Computerized Bone Age Estimation Using Deep Learning Based Program : Evaluation of the Accuracy and Efficiency
OBJECTIVE: The purpose of this study is to evaluate the accuracy and efficiency of a new automatic software system for bone age assessment and to validate its feasibility in clinical practice.
MATERIALS AND METHODS: A Greulich-Pyle method-based deep-learning technique was used to develop the automatic software system for bone age determination. Using this software, bone age was estimated from left-hand radiographs of 200 patients (3-17 years old) using first-rank bone age (software only), computer-assisted bone age (two radiologists with software assistance), and Greulich-Pyle atlas-assisted bone age (two radiologists with Greulich-Pyle atlas assistance only). The reference bone age was determined by the consensus of two experienced radiologists.
RESULTS: First-rank bone ages determined by the automatic software system showed a 69.5% concordance rate and significant correlations with the reference bone age (r = 0.992; p < 0.001). Concordance rates increased with the use of the automatic software system for both reviewer 1 (63.0% for Greulich-Pyle atlas-assisted bone age vs 72.5% for computer-assisted bone age) and reviewer 2 (49.5% for Greulich-Pyle atlas-assisted bone age vs 57.5% for computer-assisted bone age). Reading times were reduced by 18.0% and 40.0% for reviewers 1 and 2, respectively.
CONCLUSION: Automatic software system showed reliably accurate bone age estimations and appeared to enhance efficiency by reducing reading times without compromising the diagnostic accuracy.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2017 |
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| Erschienen: |
2017 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:209 |
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| Enthalten in: |
AJR. American journal of roentgenology - 209(2017), 6 vom: 17. Dez., Seite 1374-1380 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Kim, Jeong Rye [VerfasserIn] |
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| Links: |
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| Themen: |
Bone age |
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| Anmerkungen: |
Date Completed 05.12.2017 Date Revised 10.12.2019 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.2214/AJR.17.18224 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM275690911 |
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| 500 | |a Date Completed 05.12.2017 | ||
| 500 | |a Date Revised 10.12.2019 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a OBJECTIVE: The purpose of this study is to evaluate the accuracy and efficiency of a new automatic software system for bone age assessment and to validate its feasibility in clinical practice | ||
| 520 | |a MATERIALS AND METHODS: A Greulich-Pyle method-based deep-learning technique was used to develop the automatic software system for bone age determination. Using this software, bone age was estimated from left-hand radiographs of 200 patients (3-17 years old) using first-rank bone age (software only), computer-assisted bone age (two radiologists with software assistance), and Greulich-Pyle atlas-assisted bone age (two radiologists with Greulich-Pyle atlas assistance only). The reference bone age was determined by the consensus of two experienced radiologists | ||
| 520 | |a RESULTS: First-rank bone ages determined by the automatic software system showed a 69.5% concordance rate and significant correlations with the reference bone age (r = 0.992; p < 0.001). Concordance rates increased with the use of the automatic software system for both reviewer 1 (63.0% for Greulich-Pyle atlas-assisted bone age vs 72.5% for computer-assisted bone age) and reviewer 2 (49.5% for Greulich-Pyle atlas-assisted bone age vs 57.5% for computer-assisted bone age). Reading times were reduced by 18.0% and 40.0% for reviewers 1 and 2, respectively | ||
| 520 | |a CONCLUSION: Automatic software system showed reliably accurate bone age estimations and appeared to enhance efficiency by reducing reading times without compromising the diagnostic accuracy | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a bone age | |
| 650 | 4 | |a children | |
| 650 | 4 | |a deep learning | |
| 650 | 4 | |a neural network model | |
| 700 | 1 | |a Shim, Woo Hyun |e verfasserin |4 aut | |
| 700 | 1 | |a Yoon, Hee Mang |e verfasserin |4 aut | |
| 700 | 1 | |a Hong, Sang Hyup |e verfasserin |4 aut | |
| 700 | 1 | |a Lee, Jin Seong |e verfasserin |4 aut | |
| 700 | 1 | |a Cho, Young Ah |e verfasserin |4 aut | |
| 700 | 1 | |a Kim, Sangki |e verfasserin |4 aut | |
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