Intraoperative Decision Support Research Articles

Large Language Models for Intraoperative Decision Support in Plastic Surgery: A Comparison between ChatGPT-4 and Gemini.

Background and Objectives: Large language models (LLMs) are emerging as valuable tools in plastic surgery, potentially reducing surgeons' cognitive loads and improving patients' outcomes. This study aimed to assess and compare the current state of the two most common and readily available LLMs, Open AI's ChatGPT-4 and Google's Gemini Pro (1.0 Pro), in providing intraoperative decision support in plastic and reconstructive surgery procedures. Materials and Methods: We presented each LLM with 32 independent intraoperative scenarios spanning 5 procedures. We utilized a 5-point and a 3-point Likert scale for medical accuracy and relevance, respectively. We determined the readability of the responses using the Flesch-Kincaid Grade Level (FKGL) and Flesch Reading Ease (FRE) score. Additionally, we measured the models' response time. We compared the performance using the Mann-Whitney U test and Student's t-test. Results: ChatGPT-4 significantly outperformed Gemini in providing accurate (3.59 ± 0.84 vs. 3.13 ± 0.83, p-value = 0.022) and relevant (2.28 ± 0.77 vs. 1.88 ± 0.83, p-value = 0.032) responses. Alternatively, Gemini provided more concise and readable responses, with an average FKGL (12.80 ± 1.56) significantly lower than ChatGPT-4's (15.00 ± 1.89) (p < 0.0001). However, there was no difference in the FRE scores (p = 0.174). Moreover, Gemini's average response time was significantly faster (8.15 ± 1.42 s) than ChatGPT'-4's (13.70 ± 2.87 s) (p < 0.0001). Conclusions: Although ChatGPT-4 provided more accurate and relevant responses, both models demonstrated potential as intraoperative tools. Nevertheless, their performance inconsistency across the different procedures underscores the need for further training and optimization to ensure their reliability as intraoperative decision-support tools.

Abstract PO2-23-07: Three-dimensional navigated mass spectrometry for intraoperative margin assessment during breast cancer surgery

Abstract Positive resection margins occur in approximately 25% of breast cancer (BCa) surgeries, requiring re-operation. Margin status is not routinely available during surgery; thus, technologies that identify residual cancer on the specimen or cavity are needed to provide intraoperative decision support that may reduce positive margin rates. Rapid evaporative ionization mass spectrometry (REIMS) is an emerging technique that chemically profiles the plume generated by tissue cauterization to classify the ablated tissue as either cancerous or non-cancerous, on the basis of detected lipid species. Although REIMS can distinguish cancer and non-cancerous breast tissue by the signals generated, it does not indicate the location of the classified tissue in real-time. Our objective was to combine REIMS with spatio-temporal navigation (navigated REIMS), and to compare performance of navigated REIMS with conventional histopathology examination (gold standard) to assess margin status (positive or negative) in patients undergoing surgery for BCa. A multivariate model was trained using pathology-validated mass spectra from ex vivo resection specimens from 11 patients, including 36 spectra from invasive cancer, and 118 spectra from normal breast adipose. This model was subsequently tested retrospectively on intraoperative data from 25 BCa cases that were conducted using navigated REIMS. Navigation was facilitated by an electromagnetic sensor placed on the cautery. To record the position of the tumor, a localization wire fitted with an electromagnetic sensor was placed into the tumor using ultrasound prior to surgery, and a 3D map of the tumor was created. This enabled real-time knowledge of the cautery position relative to the tumor during surgery. Spectra from the plume classified as BCa were mapped onto a display of the tumor region and compared with the pathology report. Our multivariate classifier exhibited &amp;gt;90% accuracy on cross-validation, driven by an elevated ratio of glycerophospholipid:triglyceride in cancer as compared with normal adipose. In the intraoperative testing cohort, 4/150 margins were assessed by pathology as positive, all of which were correctly identified by REIMS (Sensitivity=100%). Notably, two of these cases were positive for ductal carcinoma in situ. 146/150 margins were negative, of which 131/146 negative margins were consistent with histopathology (Specificity=90%). There were 13 negative margins that were determined to be positive by REIMS (false positive), of which 8 margins were noted as either ‘close margins’ (with cancer cells being detected within 1 mm of the inked margin on histopathology), or as high-density normal breast tissue. Other normal tissue such as skin and muscle also exhibited high phospholipid content but these spectra could be rationalized by relative distance from the tumor region using the navigation data and/or information provided by the surgeon in real-time. We have demonstrated the importance of spatio-temporal tracking and histopathology to validate intraoperative REIMS. Furthermore, we have shown the feasibility of navigated REIMS in identifying margins containing cancer in real-time during BCa surgery, where a wider excision may be desirable. Citation Format: Martin Kaufmann, Amoon Jamzad, Tamas Ungi, Jessica R Rodgers, Teaghan Koster, Chris Yeung, Josh Ehrlich, Alice Santilli, Mark Asselin, Natasja Janssen, Julie McMullen, Kathryn Solberg, Joanna Cheesman, Alessia Di Carlo, Kevin Yi Mi Ren, Sonal Varma, Shaila Merchant, Cecil Jay Engel, G Ross Walker, Andrea Gallo, Doris Jabs, Parvin Mousavi, Gabor Fichtinger, John F Rudan. Three-dimensional navigated mass spectrometry for intraoperative margin assessment during breast cancer surgery [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-23-07.

AI-powered real-time annotations during urologic surgery: The future of training and quality metrics

Introduction and objectiveReal-time artificial intelligence (AI) annotation of the surgical field has the potential to automatically extract information from surgical videos, helping to create a robust surgical atlas. This content can be used for surgical education and qualitative initiatives. We demonstrate the first use of AI in urologic robotic surgery to capture live surgical video and annotate key surgical steps and safety milestones in real-time. Summary background dataWhile AI models possess the capability to generate automated annotations based on a collection of video images, the real-time implementation of such technology in urological robotic surgery to aid surgeon and training staff it is still pending to be studied. MethodsWe conducted an educational symposium, which broadcasted 2 live procedures, a robotic-assisted radical prostatectomy (RARP) and a robotic-assisted partial nephrectomy (RAPN). A surgical AI platform system (Theator, Palo Alto, CA) generated real-time annotations and identified operative safety milestones. This was achieved through trained algorithms, conventional video recognition, and novel Video Transfer Network technology which captures clips in full context, enabling automatic recognition and surgical mapping in real-time. ResultsReal-time AI annotations for procedure #1, RARP, are found in Table 1. The safety milestone annotations included the apical safety maneuver and deliberate views of structures such as the external iliac vessels and the obturator nerve. Real-time AI annotations for procedure #2, RAPN, are found in Table 1. Safety milestones included deliberate views of structures such as the gonadal vessels and the ureter. AI annotated surgical events included intraoperative ultrasound, temporary clip application and removal, hemostatic powder application, and notable hemorrhage. ConclusionsFor the first time, surgical intelligence successfully showcased real-time AI annotations of 2 separate urologic robotic procedures during a live telecast. These annotations may provide the technological framework for send automatic notifications to clinical or operational stakeholders. This technology is a first step in real-time intraoperative decision support, leveraging big data to improve the quality of surgical care, potentially improve surgical outcomes, and support training and education.

Classification of intracranial tumors based on optical-spectral analysis

The motivation for the present study was the need to develop methods of urgent intraoperative biopsy during surgery for removal of intracranial tumors. Based on the experience of previous joint work of GPI RAS and N.N. Burdenko National Medical Research Center of Neurosurgery to introduce fluorescence spectroscopy methods into clinical practice, an approach combining various optical-spectral techniques, such as autofluorescence spectroscopy, fluorescence of 5-ALA induced protoporphyrin IX, diffuse reflection of broadband light, which can be used to determine hemoglobin concentration in tissues and their optical density, Raman spectroscopy, which is a spectroscopic method that allows detection of various molecules in tissues by vibrations of individual characteristic molecular bonds. Such a variety of optical and spectral characteristics makes it difficult for the surgeon to analyze them directly during surgery, as it is usually realized in the case of fluorescence methods – tumor tissue can be distinguished from normal with a certain degree of certainty by fluorescence intensity exceeding a threshold value. In case the number of parameters exceeds a couple of dozens, it is necessary to use machine learning algorithms to build a intraoperative decision support system for the surgeon. This paper presents research in this direction. Our earlier statistical analysis of the optical-spectral features allowed identifying statistically significant spectral ranges for analysis of diagnostically important tissue components. Studies of dimensionality reduction techniques of the optical-spectral feature vector and methods of clustering of the studied samples also allowed us to approach the implementation of the automatic classification method. Importantly, the classification task can be used in two applications – to differentiate between different tumors and to differentiate between different parts of the same (center, perifocal zone, normal) tumor. This paper presents the results of our research in the first direction. We investigated the combination of several methods and showed the possibility of differentiating glial and meningeal tumors based on the proposed optical-spectral analysis method.

Artificial intelligence in pancreatic surgery: current applications

Surgery plays a central role in the treatment of benign and malignant pancreatic diseases. Artificial intelligence (AI) is an important upcoming technology to support surgeons in pre-, intra-, and postoperative diagnosis, decision-making and training toward an optimized patient care. Current AI applications show a promising role in the evaluation of preoperative images for prediction of malignancy and resectability, intraoperative decision support, surgical training as well as a postoperative risk stratification to personalize the management of complications. This scoping review summarizes the most up to date developments of AI in pancreatic surgery with the highest available level of evidence.

LBA01-17 AI-POWERED REAL-TIME ANNOTATIONS DURING UROLOGIC SURGERY: THE FUTURE OF TRAINING AND QUALITY METRICS

You have accessJournal of UrologyCME1 Apr 2023LBA01-17 AI-POWERED REAL-TIME ANNOTATIONS DURING UROLOGIC SURGERY: THE FUTURE OF TRAINING AND QUALITY METRICS Laura Zuluaga, Jordan M. Rich, Raghav Gupta, Adriana Pedraza, Burak Ucpinar, Kennedy E. Okhawere, Indu Saini, Mani Menon, Ashutosh Tewari, and Ketan K Badani Laura ZuluagaLaura Zuluaga More articles by this author , Jordan M. RichJordan M. Rich More articles by this author , Raghav GuptaRaghav Gupta More articles by this author , Adriana PedrazaAdriana Pedraza More articles by this author , Burak UcpinarBurak Ucpinar More articles by this author , Kennedy E. OkhawereKennedy E. Okhawere More articles by this author , Indu SainiIndu Saini More articles by this author , Mani MenonMani Menon More articles by this author , Ashutosh TewariAshutosh Tewari More articles by this author , and Ketan K BadaniKetan K Badani More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003360.17AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Real-time artificial intelligence (AI) annotation of the surgical field has the potential to automatically extract information from surgical videos, helping to create a robust surgical atlas. This content can be used for surgical education and qualitative initiatives. We demonstrate the first use of AI in urologic robotic surgery to capture live surgical video and annotate key surgical steps and safety milestones in real-time. METHODS: We conducted an educational symposium, which broadcasted two live procedures, a robotic-assisted radical prostatectomy (RARP) and robotic-assisted partial nephrectomy (RAPN). A surgical AI platform system (Theator, Palo Alto, CA) generated real-time annotations and identified operative safety milestones (Image 1). This was achieved through trained algorithms, conventional video recognition, and novel technology called Video Transfer Network which captures clips in full context, enabling automatic recognition and surgical mapping in real-time. RESULTS: Real-time AI annotations for procedure #1, RARP, are found in Table 1. Safety milestone annotations included the apical safety maneuver and structures identification such as the external iliac vessels and the obturator nerve. Real-time AI annotations for procedure #2, RAPN, are found in Table 1. Safety milestones included deliberate views of structures such as the gonadal vessels and the ureter. AI annotated surgical events included intraoperative ultrasound and notable hemorrhage among others. CONCLUSIONS: Surgical intelligence successfully showcased real-time AI annotations of two separate urologic robotic procedures during live telecast for the first time. These annotations may provide the technological framework to send automatic notifications to clinical or operational stakeholders. This technology is a first step in real-time intraoperative decision support; leveraging big data to improve the quality of surgical care, potentially improve surgical outcomes and, support training and education. Source of Funding: N/A © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e1183 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Laura Zuluaga More articles by this author Jordan M. Rich More articles by this author Raghav Gupta More articles by this author Adriana Pedraza More articles by this author Burak Ucpinar More articles by this author Kennedy E. Okhawere More articles by this author Indu Saini More articles by this author Mani Menon More articles by this author Ashutosh Tewari More articles by this author Ketan K Badani More articles by this author Expand All Advertisement PDF downloadLoading ...

Classification of Adverse Events in Adult Surgery.

Successful surgery combines quality (achievement of a positive outcome) with safety (avoidance of a negative outcome). Outcome assessment serves the purpose of quality improvement in health care by establishing performance indicators and allowing the identification of performance gaps. Novel surgical quality metric tools (benchmark cutoffs and textbook outcomes) provide procedure-specific ideal surgical outcomes in a subgroup of well-defined low-risk patients, with the aim of setting realistic and best achievable goals for surgeons and centers, as well as supporting unbiased comparison of surgical quality between centers and periods of time. Validated classification systems have been deployed to grade adverse events during the surgical journey: (1) the ClassIntra classification for the intraoperative period; (2) the Clavien-Dindo classification for the gravity of single adverse events; and the (3) Comprehensive Complication Index (CCI) for the sum of adverse events over a defined postoperative period. The failure to rescue rate refers to the death of a patient following one or more potentially treatable postoperative adverse event(s) and is a reliable proxy of the institutional safety culture and infrastructure. Complication assessment is undergoing digital transformation to decrease resource-intensity and provide surgeons with real-time pre- or intraoperative decision support. Standardized reporting of complications informs patients on their chances to realize favorable postoperative outcomes and assists surgical centers in the prioritization of quality improvement initiatives, multidisciplinary teamwork, surgical education, and ultimately, in the enhancement of clinical standards.

Invited Commentary: Necrosis Excision through Indocyanine Green Fluorescence-Guided Surgery: Intraoperative Decision Support for Tangential Excision.

Invited Commentary: Necrosis Excision through Indocyanine Green Fluorescence-Guided Surgery: Intraoperative Decision Support for Tangential Excision.

Ontology-based surgical workflow recognition and prediction

BackgroundSurgical 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. MethodsA 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. ResultsThe 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. ConclusionsIn 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.

Validation of an artificial intelligence platform for the guidance of safe laparoscopic cholecystectomy.

Many surgical adverse events, such as bile duct injuries during laparoscopic cholecystectomy (LC), occur due to errors in visual perception and judgment. Artificial intelligence (AI) can potentially improve the quality and safety of surgery, such as through real-time intraoperative decision support. GoNoGoNet is a novel AI model capable of identifying safe ("Go") and dangerous ("No-Go") zones of dissection on surgical videos of LC. Yet, it is unknown how GoNoGoNet performs in comparison to expert surgeons. This study aims to evaluate the GoNoGoNet's ability to identify Go and No-Go zones compared to an external panel of expert surgeons. A panel of high-volume surgeons from the SAGES Safe Cholecystectomy Task Force was recruited to draw free-hand annotations on frames of prospectively collected videos of LC to identify the Go and No-Go zones. Expert consensus on the location of Go and No-Go zones was established using Visual Concordance Test pixel agreement. Identification of Go and No-Go zones by GoNoGoNet was compared to expert-derived consensus using mean F1 Dice Score, and pixel accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). A total of 47 frames from 25 LC videos, procured from 3 countries and 9 surgeons, were annotated simultaneously by an expert panel of 6 surgeons and GoNoGoNet. Mean (± standard deviation) F1 Dice score were 0.58 (0.22) and 0.80 (0.12) for Go and No-Go zones, respectively. Mean (± standard deviation) accuracy, sensitivity, specificity, PPV and NPV for the Go zones were 0.92 (0.05), 0.52 (0.24), 0.97 (0.03), 0.70 (0.21), and 0.94 (0.04) respectively. For No-Go zones, these metrics were 0.92 (0.05), 0.80 (0.17), 0.95 (0.04), 0.84 (0.13) and 0.95 (0.05), respectively. AI can be used to identify safe and dangerous zones of dissection within the surgical field, with high specificity/PPV for Go zones and high sensitivity/NPV for No-Go zones. Overall, model prediction was better for No-Go zones compared to Go zones. This technology may eventually be used to provide real-time guidance and minimize the risk of adverse events.

Use of provider education, intra-operative decision support, and an email-feedback system in improving compliance with sugammadex dosage guideline and reducing drug expenditures

Study objectiveDue to excessive sugammadex expenditures at our institution, we designed dosing guidelines that utilize adjusted body weight and informatics-based tools aimed at reducing variability in dosing practices. DesignWe retrospectively reviewed rates of high-dose sugammadex administration in three phases: Pre-intervention - May 2018 to November 2018; First intervention - November 2018 to April 2019; and Second intervention - April 2019 to July 2019. SettingAcademic medical center in the United States - Vanderbilt University Medical Center (VUMC) PatientsN/A InterventionsFirst, anesthesia providers were educated on adjusted body weight-based dosing guidelines. Providers also received intraoperative decision support displaying a patient's actual and adjusted body weight along with rates of high-dose (>200 mg) sugammadex administration for each respective provider. Second, we implemented an email-feedback system to remind providers of the new guidelines. MeasurementsWeekly rate of high-dose sugammadex cases. Main resultsDuring the pre-intervention stage, 1556 (12.3%) cases involved high-dose sugammadex. Comparatively, 550 (4.3%) and 187 (3.1%) high-dose sugammadex cases occurred during the first and second intervention stages, respectively. Segmented regression analysis demonstrated a significant rate change of −3.51% (95% CI: −5.64%, −1.38%) in sugammadex dosing practices after provider education and the implementation of digital improvement initiatives but failed to reveal a significant change after implementation of the email-feedback system. Overall, our interventions were associated with $2563.05 in estimated weekly savings of sugammadex expenditures. ConclusionsProvider education and digital quality improvement was associated with reduced rates of high-dose sugammadex administration, generating cost savings at a large academic medical institution.

Artificial Intelligence for Computer Vision in Surgery: A Call for Developing Reporting Guidelines.

Artificial Intelligence for Computer Vision in Surgery: A Call for Developing Reporting Guidelines.

Artificial intelligence indocyanine green (ICG) perfusion for colorectal cancer intra-operative tissue classification

A new concept in intraoperative decision support for tumour delineation is proposed showing that artificial intelligence provides categorising information and interpretation from the images captured during fluorescence-guided colorectal cancer operations. This is potentially applicable to all cancer subtypes and is pertinent to new fluorophore development.A new concept in intraoperative decision support for tumour delineation is proposed showing that artificial intelligence provides categorising information and interpretation from the images captured during fluorescence-guided colorectal cancer operations. This is potentially applicable to all cancer subtypes and is pertinent to new fluorophore development. A new concept in intraoperative decision support for tumour delineation is proposed showing that artificial intelligence provides categorising information and interpretation from the images captured during fluorescence-guided colorectal cancer operations. This is potentially applicable to all cancer subtypes and is pertinent to new fluorophore development. Shows promise

Current applications of artificial intelligence for intraoperative decision support in surgery.

Research into medical artificial intelligence (AI) has made significant advances in recent years, including surgical applications. This scoping review investigated AI-based decision support systems targeted at the intraoperative phase of surgery and found a wide range of technological approaches applied across several surgical specialties. Within the twenty-one (n = 21) included papers, three main categories of motivations were identified for developing such technologies: (1) augmenting the information available to surgeons, (2) accelerating intraoperative pathology, and (3) recommending surgical steps. While many of the proposals hold promise for improving patient outcomes, important methodological shortcomings were observed in most of the reviewed papers that made it difficult to assess the clinical significance of the reported performance statistics. Despite limitations, the current state of this field suggests that a number of opportunities exist for future researchers and clinicians to work on AI for surgical decision support with exciting implications for improving surgical care.

Deep transfer learning methods for colon cancer classification in confocal laser microscopy images.

The gold standard for colorectal cancer metastases detection in the peritoneum is histological evaluation of a removed tissue sample. For feedback during interventions, real-time in vivo imaging with confocal laser microscopy has been proposed for differentiation of benign and malignant tissue by manual expert evaluation. Automatic image classification could improve the surgical workflow further by providing immediate feedback. We analyze the feasibility of classifying tissue from confocal laser microscopy in the colon and peritoneum. For this purpose, we adopt both classical and state-of-the-art convolutional neural networks to directly learn from the images. As the available dataset is small, we investigate several transfer learning strategies including partial freezing variants and full fine-tuning. We address the distinction of different tissue types, as well as benign and malignant tissue. We present a thorough analysis of transfer learning strategies for colorectal cancer with confocal laser microscopy. In the peritoneum, metastases are classified with an AUC of 97.1, and in the colon the primarius is classified with an AUC of 73.1. In general, transfer learning substantially improves performance over training from scratch. We find that the optimal transfer learning strategy differs for models and classification tasks. We demonstrate that convolutional neural networks and transfer learning can be used to identify cancer tissue with confocal laser microscopy. We show that there is no generally optimal transfer learning strategy and model as well as task-specific engineering is required. Given the high performance for the peritoneum, even with a small dataset, application for intraoperative decision support could be feasible.

Impact of a Novel Multiparameter Decision Support System on Intraoperative Processes of Care and Postoperative Outcomes

The authors hypothesized that a multiparameter intraoperative decision support system with real-time visualizations may improve processes of care and outcomes. Electronic health record data were retrospectively compared over a 6-yr period across three groups: experimental cases, in which the decision support system was used for 75% or more of the case at sole discretion of the providers; parallel controls (system used 74% or less); and historical controls before system implementation. Inclusion criteria were adults under general anesthesia, advanced medical disease, case duration of 60 min or longer, and length of stay of two days or more. The process measures were avoidance of intraoperative hypotension, ventilator tidal volume greater than 10 ml/kg, and crystalloid administration (ml · kg · h). The secondary outcome measures were myocardial injury, acute kidney injury, mortality, length of hospital stay, and encounter charges. A total of 26,769 patients were evaluated: 7,954 experimental cases, 10,933 parallel controls, and 7,882 historical controls. Comparing experimental cases to parallel controls with propensity score adjustment, the data demonstrated the following medians, interquartile ranges, and effect sizes: hypotension 1 (0 to 5) versus 1 (0 to 5) min, P < 0.001, beta = -0.19; crystalloid administration 5.88 ml · kg · h (4.18 to 8.18) versus 6.17 (4.32 to 8.79), P < 0.001, beta = -0.03; tidal volume greater than 10 ml/kg 28% versus 37%, P < 0.001, adjusted odds ratio 0.65 (0.53 to 0.80); encounter charges $65,770 ($41,237 to $123,869) versus $69,373 ($42,101 to $132,817), P < 0.001, beta = -0.003. The secondary clinical outcome measures were not significantly affected. The use of an intraoperative decision support system was associated with improved process measures, but not postoperative clinical outcomes.

Muskuloskeletale Belastungsanalysen

Mechanical loading of the lower extremities due to muscle and joint contact forces plays an important role in orthopaedic and trauma surgery. Detailed, patient specific information on the in vivo forces and their distribution is, however, currently not readily available to the surgeon in clinical routine. The goal of this study was to elucidate the relationship between the position of the cup and the musculoskeletal loading conditions at the hip using validated analyses, and further, to evaluate the predictions of the biomechanical conditions against the results of a clinical study. The results indicate that restoring the anatomical hip centre to its anatomical mediolateral position could help to reduce joint loads and add to the longevity of the reconstruction. The routine use of validated analyses of musculoskeletal loading conditions, such as in the presented example using standardised pre-operative planning and sound intra-operative decision support systems, could contribute to securing a high standard in patient treatment.