AIMC Topic: Femur

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Predicting trabecular arrangement in the proximal femur: An artificial neural network approach for varied geometries and load cases.

Journal of biomechanics
Machine learning (ML) and deep learning (DL) approaches can solve the same problems as the finite element method (FEM) with a high degree of accuracy in a fraction of the required time, by learning from previously presented data. In this work, the bo...

Enhanced Deep-Learning-Based Automatic Left-Femur Segmentation Scheme with Attribute Augmentation.

Sensors (Basel, Switzerland)
This research proposes augmenting cropped computed tomography (CT) slices with data attributes to enhance the performance of a deep-learning-based automatic left-femur segmentation scheme. The data attribute is the lying position for the left-femur m...

Body height estimation from automated length measurements on standing long leg radiographs using artificial intelligence.

Scientific reports
Artificial-intelligence (AI) allows large-scale analyses of long-leg-radiographs (LLRs). We used this technology to derive an update for the classical regression formulae by Trotter and Gleser, which are frequently used to infer stature based on long...

Robot-assisted implantation of additively manufactured patient-specific orthopaedic implants: evaluation in a sheep model.

International journal of computer assisted radiology and surgery
PURPOSE: Bone tumours must be surgically excised in one piece with a margin of healthy tissue. The unique nature of each bone tumour case is well suited to the use of patient-specific implants, with additive manufacturing allowing production of highl...

Auto-segmentation of the tibia and femur from knee MR images via deep learning and its application to cartilage strain and recovery.

Journal of biomechanics
The ability to efficiently and reproducibly generate subject-specific 3D models of bone and soft tissue is important to many areas of musculoskeletal research. However, methodologies requiring such models have largely been limited by lengthy manual s...

Stature estimation by semi-automatic measurements of 3D CT images of the femur.

International journal of legal medicine
Stature estimation is one of the most basic and important methods of personal identification. The long bones of the limbs provide the most accurate stature estimation, with the femur being one of the most useful. In all the previously reported method...

Radiographic findings involved in knee osteoarthritis progression are associated with pain symptom frequency and baseline disease severity: a population-level analysis using deep learning.

Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA
PURPOSE: To (1) develop a deep-learning (DL) algorithm capable of producing limb-length and knee-alignment measurements, and (2) determine the association between limb-length discrepancy (LLD), coronal-plane alignment, osteoarthritis (OA) severity, a...

Femoral image segmentation based on two-stage convolutional network using 3D-DMFNet and 3D-ResUnet.

Computer methods and programs in biomedicine
OBJECTIVE: The femur is a typical human long bone with an irregular spatial structure. Femoral fractures are the most common occurrence in middle-aged and older adults. The structure of human bone tissue is very complex, and there are significant dif...

Ensemble deep learning model for predicting anterior cruciate ligament tear from lateral knee radiograph.

Skeletal radiology
OBJECTIVE: To develop an ensemble deep learning model (DLM) predicting anterior cruciate ligament (ACL) tears from lateral knee radiographs and to evaluate its diagnostic performance.

Automatic segmentation model of intercondylar fossa based on deep learning: a novel and effective assessment method for the notch volume.

BMC musculoskeletal disorders
BACKGROUND: Notch volume is associated with anterior cruciate ligament (ACL) injury. Manual tracking of intercondylar notch on MR images is time-consuming and laborious. Deep learning has become a powerful tool for processing medical images. This stu...