المؤلفون: Wilhelm Nikolas, Micheler Carina M., Lang Jan J., Hinterwimmer Florian, Schaack Victor, Smits Ricardo, Haddadin Sami, Burgkart Rainer

المصدر: Current Directions in Biomedical Engineering, Vol 9, Iss 1, Pp 254-257 (2023)

مصطلحات موضوعية: imu, gait analysis, vicon, videopose3d, Medicine

الوصف: This study aimed to develop and evaluate a costeffective Inertial Measurement Unit (IMU) system for gait analysis, comparing its performance with the Vicon system and the VideoPose3D algorithm. The system comprises five calibrated sensors and a mobile app to measure lower body orientation during gait and stair climbing. Eight healthy participants were involved in the experiment, each performing ten repetitions to analyze hip and knee flexion angles. The IMU system demonstrated significantly lower mean square error than deep learning-based approaches and comparable results to the Vicon system, indicating its potential for clinical and research applications.

وصف الملف: electronic resource

العلاقة: https://doaj.org/toc/2364-5504Test

الوصول الحر: https://doaj.org/article/a7d84529104e425788f9972722a2e7e2Test

المؤلفون: Micheler Carina M., Lang Jan J., Bäumlisberger Anja, Wachtel Nikolaus, Wilhelm Nikolas J., Schaack Victor G., Eisenhart-Rothe Rüdiger von, Burgkart Rainer H. H.

المصدر: Current Directions in Biomedical Engineering, Vol 9, Iss 1, Pp 532-535 (2023)

مصطلحات موضوعية: biomechanics, suture, forehead, tensile test, Medicine

الوصف: Wound healing can be delayed if the biomechanical stability of the wound closure is inadequate. Therefore, it is necessary to investigate different suturing techniques for their biomechanical stability. In this study, suturing techniques suitable for the forehead area were investigated. For this application, a special test setup was developed to simulate the curvature of the forehead and the corresponding physiological configuration. The average forehead curvature is 62.24 ± 4.11 mm in radius. To simulate this curvature, the skin specimens are subjected to tensile stress over the spherical surface using a standard uniaxial testing machine. For the evaluation, an automated evaluation tool for MATLAB was also developed. Three different suturing techniques (Straight, Lazy-S, Zigzag) were investigated and tested for their biomechanical stability. Of the three suturing techniques, the Zigzag suture proved to be the most stable with the highest stiffness of 44.23 ± 8.18 % and the highest final failure of 32.60 ± 4.95 % (relative to the control sample without incision). The study has shown that the test setup can be used to investigate different forehead suture techniques.

وصف الملف: electronic resource

العلاقة: https://doaj.org/toc/2364-5504Test

الوصول الحر: https://doaj.org/article/d31a7b50496e452ab14d5cab0ed389d3Test

المؤلفون: Hinterwimmer Florian, Consalvo Sarah, Neumann Jan, Micheler Carina, Wilhelm Nikolas, Lang Jan, Eisenhart-Rothe Rüdiger von, Burgkart Rainer, Rueckert Daniel

المصدر: Current Directions in Biomedical Engineering, Vol 8, Iss 2, Pp 9-12 (2022)

مصطلحات موضوعية: transfer learning, self-supervised learning, radiographs, sarcoma, Medicine

الوصف: Ewing sarcomas are malignant neoplasm entities typically found in children and adolescents. Early detection is crucial for therapy and prognosis. Due to the low incidence the general experience as well as according data is limited. Novel support tools for diagnosis, such as deep learning models for image interpretation, are required. While acquiring sufficient data is a common obstacle in medicine, several techniques to tackle small data sets have emerged. The general necessity of large data sets in addition to a rare disease lead to the question whether transfer learning can solve the issue of limited data and subsequently support tasks such as distinguishing Ewing sarcoma from its main differential diagnosis (acute osteomyelitis) in paediatric radiographs. 42,608 unstructured radiographs from our musculoskeletal tumour centre were retrieved from the PACS. The images were clustered with a DeepCluster, a self-supervised algorithm. 1000 clusters were used for the upstream task (pretraining). Following, the pretrained classification network was applied for the downstream task of differentiating Ewing sarcoma and acute osteomyelitis. An untrained network achieved an accuracy of 81.5%/54.2%, while an ImageNet-pretrained network resulted in 89.6%/70.8% for validation and testing, respectively. Our transfer learning approach surpassed the best result by 4.4%/17.3% percentage points. Transfer learning demonstrated to be a powerful technique to support image interpretation tasks. Even for small data sets, the impact can be significant. However, transfer learning is not a final solution to small data sets. To achieve clinically relevant results, a structured and systematic data acquisition is of paramount importance.

وصف الملف: electronic resource

العلاقة: https://doaj.org/toc/2364-5504Test

الوصول الحر: https://doaj.org/article/2b1b87b9678b40debaffe3c17f5a174aTest

المؤلفون: Lang Jan J., Baylacher Veronika, Micheler Carina M., Wilhelm Nikolas J., Hinterwimmer Florian, Schwaiger Benedikt, Barnewitz Dirk, Eisenhart-Rothe Rüdiger von, Grosse Christian U., Burgkart Rainer

المصدر: Current Directions in Biomedical Engineering, Vol 8, Iss 2, Pp 129-132 (2022)

مصطلحات موضوعية: osteosynthesis, implant, equine, biomechanical testing, femur, internal fixation, intramedullary nail, Medicine

الوصف: Introduction: Osteosynthesis of the equine femur is still a challenge for veterinary medicine. Even though intramedullary fracture fixation is possible nowadays, the varying geometry of the medullary cavity along the bone axis is a critical factor. Limited contact area between implant and bone can cause insufficient primary stability. In this study, it was investigated whether the osteosynthesis stability can be improved with a form-adaptive reinforcement for the diaphyseal part of the proximal fragment. Material and Methods: Eight equine femora were fitted with intramedullary nail osteosynthesis and analyzed by 4-point bending. Virtual position planning of the ex-vivo implantation using CT-data increased comparability. For five femora the proximal fragment was reinforced with a flexible polymer mixture. Longterm stability was tested via cyclic loading. Bending stiffness and its development due to cyclic loading was evaluated before and after reinforcement procedure. Finally, load-to-failure was tested in the same setup. Results and Discussion: The application of the polymer reinforcement increased the maximum torque in the load-tofailure measurement by 26%. Bending stiffness was not affected in the measured loading range by the reinforcement. Cyclic loading increased bending stiffness for a conditioned state but showed to be reversible for the most part. Conclusion: The fracture adjacent reinforcement showed to be beneficial to the osteosynthesis stability, but further investigation is necessary for surgical application.

وصف الملف: electronic resource

العلاقة: https://doaj.org/toc/2364-5504Test

الوصول الحر: https://doaj.org/article/546fe8eeddb9448a84495287e68db075Test

المؤلفون: Bloier Magdalena, Hinterwimmer Florian, Breden Sebastian, Consalvo Sarah, Neumann Jan, Wilhelm Nikolas, Eisenhart-Rothe Rüdiger von, Rueckert Daniel, Burgkart Rainer

المصدر: Current Directions in Biomedical Engineering, Vol 8, Iss 2, Pp 69-72 (2022)

مصطلحات موضوعية: deep learning, sarcoma, bone tumour, detection, segmentation, Medicine

الوصف: Bone tumours are a rare and often highly malignant entity. Early clinical diagnosis is the most important step, but the difficulty of detecting and assessing bone malignancies is in its radiological peculiarity and limited experience of non-experts. Since X-ray imaging is the first imaging method of bone tumour diagnostics, the purpose of this study is to develop an artificial intelligence (AI) model to detect and segment the tumorous tissue in a radiograph. We investigated which methods are necessary to cope with limited and heterogeneous data. We collected 531 anonymised radiographs from our musculoskeletal tumour centre. In order to adapt to the complexity of recognizing the malignant tissue and cope with limited data, transfer learning, data augmentation as well as several architectures, some of which were initially designed for medical images, were implemented. Furthermore, dataset size was varied by adding another bone tumour entity. We applied a data split of 72%, 18%, 10% for training, validation and testing, respectively. To provide statistical significance and robustness, we applied a cross-validation and image stratification with respect to tumour pixels present. We achieved an accuracy of 99.72% and an intersection over union of 87.43% for hold-out test data by applying several methods to tackle limited data. Transfer learning and additional data brought the greatest performance increase. In conclusion, our model was able to detect and segment tumorous tissue in radiographs with good performance, although it was trained on a very limited amount of data. Transfer Learning and data augmentation proved to significantly mitigate the issue of limited data samples. However, to accomplish clinical significance, more data has to be acquired in the future. Through minor adjustments, the model could be adapted to other musculoskeletal tumour entities and become a general support tool for orthopaedic surgeons and radiologists.

وصف الملف: electronic resource

العلاقة: https://doaj.org/toc/2364-5504Test

الوصول الحر: https://doaj.org/article/fb759cd762374611aaf6e512add63f85Test

المؤلفون: Micheler Carina M., Lang Jan J., Wilhelm Nikolas J., Lazic Igor, Hinterwimmer Florian, Fritz Christian, Eisenhart-Rothe Rüdiger von, Zäh Michael F., Burgkart Rainer H. H.

المصدر: Current Directions in Biomedical Engineering, Vol 8, Iss 2, Pp 797-800 (2022)

مصطلحات موضوعية: pelvis, landmarks, size comparison, scaling, minimal bounding box, minimal bounding sphere, Medicine

الوصف: For the development of new types of hip implants for acetabulum revision, it is beneficial to analyse the acetabular defects of the indication group in advance. In order to be able to specially compare the bone defects with each other, a normalisation and accompanying scaling of the pelvis is necessary. Uniform scaling is required so that the bone structures are not distorted. In the following study, three scaling methods based on the minimal bounding box and sphere principle are compared with a method using 14 landmarks on the pelvis.The landmark method is applied to determine the true scaling factor. For the comparison of the different methods, 40 female pelvic models with an acetabular defect are analysed. In the comparison of the scaling methods, the method using minimal bounding spheres shows the least deviation from the landmark method (mean difference 3.30 ± 2.17 %). Due to the fact that no preprocessing (definition of the landmarks) is required and the fast implementation of the algorithm, the minimal bounding sphere is to be preferred to the landmark method for a fast size estimation.

وصف الملف: electronic resource

العلاقة: https://doaj.org/toc/2364-5504Test

الوصول الحر: https://doaj.org/article/bb7a76cd3ec443e3922a9ed7645f8815Test

المؤلفون: Micheler Carina M., Geck Paulina A., Charitou Fiona, Leix René, Foehr Peter, Lang Jan J., Wilhelm Nikolas J., Tuebel Jutta L., Burgkart Rainer H. H.

المصدر: Current Directions in Biomedical Engineering, Vol 7, Iss 2, Pp 899-902 (2021)

مصطلحات موضوعية: bioreactor, compression, tissue engineering, 3d cell culture, bone cell culture, osteoblasts, Medicine

الوصف: Bioreactors with a controlled physiological environment are being developed to study various cell processes. The influences of mechanostimulation on bone cell cultures can be investigated using a compression bioreactor. The developed bioreactor system applies a cyclic compression force to the specimen via an eccentrically mounted push rod. The compression force is monitored by a force sensor to detect changes in the material properties of the specimen. Depending on the piston setting, a stroke of 0.28 - 2.50 mm can be applied to the specimen. The bioreactor system was tested with a trial run of 18 days. A sample was continuously stimulated with a loading frequency of 2 Hz and a stroke of 1.50 mm. The sterility in the cell chamber as well as the functionality of the realised bioreactor stimulation system could be successfully confirmed

وصف الملف: electronic resource

العلاقة: https://doaj.org/toc/2364-5504Test

الوصول الحر: https://doaj.org/article/43fd2fde41ba41428a017d0ad2c8174cTest

المؤلفون: Wilhelm, Nikolas J., Schacky, Claudio E. von, Lindner, Felix, Feucht, Matthias Jens, Ehmann, Yannick J., Pogorzelski, Jonas, Haddadin, Sami, Neumann, Jan-Oliver, Hinterwimmer, Florian, Eisenhart-Rothe, Rüdiger von, Jung, Matthias, Russe, Maximilian, Izadpanah, Kaywan, Siebenlist, Sebastian, Burgkart, Rainer, Rupp, Marco C.

المصدر: Artificial intelligence in medicine. - 150 (2024) , 102843, ISSN: 1873-2860

الوصف: Osteoarthritis of the knee, a widespread cause of knee disability, is commonly treated in orthopedics due to its rising prevalence. Lower extremity misalignment, pivotal in knee injury etiology and management, necessitates comprehensive mechanical alignment evaluation via frequently-requested weight-bearing long leg radiographs (LLR). Despite LLR’s routine use, current analysis techniques are error-prone and time-consuming. To address this, we conducted a multicentric study to develop and validate a deep learning (DL) model for fully automated leg alignment assessment on anterior–posterior LLR, targeting enhanced reliability and efficiency. The DL model, developed using 594 patients’ LLR and a 60%/10%/30% data split for training, validation, and testing, executed alignment analyses via a multi-step process, employing a detection network and nine specialized networks. It was designed to assess all vital anatomical and mechanical parameters for standard clinical leg deformity analysis and preoperative planning. Accuracy, reliability, and assessment duration were compared with three specialized orthopedic surgeons across two distinct institutional datasets (136 and 143 radiographs). The algorithm exhibited equivalent performance to the surgeons in terms of alignment accuracy (DL: 0.21 ± 0.18°to 1.06 ± 1.3°vs. OS: 0.21 ± 0.16°to 1.72 ± 1.96°), interrater reliability (ICC DL: 0.90 ± 0.05 to 1.0 ± 0.0 vs. ICC OS: 0.90 ± 0.03 to 1.0 ± 0.0), and clinically acceptable accuracy (DL: 53.9%–100% vs OS 30.8%–100%). Further, automated analysis significantly reduced analysis time compared to manual annotation (DL: 22 ± 0.6 s vs. OS; 101.7 ± 7 s, p 0.01). By demonstrating that our algorithm not only matches the precision of expert surgeons but also significantly outpaces them in both speed and consistency of measurements, our research underscores a pivotal advancement in harnessing AI to enhance clinical efficiency and decision-making in orthopaedics.

وصف الملف: pdf

العلاقة: https://freidok.uni-freiburg.de/data/246680Test

الإتاحة: https://doi.org/10.1016/j.artmed.2024.102843Test
https://freidok.uni-freiburg.de/data/246680Test
https://nbn-resolving.org/urn:nbn:de:bsz:25-freidok-2466804Test
https://freidok.uni-freiburg.de/dnb/download/246680Test

المؤلفون: Hinterwimmer, Florian, Serena, Ricardo Smits, Wilhelm, Nikolas, Breden, Sebastian, Consalvo, Sarah, Seidl, Fritz, Juestel, Dominik, Burgkart, Rainer H H, Woertler, Klaus, von Eisenhart-Rothe, Ruediger, Neumann, Jan, Rueckert, Daniel

المساهمون: Institut für Diagnostische und Interventionelle Radiologie (Prof. Makowski), Lehrstuhl für Biologische Bildgebung - Zusammenarbeit mit dem Helmholtz-Zentrum München (Prof. Ntziachristos)

مصطلحات موضوعية: info:eu-repo/classification/ddc

الوصف: OBJECTIVES: To develop an algorithm to link undiagnosed patients to previous patient histories based on radiographs, and simultaneous classification of multiple bone tumours to enable early and specific diagnosis. MATERIALS AND METHODS: For this retrospective study, data from 2000 to 2021 were curated from our database by two orthopaedic surgeons, a radiologist and a data scientist. Patients with complete clinical and pre-therapy radiographic data were eligible. To ensure feasibility, the ten most frequent primary tumour entities, confirmed histologically or by tumour board decision, were included. We implemented a ResNet and transformer model to establish baseline results. Our method extracts image features using deep learning and then clusters the k most similar images to the target image using a hash-based nearest-neighbour recommender approach that performs simultaneous classification by majority voting. The results were evaluated with precision-at-k, accuracy, precision and recall. Discrete parameters were described by incidence and percentage ratios. For continuous parameters, based on a normality test, respective statistical measures were calculated. RESULTS: Included were data from 809 patients (1792 radiographs; mean age 33.73 ± 18.65, range 3-89 years; 443 men), with Osteochondroma (28.31%) and Ewing sarcoma (1.11%) as the most and least common entities, respectively. The dataset was split into training (80%) and test subsets (20%). For k = 3, our model achieved the highest mean accuracy, precision and recall (92.86%, 92.86% and 34.08%), significantly outperforming state-of-the-art models (54.10%, 55.57%, 19.85% and 62.80%, 61.33%, 23.05%). CONCLUSION: Our novel approach surpasses current models in tumour classification and links to past patient data, leveraging expert insights. CLINICAL RELEVANCE STATEMENT: The proposed algorithm could serve as a vital support tool for clinicians and general practitioners with limited experience in bone tumour classification by identifying similar cases and ...

العلاقة: https://mediatum.ub.tum.de/1738314Test; https://link.springer.com/article/10.1007/s00330-024-10672-0Test

الإتاحة: https://doi.org/10.1007/s00330-024-10672-0Test
https://mediatum.ub.tum.de/1738314Test

المؤلفون: Schacky, Claudio E. von, Wilhelm, Nikolas J., Schäfer, Valerie S., Leonhardt, Yannik, Jung, Matthias, Jungmann, Pia M., Russe, Maximilian, Foreman, Sarah C., Gassert, Felix G., Gassert, Florian T., Schwaiger, Benedikt J., Mogler, Carolin, Knebel, Carolin, Eisenhart-Rothe, Rüdiger von, Makowski, Marcus R., Wörtler, Klaus, Burgkart, Rainer, Gersing, Alexandra S.

المصدر: European radiology. - 32, 9 (2022) , 6247-6257, ISSN: 1432-1084

الوصف: Objectives To develop and validate machine learning models to distinguish between benign and malignant bone lesions and compare the performance to radiologists. Methods In 880 patients (age 33.1 ± 19.4 years, 395 women) diagnosed with malignant (n = 213, 24.2%) or benign (n = 667, 75.8%) primary bone tumors, preoperative radiographs were obtained, and the diagnosis was established using histopathology. Data was split 70%/15%/15% for training, validation, and internal testing. Additionally, 96 patients from another institution were obtained for external testing. Machine learning models were developed and validated using radiomic features and demographic information. The performance of each model was evaluated on the test sets for accuracy, area under the curve (AUC) from receiver operating characteristics, sensitivity, and specificity. For comparison, the external test set was evaluated by two radiology residents and two radiologists who specialized in musculoskeletal tumor imaging. Results The best machine learning model was based on an artificial neural network (ANN) combining both radiomic and demographic information achieving 80% and 75% accuracy at 75% and 90% sensitivity with 0.79 and 0.90 AUC on the internal and external test set, respectively. In comparison, the radiology residents achieved 71% and 65% accuracy at 61% and 35% sensitivity while the radiologists specialized in musculoskeletal tumor imaging achieved an 84% and 83% accuracy at 90% and 81% sensitivity, respectively. Conclusions An ANN combining radiomic features and demographic information showed the best performance in distinguishing between benign and malignant bone lesions. The model showed lower accuracy compared to specialized radiologists, while accuracy was higher or similar compared to residents.

وصف الملف: pdf

العلاقة: https://freidok.uni-freiburg.de/data/226439Test

الإتاحة: https://doi.org/10.1007/s00330-022-08764-wTest
https://freidok.uni-freiburg.de/data/226439Test
https://nbn-resolving.org/urn:nbn:de:bsz:25-freidok-2264392Test
https://freidok.uni-freiburg.de/dnb/download/226439Test