Bone age determination using only the index finger: a novel approach using a convolutional neural network compared with human radiologists.
Background: Recently developed convolutional neural network (CNN) models determine bone age more accurately than radiologists.Objective: The purpose of this study was to determine whether a CNN and radiologists can accurately predict bone age from radiographs using only the index finger rather than...
| Publicado en: | Pediatric Radiology Vol. 50; no. 4; pp. 516 - 524 |
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| Autores principales: | , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=142205014&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 142205014 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 03010449 O03 jtl: Pediatric Radiology issn: 03010449 maglogo: N pubinfo: dt: Apr2020 vid: 50 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 142205014 142205014 NLM31863193 142205014 10.1007/s00247-019-04587-y NLM31863193 142205014 ppf: 516 ppct: 8 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Bone age determination using only the index finger: a novel approach using a convolutional neural network compared with human radiologists. aug: au: Reddy, Nakul E. Rayan, Jesse C. Annapragada, Ananth V. Mahmood, Nadia F. Scheslinger, Alan E. Zhang, Wei Kan, J. Herman affil: Interventional Radiology, MD Anderson Cancer Center, 1515 Holcombe Blvd., Unit 1471, 77030, Houston, TX, USA sug: subj: Age Determination by Skeleton Fingers Infant Female Data Collection Image Processing, Computer Assisted Child, Preschool Adolescence Retrospective Design Child Male Human Infant: 1-23 months Child, Preschool: 2-5 years Adolescent: 13-18 years Child: 6-12 years Female Male ab: Background: Recently developed convolutional neural network (CNN) models determine bone age more accurately than radiologists.Objective: The purpose of this study was to determine whether a CNN and radiologists can accurately predict bone age from radiographs using only the index finger rather than the whole hand.Materials and Methods: We used a public anonymized dataset provided by the Radiological Society of North America (RSNA) pediatric bone age challenge. The dataset contains 12,611 hand radiographs for training and 200 radiographs for testing. The index finger was cropped from these images to create a second dataset. Separate CNN models were trained using the whole-hand radiographs and the cropped second-digit dataset using the consensus ground truth provided by the RSNA bone age challenge. Bone age determination using both models was compared with ground truth as provided by the RSNA dataset. Separately, three pediatric radiologists determined bone age from the whole-hand and index-finger radiographs, and the consensus was compared to the ground truth and CNN-model-determined bone ages.Results: The mean absolute difference between the ground truth and CNN bone age for whole-hand and index-finger was similar (4.7 months vs. 5.1 months, P=0.14), and both values were significantly smaller than that for radiologist bone age determination from the single-finger radiographs (8.0 months, P<0.0001).Conclusion: CNN-model-determined bone ages from index-finger radiographs are similar to whole-hand bone age interpreted by radiologists in the dataset, as well as a model trained on the whole-hand radiograph. In addition, the index-finger model performed better than the ground truth compared to subspecialty trained pediatric radiologists also using only the index finger to determine bone age. The radiologist interpreting bone age can use the second digit as a reliable starting point in their search pattern. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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