Deep Learning Framework for Automatic Bone Age Assessment

Mehta, C. H.; Ayeesha, Bibi; Sotakanal, Ayesha; R, Nirmala S.; Desai, Shrinivas D.; K, Venkata Suryanarayana; Ganguly, Ashes Dhanna; Shetty, Veerendra

doi:10.1109/embc46164.2021.9629650

Cited by 9 publications

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As mentioned above, the additional manual annotation is tedious and time-consuming. Furthermore, Wu et al (2018), Mehta et al (2021), Yang et al (2021) and our method detects the specific bone area automatically without any additional manual annotations and fed the critical bone area images into the age prediction network as input. In general, the experimental results show that our method performs better than the majority of current evaluation methods without additional labor annotation costs.…”

Section: Resultsmentioning

confidence: 99%

Bone age assessment based on deep neural networks with annotation-free cascaded critical bone region extraction

Chen

et al. 2023

Front. Artif. Intell.

View full text Add to dashboard Cite

Bone age assessment (BAA) from hand radiographs is crucial for diagnosing endocrinology disorders in adolescents and supplying therapeutic investigation. In practice, due to the conventional clinical assessment being a subjective estimation, the accuracy of BAA relies highly on the pediatrician's professionalism and experience. Recently, many deep learning methods have been proposed for the automatic estimation of bone age and had good results. However, these methods do not exploit sufficient discriminative information or require additional manual annotations of critical bone regions that are important biological identifiers in skeletal maturity, which may restrict the clinical application of these approaches. In this research, we propose a novel two-stage deep learning method for BAA without any manual region annotation, which consists of a cascaded critical bone region extraction network and a gender-assisted bone age estimation network. First, the cascaded critical bone region extraction network automatically and sequentially locates two discriminative bone regions via the visual heat maps. Second, in order to obtain an accurate BAA, the extracted critical bone regions are fed into the gender-assisted bone age estimation network. The results showed that the proposed method achieved a mean absolute error (MAE) of 5.45 months on the public dataset Radiological Society of North America (RSNA) and 3.34 months on our private dataset.

show abstract

Section: Resultsmentioning

confidence: 99%

Bone age assessment based on deep neural networks with annotation-free cascaded critical bone region extraction

Chen

et al. 2023

Front. Artif. Intell.

View full text Add to dashboard Cite

show abstract

“…The datasets included 12,611 hand radiographs and a validation set of 1425 Xrays, and a separate test set of 200 images. Metha et al, for example, applied transfer learning to pre-train a neural network architecture obtaining a difference between the actual and estimated age of 5.9 months [55].…”

Section: Ai-based Approachesmentioning

confidence: 99%

Artificial Intelligence (AI)-Based Systems for Automatic Skeletal Maturity Assessment through Bone and Teeth Analysis: A Revolution in the Radiological Workflow?

Caloro

Cè

Gibelli³

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

Bone age is an indicator of bone maturity and is useful for the treatment of different pediatric conditions as well as for legal issues. Bone age can be assessed by the analysis of different skeletal segments and teeth and through several methods; however, traditional bone age assessment is a complicated and time-consuming process, prone to inter- and intra-observer variability. There is a high demand for fully automated systems, but creating an accurate and reliable solution has proven difficult. Deep learning technology, machine learning, and Convolutional Neural Networks-based systems, which are rapidly evolving, have shown promising results in automated bone age assessment. We provide the background of bone age estimation, its usefulness and traditional methods of assessment, and review the currently artificial-intelligence-based solutions for bone age assessment and the future perspectives of these applications.

show abstract