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Deep learning algorithm enables automated Cobb angle measurements with high accuracy.
Skeletal Radiol
December 2024
Department of Radiology, Chobanian and Avedisian School of Medicine, Boston University, Boston, MA, USA.
Objective: To determine the accuracy of automatic Cobb angle measurements by deep learning (DL) on full spine radiographs.
Materials And Methods: Full spine radiographs of patients aged > 2 years were screened using the radiology reports to identify radiographs for performing Cobb angle measurements. Two senior musculoskeletal radiologists and one senior orthopedic surgeon independently annotated Cobb angles exceeding 7° indicating the angle location as either proximal thoracic (apices between T3 and T5), main thoracic (apices between T6 and T11), or thoraco-lumbar (apices between T12 and L4).
Mark3D - A semi-automated open-source toolbox for 3D head- surface reconstruction and electrode position registration using a smartphone camera video.
Med Biol Eng Comput
November 2024
Department of Biomedical Engineering, Indian Institute of Technology, Hyderabad, India.
Source localization in EEG necessitates co-registering the EEG sensor locations with the subject's MRI, where EEG sensor locations are typically captured using electromagnetic tracking or 3D scanning of the subject's head with EEG cap, using commercially available 3D scanners. Both methods have drawbacks, where, electromagnetic tracking is slow and immobile, while 3D scanners are expensive. Photogrammetry offers a cost-effective alternative but requires multiple photos to sample the head, with good spatial sampling to adequately reconstruct the head surface.
View Article and Find Full Text PDFToward widespread use of virtual trials in medical imaging innovation and regulatory science.
Med Phys
December 2024
Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Departments of Radiology and Electrical & Computer Engineering, Medical Physics Graduate Program, Duke University, Durham, North Carolina, USA.
Article Synopsis
- * Virtual trials (in silico trials) offer a viable alternative by employing computational models, but there’s a pressing need for a unified framework that the medical imaging community can adopt.
- * Essential requirements for these virtual trial frameworks include ensuring credibility through rigorous assessments, enhancing reproducibility with thorough documentation, and improving accessibility via user-friendly tools and data-sharing solutions.
Not-so-disposable e-cigarettes: Methods young people use to discard single-use e-cigarettes.
Addiction
June 2024
Schroeder Institute, Truth Initiative, Washington, DC, USA.
Background And Aims: To date, most tobacco product waste research focuses on cigarettes. Less is known about single-use 'disposable' e-cigarette waste, which contains several hazardous and toxic materials. This exploratory study examines self-reported methods for discarding disposables among a national sample of US adolescents and young adults.
View Article and Find Full Text PDFComparison of Machine Learning Algorithms for Heartbeat Detection Based on Accelerometric Signals Produced by a Smart Bed.
Sensors (Basel)
March 2024
Department of Engineering and Architecture, University of Parma, 43124 Parma, Italy.
This work aims to compare the performance of Machine Learning (ML) and Deep Learning (DL) algorithms in detecting users' heartbeats on a smart bed. Targeting non-intrusive, continuous heart monitoring during sleep time, the smart bed is equipped with a 3D solid-state accelerometer. Acceleration signals are processed through an STM 32-bit microcontroller board and transmitted to a PC for recording.
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