Artificial intelligence in bone age assessment: accuracy and efficiency of a novel fully automated algorithm compared to the Greulich-Pyle method

Booz, Christian; Yel, Ibrahim; Wichmann, Julian L.; Boettger, S; Kamali, Ahmed Al; Albrecht, Moritz H.; Martin, Simon S.; Lenga, Lukas; Huizinga, Nicole A.; D’Angelo, Tommaso; Cavallaro, Marco; Vogl, Thomas J.; Bodelle, Boris

doi:10.1186/s41747-019-0139-9

Cited by 49 publications

(48 citation statements)

References 24 publications

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“…It has been validated through comparisons of manual ratings in several studies. According to Booz et al [ 43 ], the correlation between BoneXpert-derived and reference bone ages ( r = 0.99) was significantly higher than that between the reader-calculated and reference bone ages ( r = 0.90; p < 0.001). Moreover, BoneXpert requires considerably less time for image interpretation than manual rating using the GP method, thereby improving the time efficiency in routine clinical practice [ 43 ].…”

Section: Ai-based Automated Bone Age Assessment: Is This the New Era Of Bone Age Assessment?mentioning

confidence: 99%

“…According to Booz et al [ 43 ], the correlation between BoneXpert-derived and reference bone ages ( r = 0.99) was significantly higher than that between the reader-calculated and reference bone ages ( r = 0.90; p < 0.001). Moreover, BoneXpert requires considerably less time for image interpretation than manual rating using the GP method, thereby improving the time efficiency in routine clinical practice [ 43 ]. However, BoneXpert showed limited efficacy when fewer than eight bones were included, as well as in cases of poor image quality and abnormal bone morphology [ 43 ].…”

Section: Ai-based Automated Bone Age Assessment: Is This the New Era Of Bone Age Assessment?mentioning

confidence: 99%

“…Moreover, BoneXpert requires considerably less time for image interpretation than manual rating using the GP method, thereby improving the time efficiency in routine clinical practice [ 43 ]. However, BoneXpert showed limited efficacy when fewer than eight bones were included, as well as in cases of poor image quality and abnormal bone morphology [ 43 ]. These limitations may be overcome through consistent version upgrades.…”

Section: Ai-based Automated Bone Age Assessment: Is This the New Era Of Bone Age Assessment?mentioning

confidence: 99%

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Automated Bone Age Assessment Using Artificial Intelligence: The Future of Bone Age Assessment

Lee

2021

Korean J Radiol

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Bone age assessments are a complicated and lengthy process, which are prone to inter- and intra-observer variabilities. Despite the great demand for fully automated systems, developing an accurate and robust bone age assessment solution has remained challenging. The rapidly evolving deep learning technology has shown promising results in automated bone age assessment. In this review article, we will provide information regarding the history of automated bone age assessments, discuss the current status, and present a literature review, as well as the future directions of artificial intelligence-based bone age assessments.

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Section: Ai-based Automated Bone Age Assessment: Is This the New Era Of Bone Age Assessment?mentioning

confidence: 99%

Section: Ai-based Automated Bone Age Assessment: Is This the New Era Of Bone Age Assessment?mentioning

confidence: 99%

Section: Ai-based Automated Bone Age Assessment: Is This the New Era Of Bone Age Assessment?mentioning

confidence: 99%

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Automated Bone Age Assessment Using Artificial Intelligence: The Future of Bone Age Assessment

Lee

2021

Korean J Radiol

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“…Further, the use of DICOM is likely to improve image acquisition and image review workflows. AI has been shown to substantially reduce the amount of time radiologists spend on image review without compromising diagnostic accuracy ( 13 – 15 ). The same potential exists in dermatology, and the greatest efficiency gains are likely to be achieved for dermatologists reviewing total skin imaging studies.…”

Section: Derived Objectsmentioning

confidence: 99%

The Role of DICOM in Artificial Intelligence for Skin Disease

et al. 2021

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There is optimism that artificial intelligence (AI) will result in positive clinical outcomes, which is driving research and investment in the use of AI for skin disease. At present, AI for skin disease is embedded in research and development and not practiced widely in clinical dermatology. Clinical dermatology is also undergoing a technological transformation in terms of the development and adoption of standards that optimizes the quality use of imaging. Digital Imaging and Communications in Medicine (DICOM) is the international standard for medical imaging. DICOM is a continually evolving standard. There is considerable effort being invested in developing dermatology-specific extensions to the DICOM standard. The ability to encode relevant metadata and afford interoperability with the digital health ecosystem (e.g., image repositories, electronic medical records) has driven the initial impetus in the adoption of DICOM for dermatology. DICOM has a dedicated working group whose role is to develop a mechanism to support AI workflows and encode AI artifacts. DICOM can improve AI workflows by encoding derived objects (e.g., secondary images, visual explainability maps, AI algorithm output) and the efficient curation of multi-institutional datasets for machine learning training, testing, and validation. This can be achieved using DICOM mechanisms such as standardized image formats and metadata, metadata-based image retrieval, and de-identification protocols. DICOM can address several important technological and workflow challenges for the implementation of AI. However, many other technological, ethical, regulatory, medicolegal, and workforce barriers will need to be addressed before DICOM and AI can be used effectively in dermatology.

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“…A large dataset allows for the network to learn better in generalizing the mapping between the X-ray images to the predicted age. Thus, several works [20,21] that are based on a small dataset will not be considered as the benchmark methods and, hence, all performance comparisons are benchmarked with the state-of-the-art deep learning networks. Data augmentation has also been applied to further improve the training process by using shearing, flipping, and contrast variation operations.…”

Section: Introductionmentioning

confidence: 99%

Automated Bone Age Assessment with Image Registration Using Hand X-ray Images

2020

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One of the methods for identifying growth disorder is by assessing the skeletal bone age. A child with a healthy growth rate will have approximately the same chronological and bone ages. It is important to detect any growth disorder as early as possible, so that mitigation treatment can be administered with less negative consequences. Recently, the most popular approach in assessing the discrepancy between bone and chronological ages is through the subjective protocol of Tanner–Whitehouse that assesses selected regions in the hand X-ray images. This approach relies heavily on the medical personnel experience, which produces a high intra-observer bias. Therefore, an automated bone age prediction system with image registration using hand X-ray images is proposed in order to complement the inexperienced doctors by providing the second opinion. The system relies on an optimized regression network using a novel residual separable convolution model. The regressor network requires an input image to be 299 × 299 pixels, which will be mapped to the predicted bone age through three modules of the Xception network. Moreover, the images will be pre-processed or registered first to a standardized and normalized pose using separable convolutional neural networks. Three steps image registration are performed by segmenting the hand regions, which will be rotated using angle calculated from four keypoints of interest, before positional alignment is applied to ensure the region of interest is located in the middle. The hand segmentation is based on DeepLab V3 plus architecture, while keypoints regressor for angle alignment is based on MobileNet V1 architecture, where both of them use separable convolution as the core operators. To avoid the pitfall of underfitting, synthetic data are generated while using various rotation angles, zooming factors, and shearing images in order to augment the training dataset. The experimental results show that the proposed method returns the lowest mean absolute error and mean squared error of 8.200 months and 121.902 months2, respectively. Hence, an error of less than one year is acceptable in predicting the bone age, which can serve as a good supplement tool for providing the second expert opinion. This work does not consider gender information, which is crucial in making a better prediction, as the male and female bone structures are naturally different.

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Artificial intelligence in bone age assessment: accuracy and efficiency of a novel fully automated algorithm compared to the Greulich-Pyle method

Cited by 49 publications

References 24 publications

Automated Bone Age Assessment Using Artificial Intelligence: The Future of Bone Age Assessment

Automated Bone Age Assessment Using Artificial Intelligence: The Future of Bone Age Assessment

The Role of DICOM in Artificial Intelligence for Skin Disease

Automated Bone Age Assessment with Image Registration Using Hand X-ray Images

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