Automated classification of otitis media in pediatric OCT images: Augmenting with gold-standard animal model data

2021 ◽  
Author(s):  
Guillermo L. Monroy ◽  
Jungeun Won ◽  
Darold Spillman ◽  
Stephen A. Boppart
Keyword(s):  
Animal Model ◽  
Otitis Media ◽  
Gold Standard ◽  
Automated Classification ◽  
Model Data ◽  
Standard Animal

Automated classification of copy number variants based on 2019 ACMG standards

2021 ◽  
Vol 132 ◽  
pp. S287-S288
Author(s):  
Jianling Ji ◽  
Ryan Schmidt ◽  
Westley Sherman ◽  
Ryan Peralta ◽  
Megan Roytman ◽  
...  
Keyword(s):  
Copy Number ◽  
Copy Number Variants ◽  
Automated Classification

scholarly journals Ensemble-based Bag of Features for Automated Classification of Normal and COVID-19 CXR Images

2021 ◽  
pp. 102656
Author(s):  
Amira S. Ashour ◽  
Merihan M. Eissa ◽  
Maram A. Wahba ◽  
Radwa A. Elsawy ◽  
Hamada Fathy Elgnainy ◽  
...  
Keyword(s):  
Automated Classification ◽  
Bag Of Features

scholarly journals Implementation of a deep learning model for automated classification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) in real time

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Song-Quan Ong ◽  
Hamdan Ahmad ◽  
Gomesh Nair ◽  
Pradeep Isawasan ◽  
Abdul Hafiz Ab Majid
Keyword(s):  
Deep Learning ◽  
Aedes Aegypti ◽  
Real Time ◽  
Aedes Albopictus ◽  
Automated Classification ◽  
Expert Performance ◽  
Deep Convolutional Neural Networks ◽  
Significant Difference ◽  
Set Up

AbstractClassification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) by humans remains challenging. We proposed a highly accessible method to develop a deep learning (DL) model and implement the model for mosquito image classification by using hardware that could regulate the development process. In particular, we constructed a dataset with 4120 images of Aedes mosquitoes that were older than 12 days old and had common morphological features that disappeared, and we illustrated how to set up supervised deep convolutional neural networks (DCNNs) with hyperparameter adjustment. The model application was first conducted by deploying the model externally in real time on three different generations of mosquitoes, and the accuracy was compared with human expert performance. Our results showed that both the learning rate and epochs significantly affected the accuracy, and the best-performing hyperparameters achieved an accuracy of more than 98% at classifying mosquitoes, which showed no significant difference from human-level performance. We demonstrated the feasibility of the method to construct a model with the DCNN when deployed externally on mosquitoes in real time.

scholarly journals Validity of classification of distal radial fractures in the Swedish fracture register

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Malena Bergvall ◽  
Carl Bergdahl ◽  
Carl Ekholm ◽  
David Wennergren
Keyword(s):  
Gold Standard ◽  
Reference Group ◽  
Observer Agreement ◽  
Distal Radial Fractures ◽  
Young Males ◽  
Small Peak ◽  
National Quality ◽  
Fracture Register ◽  
Standard Classification

Abstract Background Distal radial fractures (DRF) are one of the most common fractures with a small peak in incidence among young males and an increasing incidence with age among women. The reliable classification of fractures is important, as classification provides a framework for communicating effectively on clinical cases. Fracture classification is also a prerequisite for data collection in national quality registers and for clinical research. Since its inception in 2011, the Swedish Fracture Register (SFR) has collected data on more than 490,000 fractures. The attending physician classifies the fracture according to the AO/OTA classification upon registration in the SFR. Previous studies regarding the classification of distal radial fractures (DRF) have shown difficulties in inter- and intra-observer agreement. This study aims to assess the accuracy of the registration of DRF in adults in the SFR as it is carried out in clinical practice. Methods A reference group of three experienced orthopaedic trauma surgeons classified 128 DRFs, randomly retrieved from the SFR, at two classification sessions 6 weeks apart. The classification the reference group agreed on was regarded as the gold standard classification for each fracture. The accuracy of the classification in the SFR was defined as the agreement between the gold standard classification and the classification in the SFR. Inter- and intra-observer agreement was evaluated and the degree of agreement was calculated as Cohen’s kappa. Results The accuracy of the classification of DRF in the SFR was kappa = 0.41 (0.31–0.51) for the AO/OTA subgroup/group and kappa = 0.48 (0.36–0.61) for the AO/OTA type. This corresponds to moderate agreement. Inter-observer agreement ranged from kappa 0.22–0.48 for the AO/OTA subgroup/group and kappa 0.48–0.76 for the AO/OTA type. Intra-observer agreement ranged from kappa 0.52–0.70 for the AO/OTA subgroup/group and kappa 0.71–0.76 for the AO/OTA type. Conclusions The study shows moderate accuracy in the classification of DRF in the SFR. Although the degree of accuracy for DRF appears to be lower than for other fracture locations, the accuracy shown in the current study is similar to that in previous studies of DRF.

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