AIMC Topic: Extremities
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An improved AlexNet deep learning method for limb tumor cancer prediction and detection.
Biomedical physics & engineering express
Nov 5, 2024
Synovial sarcoma (SS) is a rare cancer that forms in soft tissues around joints, and early detection is crucial for improving patient survival rates. This study introduces a convolutional neural network (CNN) using an improved AlexNet deep learning c...
Universal optimal design in the vertebrate limb pattern and lessons for bioinspired design.
Bioinspiration & biomimetics
Aug 9, 2024
This paper broadly summarizes the variation of design features found in vertebrate limbs and analyses the resultant versatility and multifunctionality in order to make recommendations for bioinspired robotics. The vertebrate limb pattern (e.g. should...
Development and benchmarking of a Deep Learning-based MRI-guided gross tumor segmentation algorithm for Radiomics analyses in extremity soft tissue sarcomas.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
May 22, 2024
BACKGROUND: Volume of interest (VOI) segmentation is a crucial step for Radiomics analyses and radiotherapy (RT) treatment planning. Because it can be time-consuming and subject to inter-observer variability, we developed and tested a Deep Learning-b...
Development and external validation of a machine learning model for prediction of survival in extremity leiomyosarcoma.
Surgical oncology
Mar 7, 2024
PURPOSE: Machine learning (ML) models have been used to predict cancer survival in several sarcoma subtypes. However, none have investigated extremity leiomyosarcoma (LMS). ML is a powerful tool that has the potential to better prognosticate extremit...
Assessing the Potential of a Deep Learning Tool to Improve Fracture Detection by Radiologists and Emergency Physicians on Extremity Radiographs.
Academic radiology
Nov 22, 2023
RATIONALE AND OBJECTIVES: To evaluate the standalone performance of a deep learning (DL) based fracture detection tool on extremity radiographs and assess the performance of radiologists and emergency physicians in identifying fractures of the extrem...
Mangled extremity: limb salvage for reconstruction versus primary amputation.
Current opinion in critical care
Oct 16, 2023
PURPOSE OF REVIEW: While MESS has historically influenced limb salvage versus amputation decisions, its universal applicability remains uncertain. With trauma systems expanding and advancements in trauma care, the need for a nuanced understanding of ...
A deep learning model for accurately predicting cancer-specific survival in patients with primary bone sarcoma of the extremity: a population-based study.
Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico
Aug 8, 2023
PURPOSE: Primary bone and joint sarcomas of the long bone are relatively rare neoplasms with poor prognosis. An efficient clinical tool that can accurately predict patient prognosis is not available. The current study aimed to use deep learning algor...
CORR Insights®: Does the SORG Machine-learning Algorithm for Extremity Metastases Generalize to a Contemporary Cohort of Patients? Temporal Validation From 2016 to 2020.
Clinical orthopaedics and related research
Jun 19, 2023
Comparing end-effector position and joint angle feedback for online robotic limb tracking.
PloS one
Jun 8, 2023
Somatosensation greatly increases the ability to control our natural body. This suggests that supplementing vision with haptic sensory feedback would also be helpful when a user aims at controlling a robotic arm proficiently. However, whether the pos...
Design of Low-Complexity Convolutional Neural Network Accelerator for Finger Vein Identification System.
Sensors (Basel, Switzerland)
Feb 15, 2023
In the biometric field, vein identification is a vital process that is constrained by the invisibility of veins as well as other unique features. Moreover, users generally do not wish to have their personal information uploaded to the cloud, so edge ...
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