Clinical application of artificial intelligence in longitudinal image analysis of bone age among GHD patients

Zhang, Lina; Chen, Jia; Hou, Lele; Xu, Yingying; Liu, Zulin; Huang, Siqi; Ou, Hui; Meng, Zhe; Liang, Liyang

doi:10.3389/fped.2022.986500

Cited by 7 publications

(2 citation statements)

References 18 publications

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“…It is an important clinical indicator for the diagnosis and efficacy evaluation of DDH. At present, a number of studies have evaluated the application of artificial intelligence in the pediatric musculoskeletal disorders and include predicting scoliosis according to x -ray ( 12 ), predicting bone age according to hand and wrist x -ray ( 13 ), determining leglength discrepancy from radiographs ( 14 ), quantifying the degree of metopic craniosynostosis from skull CT scans ( 15 ), predicting the presence of discoid lateral menisci from radiographs ( 16 ).…”

Section: Discussionmentioning

confidence: 99%

Application of deep-learning–based artificial intelligence in acetabular index measurement

Ma²,

Sun³

et al. 2023

Front. Pediatr.

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ObjectiveTo construct an artificial intelligence system to measure acetabular index and evaluate its accuracy in clinical application.MethodsA total of 10,219 standard anteroposterior pelvic radiographs were collected retrospectively from April 2014 to December 2018 in our hospital. Of these, 9,219 radiographs were randomly selected to train and verify the system. The remaining 1,000 radiographs were used to compare the system's and the clinicians' measurement results. All plain pelvic films were labeled by an expert committee through PACS system based on a uniform standard to measure acetabular index. Subsequently, eight other clinicians independently measured the acetabular index from 200 randomly selected radiographs from the test radiographs. Bland–Altman test was used for consistency analysis between the system and clinician measurements.ResultsThe test set included 1,000 cases (2,000 hips). Compared with the expert committee measurement, the 95% limits of agreement (95% LOA) of the system was −4.02° to 3.45° (bias = −0.27°, P < 0.05). The acetabular index measured by the system within all age groups, including normal and abnormal groups, also showed good credibility according to the Bland–Altman principle. Comparison of the measurement evaluations by the system and eight clinicians vs. that of, the expert committee, the 95% LOA of the clinician with the smallest measurement error was −2.76° to 2.56° (bias = −0.10°, P = 0.126). The 95% LOA of the system was −0.93° to 2.86° (bias = −0.03°, P = 0.647). The 95% LOA of the clinician with the largest measurement error was −3.41° to 4.25° (bias = 0.42°, P < 0.05). The measurement error of the system was only greater than that of a senior clinician.ConclusionThe newly constructed artificial intelligence system could quickly and accurately measure the acetabular index of standard anteroposterior pelvic radiographs. There is good data consistency between the system in measuring standard anteroposterior pelvic radiographs. The accuracy of the system is closer to that of senior clinicians.

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Section: Discussionmentioning

confidence: 99%

Application of deep-learning–based artificial intelligence in acetabular index measurement

Ma²,

Sun³

et al. 2023

Front. Pediatr.

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“…In conclusion, AI in bone age assessment is a useful tool that can assist radiologists, reducing workload and inter- and intra-observer variability. AI-assisted interpretation of bone age can also improve accuracy among junior readers [ 64 ]. However, multi-center and multi-national clinical trials are warranted to overcome the limitations of currently available AI methods.…”

Section: Bone Age Estimationmentioning

confidence: 99%

AI applications in musculoskeletal imaging: a narrative review

Gitto,

Serpi,

Albano

et al. 2024

Eur Radiol Exp

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This narrative review focuses on clinical applications of artificial intelligence (AI) in musculoskeletal imaging. A range of musculoskeletal disorders are discussed using a clinical-based approach, including trauma, bone age estimation, osteoarthritis, bone and soft-tissue tumors, and orthopedic implant-related pathology. Several AI algorithms have been applied to fracture detection and classification, which are potentially helpful tools for radiologists and clinicians. In bone age assessment, AI methods have been applied to assist radiologists by automatizing workflow, thus reducing workload and inter-observer variability. AI may potentially aid radiologists in identifying and grading abnormal findings of osteoarthritis as well as predicting the onset or progression of this disease. Either alone or combined with radiomics, AI algorithms may potentially improve diagnosis and outcome prediction of bone and soft-tissue tumors. Finally, information regarding appropriate positioning of orthopedic implants and related complications may be obtained using AI algorithms. In conclusion, rather than replacing radiologists, the use of AI should instead help them to optimize workflow, augment diagnostic performance, and keep up with ever-increasing workload.Relevance statement This narrative review provides an overview of AI applications in musculoskeletal imaging. As the number of AI technologies continues to increase, it will be crucial for radiologists to play a role in their selection and application as well as to fully understand their potential value in clinical practice.Key points• AI may potentially assist musculoskeletal radiologists in several interpretative tasks.• AI applications to trauma, age estimation, osteoarthritis, tumors, and orthopedic implants are discussed.• AI should help radiologists to optimize workflow and augment diagnostic performance. Graphical Abstract

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