Fast Data-Driven Readout System for the Wide Aperture Silicon Tracking System of the BM@N Experiment

Computer aided orthopedic surgery suffers from low clinical adoption, despite increased accuracy and patient safety. This can partly be attributed to cumbersome and often radiation intensive registration methods. Emerging RGB-D sensors combined with artificial intelligence data-driven methods have the potential to streamline these procedures. However, developing such methods requires vast amount of data. To this end, a multi-modal approach that enables acquisition of large clinical data, tailored to pedicle screw placement, using RGB-D sensors and a co-calibrated high-end optical tracking system was developed. The resulting dataset comprises RGB-D recordings of pedicle screw placement along with individually tracked ground truth poses and shapes of spine levels L1–L5 from ten cadaveric specimens. Besides a detailed description of our setup, quantitative and qualitative outcome measures are provided. We found a mean target registration error of 1.5 mm. The median deviation between measured and ground truth bone surface was 2.4 mm. In addition, a surgeon rated the overall alignment based on 10% random samples as 5.8 on a scale from 1 to 6. Generation of labeled RGB-D data for orthopedic interventions with satisfactory accuracy is feasible, and its publication shall promote future development of data-driven artificial intelligence methods for fast and reliable intraoperative registration.

Download Full-text

Data Driven Enhancements to the Intestinal Integrity (I2) Index: A Novel Approach to Support Poultry Sustainability

Agriculture ◽

10.3390/agriculture10080320 ◽

2020 ◽

Vol 10 (8) ◽

pp. 320

Author(s):

Alexandra L. Swirski ◽

Hind Kasab-Bachi ◽

Jocelyn Rivers ◽

Jeffrey B. Wilson

Keyword(s):

Production Efficiency ◽

Tracking System ◽

Average Daily Gain ◽

Data Driven ◽

Performance Parameters ◽

Feed Conversion ◽

Intestinal Integrity ◽

Novel Approach ◽

Comprehensive Health ◽

And Performance

Background: Optimizing the intestinal integrity of poultry flocks through a comprehensive index measure, such as the intestinal integrity (I2) index, could help to promote sustainable production in the poultry industry. The I2 index is a tool for assessing the intestinal health of flocks based on flock level health and performance data, captured by Elanco Animal Health’s global surveillance system, i.e., the Health Tracking System (HTSi). The objectives of this study were to evaluate the relationships between the proposed I2 index and each of the following four performance parameters: average daily gain (ADG), feed conversion ratio (FCR), European production efficiency factor (EPEF), and percent livability; and compare the ability of the proposed I2 index to predict these performance parameters with the current I2 index. Results: The proposed I2 index was found to produce a greater range and increased variation in flock level I2 index scores as compared with the current I2 index. The proposed I2 index was found to predict the four performance measures at least as well as the current I2 index, and the results suggested that the proposed I2 index could be superior at predicting ADG, EPEF, and percent livability. Conclusion: Our results highlight the strength of data-driven approaches in the development and improvement of comprehensive health metrics.

Download Full-text

Sensor-fusion Location Tracking System using Hybrid Multimodal Deep Neural Network

10.20944/preprints202109.0301.v1 ◽

2021 ◽

Author(s):

Xijia Wei ◽

Zhiqiang Wei ◽

Valentin Radu

Keyword(s):

Neural Network ◽

Good Accuracy ◽

Indoor Localization ◽

Tracking System ◽

Data Driven ◽

Location Tracking ◽

Neural Architecture ◽

Data Driven Approach ◽

Complex Features ◽

The Hard Problem

Many engineered approaches have been proposed over the years for solving the hard problem of performing indoor localization. However, specialising solutions for the edge cases remains challenging. Here we propose to build the solution with zero hand-engineered features, but having everything learned directly from data. We use a modality specific neural architecture for extracting preliminary features, which are then integrated with cross-modality neural network structures. We show that each modality-specific neural architecture branch is capable of estimating the location with good accuracy independently. But for better accuracy a cross-modality neural network fusing the features of those early modality-specific representations is a better proposition. Our multimodal neural network, MM-Loc, is effective because it allows the uniform flow of gradients during training across modalities. Because it is a data driven approach, complex features representations are learned rather than relying heavily on hand-engineered features.

Download Full-text