Challenges related to artificial intelligence research in medical imaging and the importance of image analysis competitions

LM Prevedello, SS Halabi, G Shih, CC Wu… - Radiology: Artificial …, 2019 - pubs.rsna.org
In recent years, there has been enormous interest in applying artificial intelligence (AI) to
radiology. Although some of this interest may have been driven by exaggerated …

[HTML][HTML] Bone age assessment with various machine learning techniques: a systematic literature review and meta-analysis

AL Dallora, P Anderberg, O Kvist, E Mendes… - PloS one, 2019 - journals.plos.org
BACKGROUND The assessment of bone age and skeletal maturity and its comparison to
chronological age is an important task in the medical environment for the diagnosis of …

[HTML][HTML] Age-group determination of living individuals using first molar images based on artificial intelligence

S Kim, YH Lee, YK Noh, FC Park, QS Auh - Scientific reports, 2021 - nature.com
Dental age estimation of living individuals is difficult and challenging, and there is no
consensus method in adults with permanent dentition. Thus, we aimed to provide an …

RAGCN: Region aggregation graph convolutional network for bone age assessment from X-ray images

X Li, Y Jiang, Y Liu, J Zhang, S Yin… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Rapid and accurate measurement of bone age from hand X-ray images is a significant task
for children's maturity assessment and metabolic disorders diagnosis. With the development …

An ensemble-based densely-connected deep learning system for assessment of skeletal maturity

S Wang, X Wang, Y Shen, B He, X Zhao… - … on Systems, Man …, 2020 - ieeexplore.ieee.org
Assessment of skeletal maturity is important for a clinician to make a decision of the most
appropriate treatment on various skeletal disorders. This task is very challenging when …

[HTML][HTML] Urban poverty maps-From characterising deprivation using geo-spatial data to capturing deprivation from space

E Luo, M Kuffer, J Wang - Sustainable Cities and Society, 2022 - Elsevier
Most earth observation (EO) approaches only yield a binary delineation of deprived/non-
deprived areas–an oversimplified characterisation with little information inferred regarding …

[HTML][HTML] Emerging applications of deep learning in bone tumors: current advances and challenges

X Zhou, H Wang, C Feng, R Xu, Y He, L Li… - Frontiers in oncology, 2022 - frontiersin.org
Deep learning is a subfield of state-of-the-art artificial intelligence (AI) technology, and
multiple deep learning-based AI models have been applied to musculoskeletal diseases …

Fully automatic model based on se-resnet for bone age assessment

J He, D Jiang - IEEE access, 2021 - ieeexplore.ieee.org
Bone age assessment (BAA) based on hand X-ray imaging is a common clinical practice for
investigating disorders and predicting the adult height of a child. However, the traditional …

[PDF][PDF] Traditional and new methods of bone age assessment-an overview

M Prokop-Piotrkowska… - Journal of Clinical …, 2021 - jag.journalagent.com
Bone age is one of biological indicators of maturity used in clinical practice and it is a very
important parameter of a child's assessment, especially in paediatric endocrinology. The …

Faster region-convolutional neural network oriented feature learning with optimal trained recurrent neural network for bone age assessment for pediatrics

S Deshmukh, A Khaparde - Biomedical Signal Processing and Control, 2022 - Elsevier
This paper tactics to develop the novel Tanner-Whitehouse 3 (TW3)-based automated Bone
Age Assessment (BAA) model for children with the assistance of Faster Region …