An analytical method to create patient-specific deformed bone models using X-ray images and a healthy bone model

A new ring fixator system for automated bone fixation

BACKGROUND

In the field of orthopaedics, external fixators are commonly employed for treating extremity fractures and deformities. Computer-assisted systems offer a promising and less error-prone treatment alternative to manual fixation by utilising a software to plan treatments based on radiological and clinical data. Nevertheless, existing computer-assisted systems have limitations and constraints.

METHODS

This work represents the culmination of a project aimed at developing a new automatised fixation system and a corresponding software to minimise human intervention and associated errors, and the developed system incorporates enhanced functionalities and has fewer constraints compared to existing systems.

RESULTS

The automatised fixation system and its graphical user interface (GUI) demonstrate promising results in terms of accuracy, efficiency, and reliability.

CONCLUSION

The developed fixation system and its accompanying GUI represent an improvement in computer-assisted fixation systems. Future research may focus on further refining the system and conducting clinical trials.

Database of segmentations and surface models of bones of the entire lower body created from cadaver CT scans

The range of applications of digital surface models of the bones in science and industry is wide. Three-dimensional reconstructions of bones are used in biomechanics, biomedical engineering, medical image processing, orthopedics, traumatology, radiology, patient education, anatomy, anthropometry, forensic anthropology, ergonomics, usability and human factors engineering, or accident and injury analysis and prevention. No open access database or repository of skeletal surface models of the full lower extremities exists. Therefore, the objective of this publication was to provide access to consistent complete bone models of the pelvis and lower limbs of multiple subjects, including biometric data. Segmentations and surface models of the bones of the lower extremities of more than twenty subjects were created from open access postmortem whole-body computed tomography scans. The database provides a broad range of applications by giving access to the data of the complete process chain, from the raw medical imaging data through the segmentations to the surface models.

Evolving Deep Learning Convolutional Neural Networks for Early COVID-19 Detection in Chest X-ray Images

This article proposes a framework that automatically designs classifiers for the early detection of COVID-19 from chest X-ray images. To do this, our approach repeatedly makes use of a heuristic for optimisation to efficiently find the best combination of the hyperparameters of a convolutional deep learning model. The framework starts with optimising a basic convolutional neural network which represents the starting point for the evolution process. Subsequently, at most two additional convolutional layers are added, at a time, to the previous convolutional structure as a result of a further optimisation phase. Each performed phase maximises the the accuracy of the system, thus requiring training and assessment of the new model, which gets gradually deeper, with relevant COVID-19 chest X-ray images. This iterative process ends when no improvement, in terms of accuracy, is recorded. Hence, the proposed method evolves the most performing network with the minimum number of convolutional layers. In this light, we simultaneously achieve high accuracy while minimising the presence of redundant layers to guarantee a fast but reliable model. Our results show that the proposed implementation of such a framework achieves accuracy up to 99.11%, thus being particularly suitable for the early detection of COVID-19.