Orthopadie (Heidelberg, Germany)最新文献

Background: Large language models (LLM) can automatically process clinical free-text documents, extract key information, and thereby reduce reading effort and documentation-related workload. High-quality data and targeted model control are essential for practical applicability.

Material and methods: Various approaches to information extraction are presented. Additionally, 24 unstructured pathological reports of bone and soft tissue tumors are processed using the local, generic LLM Llama 4 Scout with three different prompt variants and compared in terms of extraction quality.

Results: Prompt design had a substantial impact on model behavior. Prompts with clear parameter definitions and examples achieved the most reliable results. Typical LLM-specific errors, such as hallucinations and misclassifications, were also observed. LLM can support clinical staff by rapidly and systematically extracting relevant content from free-text documents. Safe and effective use requires high-quality data, precise inputs, and close collaboration between medical and technical experts.

Palpation and haptic feedback are vital for improving surgical precision, safety and decision-making in robotic-assisted surgery (RAS). Current RAS systems largely rely on visual input, lacking tactile feedback and autonomy, which increases training demands and limits scalability. The absence of touch impairs tissue characterization, complicates diagnostics and elevates the risk of unintended damage. Integrating haptics, through force sensors, tactile interfaces and emerging audio-based tools, has shown promise in restoring a sense of touch, particularly when enhanced by artificial intelligence and multimodal feedback systems. These technologies will enable interpretation of real-time data, improved tissue discrimination and reduced application of force, especially valuable in minimally invasive procedures. While progress has been made challenges remain in sensor miniaturization, biocompatibility and system integration; however, achieving semiautonomous and fully autonomous RAS requires intelligent sensing platforms combined with AI-driven analytics, and feedback mechanisms that approach human tactile perception. As tactile simulation technologies evolve, future surgical robots will operate with greater autonomy, improved accuracy and broader global accessibility. The field is moving toward a new paradigm: surgical robots as intelligent, adaptive systems capable of performing or assisting procedures collaboratively or independently, using real-time sensing and control to optimize outcomes and reduce reliance on human expertise.

Background: Osteoarthritis (OA) is one of the most common joint diseases, affecting more than 500 million people worldwide. In recent decades, there has only been limited progress in terms of diagnosis and treatment. For a long time, OA was considered to be primarily a mechanically induced degenerative disease. However, more recent work has shown that OA is a heterogeneous condition that manifests in different phenotypes. Although artificial intelligence (AI) is becoming increasingly important in medical research, its specific application in the field of OA remains limited in clinical use.

Objectives: The aim of this review is to summarize the current approaches to phenotyping OA and to highlight the role of AI in the identification and classification of OA phenotypes.

Materials and methods: Selective literature review RESULTS: There are several promising applications of AI in OA diagnosis and assessment, such as automated assessment of cartilage damage or prediction of the need for arthroplasty. Close cooperation between orthopaedics, radiology, and AI experts is necessary to integrate AI models into clinical practice.

Conclusions: The use of AI to detect and assess OA-typical changes offers major potential to improve diagnostic imaging, clinical interpretation, and disease prognosis. Through more precise diagnoses and individualized prognoses, AI-based methods could significantly contribute to making treatment decisions more effective and, thus, optimizing patient care overall.

Background: The integration of artificial intelligence (AI) into musculoskeletal radiology has the potential to revolutionize diagnostic precision and efficiency. At the beginning of the radiological diagnostic workflow, AI algorithms lead to a significant reduction in examination time, for example in image acquisition in MRI.

Diagnostics: Certain AI algorithms achieve a diagnostic accuracy in fracture detection that is comparable to that of board-certified radiologists and perform time-consuming diagnostic analyses such as joint and axis measurements fully automatically. Generative AI based on large language models (LLM) can contribute to automation in structured reporting, with first clinical applications already available.

Challenges: While AI algorithms for accelerating MRI acquisition already contribute significantly to efficiency, challenges in the clinical translation of AI algorithms for automated reading support lie primarily in the low number of commercially available AI algorithms that offer a significant clinical value to quality and efficiency in the diagnostic workflow. Accepted key performance indices (KPI) for quantitatively measuring the return on investment (ROI) for the high running costs are also largely unresolved internationally.

Background: Failure of osteosynthesis following proximal femur fracture represents an increasing challenge in geriatric traumatology. In these cases, conversion total hip arthroplasty (cTHA) is often required. It remains unclear whether a single-stage or a two-stage approach is superior in terms of infection risk and clinical outcomes.

Methods: A systematic literature search was conducted in PubMed for the period 1995-2025. Included were original articles, systematic reviews, and meta-analyses investigating single-stage or two-stage procedures following osteosynthesis failure of the proximal femur. Case reports, editorials, and studies unrelated to hip arthroplasty were excluded. A total of 17 publications were included in the analysis. Based on the findings, a clinical decision-making algorithm for treatment planning was developed in our own clinic.

Results: The available evidence is predominantly based on retrospective studies with small sample sizes. Reported infection rates after cTHA vary widely between studies (0-8.6%). Some studies indicate advantages of the two-stage approach in terms of infection control, while others report comparable outcomes for both strategies. Positive microbiological findings despite the absence of clinical signs of infection are common, but their clinical relevance remains unclear.

Conclusion: Current evidence does not allow a definitive recommendation in favor of either a single-stage or two-stage approach. Individual risk stratification, considering clinical, laboratory, and radiological parameters, is crucial. The clinical algorithm presented provides a practical approach for decision-making. Prospective multicenter studies are needed to derive robust recommendations.

Background: The posterior tibial slope (PTS) is the inclination of the tibial plateau in the sagittal plane and is an important factor for knee joint biomechanics. A normal PTS lies between 6 and 12°. Deviations, such as a steeper slope (> 12°) or a flatter slope (< 5°), are associated with characteristic biomechanical changes.

Objective: This paper addresses the clinical significance of sagittal alignment and its effects on the risk of injury to cruciate ligaments, menisci, and cartilage. Furthermore, common radiological measurement methods and surgical corrections for pathological PTS values are investigated.

Material and methods: The work summarizes findings from numerous biomechanical and clinical studies. The radiological determination of the PTS, as well as the surgical correction options, are presented in detail.

Results: An increased PTS is a significant risk factor for ACL (anterior cruciate ligament) ruptures and graft failure after reconstruction. A value of > 12° has emerged as a statistical threshold in many studies, above which the risk increases significantly. Increased slope values are also associated with an elevated risk of meniscal root and ramp lesions. The evidence for an association with cartilage damage is still low. Conversely, a flatter slope (< 5°) increases the risk for PCL (posterior cruciate ligament) ruptures and re-ruptures. The correction of the PTS through a closed-wedge osteotomy is frequently discussed in the literature for values > 12° and appears to reduce the risk of a renewed ACL rupture.

Discussion: The strong correlation between sagittal alignment and ligament instability underscores the central importance of the PTS in knee joint biomechanics. Consequently, PTS must be systematically included in the diagnostic workup as a primary risk factor. In cases of ACL or PCL graft failure combined with a pathological slope, operative correction should be discussed as a causal therapeutic option to minimize graft strain. However, high-quality prospective studies are necessary to conclusively prove the long-term superiority of this combined approach.

Fractures of the tibial head continue to pose a challenge in surgical treatment. The goal is to achieve anatomical repositioning of the joint surface in order to prevent the development of posttraumatic gonarthrosis whenever possible. Sequential imaging techniques, such as computed tomography and magnetic resonance imaging, have become established for identifying the fracture morphology along with associated ligamentous and neurovascular accompanying injuries as well as for determining the treatment regimen, and for planning the surgical intervention. The updated S2k guidelines provide assistance for a structured and standardized approach that can be tailored to the individual patient. Based on the preoperative classification of the fracture the optimal procedure can be chosen. In addition to the timing of treatment, the choice of access also has a decisive impact on the postoperative outcome. Modern implants enable fracture-adapted osteosynthesis, likely resulting in less secondary loss of reduction in the hands of an experienced surgeon. Therefore, the training and further education of future surgeons is of great importance to ensure optimal care for our patients.

Background: With the progress of digitalization and the rapid development of portable measurement systems, the integration of new technologies into clinical practice is inevitable. Considering the demographic change, the current healthcare system needs exactly this integration to appropriately address the growing challenges. Regarding orthopaedic patient care, the objective analysis of the musculoskeletal system represents an increasingly important aspect. Innovative key technologies are necessary to handle the amount of instrumental data and create new opportunities. While an enormous increase in efficiency, effectiveness and quality of care can be realized, potential risks from the application of artificial intelligence (AI) need to be considered.

Methods: Based on a literature review using the PRISMA-method, specific opportunities for clinical integration are identified and potential risks are discussed.

Results: A total of 188 publications were included, of which 54 were analyzed in detail.

Conclusion: While the application of AI methods for the use of objective measurement systems opens a variety of opportunities in orthopaedic patient care, it is also associated with relevant risks. In order to address specific problems and achieve the primary goal of optimized healthcare, the appropriate use and the proper integration of AI methods are essential. It must be acknowledged that artificial intelligence can only play a supporting role, with transparency being the main criterion for trustworthy use in the healthcare sector.

Background: Artificial intelligence (AI) is considered a key technology for alleviating the burden on the healthcare system. For instrumental gait analysis, AI-based evaluations promise a direct and intuitive access to clinically relevant information in orthopaedics and trauma surgery, avoiding the challenging and time-consuming manual evaluations of large amounts of patient data.

Objective: The objective of this work is to investigate the specific challenges and limitations of using AI for clinical evaluation of gait analysis data and to propose effective solutions to address these limitations.

Method: This work combines a systematic literature review on AI in gait analysis with practical experiences from applications of AI in the authors' own published research projects.

Results: Six key challenges have been identified. While AI methods work best when extensive training data, a limited number of influencing factors, and a clearly defined target variable are available, instrumental gait analysis is characterised by opposite conditions (little training data, multiple influencing factors, and fuzzy target variables). To address these contradicting characteristics, a catalogue of possible solution approaches focusing on integrating clinical expert knowledge into AI development and operation is outlined.

Conclusion: It is shown that AI offers significant potential for improving the efficiency and quality of gait data exploitation. However, current AI approaches from other fields are only partially transferable to gait analysis due to insufficient fitting. By addressing the specific challenges for AI in gait analysis, it can be expected that specialized procedures and best practices can be developed, which will boost AI assistance in IGA clinical evaluation.