Evidence map›Paper›PMID 39090563›Full record

ArticleBMC medical imaging2024

Deep learning-based automated bone age estimation for Saudi patients on hand radiograph images: a retrospective study.

Zuhal Y Hamd, Amal I Alorainy, Mohammed A Alharbi, Anas Hamdoun, Arwa Alkhedeiri, Shaden Alhegail, Nurul Absar, Mayeen Uddin Khandaker, Alexander F I Osman

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Article in BMC medical imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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3 · Its place in the literature

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2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Zuhal Y HamdDepartment of Radiological Sciences, College of Health and Rehabilitation Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia.
Amal I AlorainyDepartment of Radiological Sciences, College of Health and Rehabilitation Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia.
Mohammed A AlharbiMedical Imaging Department, KAAUH, Riyadh, Saudi Arabia.
Anas HamdounMedical Imaging Department, KAAUH, Riyadh, Saudi Arabia.
Arwa AlkhedeiriMedical Imaging Department, KAAUH, Riyadh, Saudi Arabia.
Shaden AlhegailMedical Imaging Department, KAAUH, Riyadh, Saudi Arabia.
Nurul AbsarDepartment of Computer Science & Engineering, BGC Trust University Bangladesh, Chittagong, 4301, Bangladesh.
Mayeen Uddin KhandakerApplied Physics and Radiation Technologies Group, CCDCU, School of Engineering and Technology, Sunway University, Bandar Sunway, Subang jaya, 47500, Malaysia.
Alexander F I OsmanDepartment of Medical Physics, Al-Neelain University, Khartoum, 11121, Sudan. alexanderfadul@yahoo.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeIn pediatric medicine, precise estimation of bone age is essential for skeletal maturity evaluation, growth disorder diagnosis, and therapeutic intervention planning. Conventional techniques for determining bone age depend on radiologists' subjective judgments, which may lead to non-negligible differences in the estimated bone age. This study proposes a deep learning-based model utilizing a fully connected convolutional neural network(CNN) to predict bone age from left-hand radiographs.

methodsThe data set used in this study, consisting of 473 patients, was retrospectively retrieved from the PACS (Picture Achieving and Communication System) of a single institution. We developed a fully connected CNN consisting of four convolutional blocks, three fully connected layers, and a single neuron as output. The model was trained and validated on 80% of the data using the mean-squared error as a cost function to minimize the difference between the predicted and reference bone age values through the Adam optimization algorithm. Data augmentation was applied to the training and validation sets yielded in doubling the data samples. The performance of the trained model was evaluated on a test data set (20%) using various metrics including, the mean absolute error (MAE), median absolute error (MedAE), root-mean-squared error (RMSE), and mean absolute percentage error (MAPE). The code of the developed model for predicting the bone age in this study is available publicly on GitHub at https://github.com/afiosman/deep-learning-based-bone-age-estimation .

resultsExperimental results demonstrate the sound capabilities of our model in predicting the bone age on the left-hand radiographs as in the majority of the cases, the predicted bone ages and reference bone ages are nearly close to each other with a calculated MAE of 2.3 [1.9, 2.7; 0.95 confidence level] years, MedAE of 2.1 years, RMAE of 3.0 [1.5, 4.5; 0.95 confidence level] years, and MAPE of 0.29 (29%) on the test data set.

conclusionThese findings highlight the usability of estimating the bone age from left-hand radiographs, helping radiologists to verify their own results considering the margin of error on the model. The performance of our proposed model could be improved with additional refining and validation.

Indexed as

Age Determination by SkeletonDeep LearningAdolescentChildChild, PreschoolFemaleHand BonesHumansInfantMaleNeural Networks, ComputerRetrospective StudiesSaudi ArabiaBone age assessmentConvolutional neural networkDeep learningLeft-hand radiographsPediatric medicine

Identifiers

PMID39090563
PMCPMC11295702

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