AIMC Topic: Bone and Bones

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Biohybrid Variable-Stiffness Soft Actuators that Self-Create Bone.

Advanced materials (Deerfield Beach, Fla.)
Inspired by the dynamic process of initial bone development, in which a soft tissue turns into a solid load-bearing structure, the fabrication, optimization, and characterization of bioinduced variable-stiffness actuators that can morph in various sh...

Bone suppression on pediatric chest radiographs via a deep learning-based cascade model.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Bone suppression images (BSIs) of chest radiographs (CXRs) have been proven to improve diagnosis of pulmonary diseases. To acquire BSIs, dual-energy subtraction (DES) or a deep-learning-based model trained with DES-based BSI...

Generation of Vertebra Micro-CT-like Image from MDCT: A Deep-Learning-Based Image Enhancement Approach.

Tomography (Ann Arbor, Mich.)
This paper proposes a deep-learning-based image enhancement approach that can generate high-resolution micro-CT-like images from multidetector computed tomography (MDCT). A total of 12,500 MDCT and micro-CT image pairs were obtained from 25 vertebral...

Deep learning methods for automatic segmentation of lower leg muscles and bones from MRI scans of children with and without cerebral palsy.

NMR in biomedicine
Cerebral palsy is a neurological condition that is known to affect muscle growth. Detailed investigations of muscle growth require segmentation of muscles from MRI scans, which is typically done manually. In this study, we evaluated the performance o...

Multitask Deep Learning for Segmentation and Classification of Primary Bone Tumors on Radiographs.

Radiology
Background An artificial intelligence model that assesses primary bone tumors on radiographs may assist in the diagnostic workflow. Purpose To develop a multitask deep learning (DL) model for simultaneous bounding box placement, segmentation, and cla...

Automatic identification of suspicious bone metastatic lesions in bone scintigraphy using convolutional neural network.

BMC medical imaging
BACKGROUND: We aimed to construct an artificial intelligence (AI) guided identification of suspicious bone metastatic lesions from the whole-body bone scintigraphy (WBS) images by convolutional neural networks (CNNs).

Deep learning approach to assess damage mechanics of bone tissue.

Journal of the mechanical behavior of biomedical materials
Machine learning methods have the potential to transform imaging techniques and analysis for healthcare applications with automation, making diagnostics and treatment more accurate and efficient, as well as to provide mechanistic insights into tissue...

dSPIC: a deep SPECT image classification network for automated multi-disease, multi-lesion diagnosis.

BMC medical imaging
BACKGROUND: Functional imaging especially the SPECT bone scintigraphy has been accepted as the effective clinical tool for diagnosis, treatment, evaluation, and prevention of various diseases including metastasis. However, SPECT imaging is brightly c...

Estimating Reference Bony Shape Models for Orthognathic Surgical Planning Using 3D Point-Cloud Deep Learning.

IEEE journal of biomedical and health informatics
Orthognathic surgical outcomes rely heavily on the quality of surgical planning. Automatic estimation of a reference facial bone shape significantly reduces experience-dependent variability and improves planning accuracy and efficiency. We propose an...

Bone collision detection method for robot assisted fracture reduction based on force curve slope.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: The application of robot technology in fracture reduction ensures the minimal invasiveness and accurate operation process. Most of the existing robot assisted fracture reduction systems don't have the function of bone collis...