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Purpose Dysphagia is the inability or difficulty to swallow normally. Standard procedures for diagnosing the exact disease are, among others, X-ray videofluoroscopy, manometry and impedance examinations, usually performed consecutively. In order to gain more insights, ongoing research is aiming to collect these different modalities at the same time, with the goal to present them in a joint visualization. One idea to create a combined view is the projection of the manometry and impedance values onto the right location in the X-ray images. This requires to identify the exact sensor locations in the images. Methods This work gives an overview of the challenges associated with the sensor detection task and proposes a robust approach to detect the sensors in X-ray image sequences, ultimately allowing to project the manometry and impedance values onto the right location in the images. Results The developed sensor detection approach is evaluated on a total of 14 sequences from different patients, achieving a F1-score of 86.36%. To demonstrate the robustness of the approach, another study is performed by adding different levels of noise to the images, with the performance of our sensor detection method only slightly decreasing in these scenarios. This robust sensor detection provides the basis to accurately project manometry and impedance values onto the images, allowing to create a multimodal visualization of the swallow process. The resulting visualizations are evaluated qualitatively by domain experts, indicating a great benefit of this proposed fused visualization approach. Conclusion Using our preprocessing and sensor detection method, we show that the sensor detection task can be successfully approached with high accuracy. This allows to create a novel, multimodal visualization of esophageal motility, helping to provide more insights into swallow disorders of patients.

Interventional healthcare will evolve from an artisanal craft based on the individual experiences, preferences and traditions of physicians into a discipline that relies on objective decision-making on the basis of large-scale data from heterogeneous sources.

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.

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. Plain language summary 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. Wagner et al. present a robotic scrub nurse (RSN) system that predicts and delivers required instruments based on real-time laparoscopic video analysis. The machine learning based system accurately anticipates the necessary tools required for laparoscopic cholecystectomies, streamlining the surgical workflow and minimizing verbal communication.

Background The current trend in surgery toward further trauma reduction inevitably leads to increased technological complexity. It must be assumed that this situation will not stay under the sole control of surgeons; mechanical systems will assist them. Certain segments of the work flow will likely have to be taken over by a machine in an automatized or autonomous mode. Methods In addition to the analysis of our own surgical practice, a literature search of the Medline database was performed to identify important aspects, methods, and technologies for increased operating room (OR) autonomy. Results Robotic surgical systems can help to increase OR autonomy by camera control, application of intelligent instruments, and even accomplishment of automated surgical procedures. However, the important step from simple task execution to autonomous decision making is difficult to realize. Another important aspect is the adaption of the general technical OR environment. This includes adaptive OR setting and context-adaptive interfaces, automated tool arrangement, and optimal visualization. Finally, integration of peri- and intraoperative data consisting of electronic patient record, OR documentation and logistics, medical imaging, and patient surveillance data could increase autonomy. Conclusions To gain autonomy in the OR, a variety of assistance systems and methodologies need to be incorporated that endorse the surgeon autonomously as a first step toward the vision of cognitive surgery. Thus, we require establishment of model-based surgery and integration of procedural tasks. Structured knowledge is therefore indispensable.

Flexible endoscopy is increasingly developing into a therapeutic instead of a purely diagnostic discipline. Improved visualization makes early lesions easily detectable and allows us to decide ad hoc on the required treatment. Deep enteroscopy allows the exploration of even the small bowel - for long a \"white spot\" for gastrointestinal endoscopy - and to perform direct treatment. Endoscopic submucosal dissection is a considerable step forward in oncologically correct endoscopic treatment of (early) malignant lesions. Though still technically challenging, it is increasingly facilitated by new manipulation techniques and tools that are being steadily optimized. Closure of wall defects and hemostasis could be improved significantly. Even the anatomy beyond the gastrointestinal wall is being explored by the therapeutic use of endoluminal ultrasound. Endosonographic-guided surgery is not only a suitable fallback solution if conventional endoscopic retrograde cholangiopancreatography fails, but even makes necrosectomy procedures, abscess drainage, and neurolysis feasible for the endoscopist. Newly developed endoscopic approaches aim at formerly distinctive surgical domains like gastroesophageal reflux disease, appendicitis, and cholecystitis. Combined endoscopic/laparoscopic interventional techniques could become the harbingers of natural orifice transluminal endoscopic surgery, whereas pure natural orifice transluminal endoscopic surgery is currently still in its beginnings.

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.

BackgroundEndoscopic treatment of Zenker’s diverticulum is an attractive minimally invasive alternative compared to the classic open approach. However, increased recurrence rate were reported. In case of relapse, endoscopic therapy might be repeated, or alternatively open surgery is performed. This study aimed to identify potential differences in the outcomes between primary or secondary surgical treatment in Zenker’s diverticulum.MethodsFrom January 2003 to April 2019, 227 subsequent patients underwent surgical diverticulectomy and cervical myotomy at the surgical department of TUM. 41 of 227 patients had received previous therapy, either open or endoscopic. Perioperative parameters in priorly untreated patients were retrospectively compared to those after previous therapy (mostly endoscopic) with special regard to perioperative data and postoperative complications. Univariate and multivariate regression analyses were performed to identify predictors for postoperative complications.ResultsWe could show that the number of complications (p = 0.047) in pretreated patients is significant higher as well as the severity after Clavien–Dindo (p = 0.025). Stapler line leakage, wound infections, and operative revision rate was higher also pretreated group. Pretreatment and surgery time showed a significant association with postoperative complications in univariate analysis. In multivariate analysis, pretreatment remained a significant independent predictor of complications.ConclusionThe present data indicate that endoscopic therapy might represent a risk factor for postoperative complications in case of relapse surgery. Therefore primary open surgery should be debated in patients with an increased high risk of relapse.

Background We examined the impact of muscle relaxation on surgical conditions and patients’ postoperative outcome during elective laparoscopic cholecystectomy under balanced anaesthesia. Methods After approval and consent, 57 anaesthetized patients were randomly assigned to group no neuromuscular blockade (No NMB) and deep neuromuscular blockade (Deep NMB), i.e. no twitch response to train-of-four nerve stimulation. Laparoscopic cholecystectomy was performed using the 4-trocar technique with a CO 2 -pneumoperitoneum. Surgical conditions were assessed using a Visual Analogue Scale. Movement of diaphragm or abdominal muscles, inadequate visibility, or breathing and coughing against the ventilator were documented as events reflecting inadequate muscle relaxation. Independently, surgeons could request 0.3 mg/kg rocuronium to improve surgical conditions. Workflow variables were obtained as a surrogate of surgical conditions. Data are presented as mean (95 % confidence interval). The trial is registered at ClinicalTrials.gov (NCT00895778). Results While in 12 of 25 patients of group “No NMB” one or more adverse events impaired the surgical procedure ( p  < 0.001), only 1 of 25 patients of group “Deep NMB” showed an adverse event. Deep NMB resulted in an absolute risk reduction of 0.44 (0.23–0.65) and a number needed to treat of 2.3 (1.5–4.4), respectively. Surgeons requested 0.3 mg/kg rocuronium in 10 of 25 cases (40 %) of group “No NMB” only. This dose significantly improved surgical conditions by an average 62 of 100 possible points. All further variables did not differ between groups. Conclusions Deep NMB ameliorates surgical conditions for laparoscopic cholecystectomy by improved visibility and reduction of involuntary movements.

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.