHODS: The study included questioning the surgeons and pathologists in Germany in addition to a literature research. The literature investigation included all published articles about pilonidal carcinoma in all languages. The questionnaire included 1050 pathologists and all 834 hospitals with a surgical division in Germany. The outcome measures included the total number of cases, the language of publication, gender, age, country of origin, interval until the diagnosis of carcinoma, and reported incidence based on local studies. RESULTS: From 1900 to 2022, we found 140 cases of pilonidal sinus carcinoma in 103 articles. The investigation revealed two additional unpublished cases from Germany. The male-to-female ratio was 7.75:1. The countries with the most cases were the USA (35 cases, 25.0%), Spain (13 cases, 9.3%), and Turkey (11 cases, 7.6%). The average age was 54.0 ± 11.8 years and the interval between the diagnosis of the disease and the development of carcinoma was 20.1 ± 14.1 years. There was a parallel increase in reported cases of pilonidal sinus disease and pilonidal carcinoma over the last century. The reported incidence varied from 0.03% to 5.56%. The worldwide calculated incidence equaled 0.17%. CONCLUSION: Due to underreporting and other causes, the incidence of carcinoma emerging on the background of pilonidal sinus disease is higher than reported.

A nationwide population-based study on the clinical and economic burden of anastomotic leakage in colorectal surgery.

AIM: Anastomotic leakage (AL) is one of the most dreaded complications in colorectal surgery. In 2013, the International Classification of Diseases code K91.83 for AL was introduced in Germany, allowing nationwide analysis of AL rates and associated parameters. The aim of this population-based study was to investigate the current incidence, risk factors, mortality, clinical management, and associated costs of AL in colorectal surgery. METHODS: A data query was performed based on diagnosis-related group data of all hospital cases of inpatients undergoing colon or sphincter-preserving rectal resections between 2013 and 2018 in Germany. RESULTS: A total number of 690,690 inpatient cases were included in this study. AL rates were 6.7% for colon resections and 9.2% for rectal resections in 2018. Regarding the treatment of AL, the application of endoluminal vacuum therapy increased during the studied period, while rates of relaparotomy, abdominal vacuum therapy, and terminal enterostomy remained stable. AL was associated with significantly increased in-house mortality (7.11% vs. 20.11% for colon resections and 3.52% vs. 11.33% for rectal resections in 2018) and higher socioeconomic costs (mean hospital reimbursement volume per case: 14,877€ (no AL) vs. 37,521€ (AL) for colon resections and 14,602€ (no AL) vs. 30,606€ (AL) for rectal resections in 2018). CONCLUSIONS: During the studied time period, AL rates did not decrease, and associated mortality remained at a high level. Our study provides updated population-based data on the clinical and economic burden of AL in Germany. Focused research in the field of AL is still urgently necessary to develop targeted strategies to prevent AL, improve patient care, and decrease socioeconomic costs.

Development and Validation of a Deep Learning Algorithm to Differentiate Colon Carcinoma From Acute Diverticulitis in Computed Tomography Images.

Importance: Differentiating between malignant and benign etiology in large-bowel wall thickening on computed tomography (CT) images can be a challenging task. Artificial intelligence (AI) support systems can improve the diagnostic accuracy of radiologists, as shown for a variety of imaging tasks. Improvements in diagnostic performance, in particular the reduction of false-negative findings, may be useful in patient care. Objective: To develop and evaluate a deep learning algorithm able to differentiate colon carcinoma (CC) and acute diverticulitis (AD) on CT images and analyze the impact of the AI-support system in a reader study. Design, Setting, and Participants: In this diagnostic study, patients who underwent surgery between July 1, 2005, and October 1, 2020, for CC or AD were included. Three-dimensional (3-D) bounding boxes including the diseased bowel segment and surrounding mesentery were manually delineated and used to develop a 3-D convolutional neural network (CNN). A reader study with 10 observers of different experience levels was conducted. Readers were asked to classify the testing cohort under reading room conditions, first without and then with algorithmic support. Main Outcomes and Measures: To evaluate the diagnostic performance, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for all readers and reader groups with and without AI support. Metrics were compared using the McNemar test and relative and absolute predictive value comparisons. Results: A total of 585 patients (AD: n = 267, CC: n = 318; mean [SD] age, 63.2 [13.4] years; 341 men [58.3%]) were included. The 3-D CNN reached a sensitivity of 83.3% (95% CI, 70.0%-96.6%) and specificity of 86.6% (95% CI, 74.5%-98.8%) for the test set, compared with the mean reader sensitivity of 77.6% (95% CI, 72.9%-82.3%) and specificity of 81.6% (95% CI, 77.2%-86.1%). The combined group of readers improved significantly with AI support from a sensitivity of 77.6% to 85.6% (95% CI, 81.3%-89.3%; P < .001) and a specificity of 81.6% to 91.3% (95% CI, 88.1%-94.5%; P < .001). Artificial intelligence support significantly reduced the number of false-negative and false-positive findings (NPV from 78.5% to 86.4% and PPV from 80.9% to 90.8%; P < .001). Conclusions and Relevance: The findings of this study suggest that a deep learning model able to distinguish CC and AD in CT images as a support system may significantly improve the diagnostic performance of radiologists, which may improve patient care.

Perioperative Low-Dose Prednisolone Treatment Has Beneficial Effects on Postoperative Recovery and Anastomotic Healing in a Murine Colitis Model.

BACKGROUND AND AIMS: High-dose glucocorticoid treatment has been identified as a risk factor for anastomotic leakage in patients with inflammatory bowel disease [IBD] undergoing bowel resection surgery. By contrast, active disease during surgery is also associated with elevated morbidity. Perioperative low-dose treatment might be beneficial regarding postoperative outcomes by controlling disease activity. The present study is the first to investigate the dose-dependent effect of perioperative prednisolone therapy in a murine IBD model combining dextran sodium sulphate [DSS] colitis with intestinal anastomosis surgery. METHODS: In 84 10-week-old wild-type mice, a colorectal anastomosis was performed using a microsurgical technique. Half the animals received induction of chemical colitis with 2% DSS via drinking water prior to surgery. In both groups, one-third of the animals received daily oral administration of high-dose [0.533 mg/kg] and one-third low-dose [0.133 mg/kg] prednisolone. Evaluation was performed on postoperative days 3 and 7. RESULTS: While high-dose prednisolone treatment led to an increased anastomotic leakage rate in mice under colitis, low-dose prednisolone treatment limited preoperative disease activity and did not influence the leakage rate. Histological examination showed a beneficial effect of low-dose prednisolone treatment on microscopic abscess formation at the anastomotic site in DSS mice as well as an increased anastomotic healing score. CONCLUSIONS: We demonstrate a beneficial effect of perioperative short-term low-dose prednisolone treatment on intestinal anastomotic healing in the context of colitis. Perioperative use of short-term low-dose prednisolone treatment might be beneficial in IBD patients who need to undergo surgery during active disease.