The morphology of the finger bones in hand-wrist radiographs (HWRs) can be considered as a radiological skeletal maturity indicator, along with the other indicators. This study aims to validate the anatomical landmarks envisaged to be used for classification of the morphology of the phalanges, by developing classical neural network (NN) classifiers based on a sub-dataset of 136 HWRs. A web-based tool was developed and 22 anatomical landmarks were labeled on four region of interests (proximal (PP3), medial (MP3), distal (DP3) phalanges of the third and medial phalanx (MP5) of the fifth finger) and the epiphysis-diaphysis relationships were saved as “narrow,”“equal,”“capping” or “fusion” by three observers. In each region, 18 ratios and 15 angles were extracted using anatomical points. The data set is analyzed by developing two NN classifiers, without (NN-1) and with (NN-2) the 5-fold cross-validation. The performance of the models was evaluated with percentage of agreement, Cohen’s (cκ) and Weighted (wκ) Kappa coefficients, precision, recall, F1-score and accuracy (statistically significance: p < 0.05). Method error was found to be in the range of cκ: 0.7–1. Overall classification performance of the models was changed between 82.14% and 89.29%. On average, performance of the NN-1 and NN-2 models were found to be 85.71% and 85.52%, respectively. The cκ and wκ of the NN-1 model were changed between −0.08 (p > 0.05) and 0.91 among regions. The average performance was found to be promising except the regions without adequate samples and the anatomical points are validated to be used in the future studies, initially.
FishmanLS. Radiographic evaluation of skeletal maturation. A clinically oriented method based on hand-wrist films. Angle Orthod1982; 52: 88–112.
2.
Flores-MirCBurgessCAChampneyM, et al. Correlation of skeletal maturation stages determined by cervical vertebrae and hand-wrist evaluations. Angle Orthod2006; 76(1): 1–5.
3.
VermaDPeltomäkiTJägerA. Reliability of growth prediction with hand-wrist radiographs. Eur J Orthod2009; 31: 438–442.
4.
PinchiVDe LucaFRicciardiF, et al. Skeletal age estimation for forensic purposes: A comparison of GP, TW2 and TW3 methods on an Italian sample. Forensic Sci Int2014; 238: 83–90.
5.
GreulichWWPyleSI. Radiographic atlas of skeletal development of hand and wrist. 2nd ed. Stanford: Stanford University Press, 1959.
6.
Flores-MirCNebbeBMajorPW. Use of skeletal maturation based on hand-wrist radiographic analysis as a predictor of facial growth: a systematic review. Angle Orthod2004; 74: 118–124.
7.
JonesLDGolanDHannaSA, et al. Artificial intelligence, machine learning and the evolution of healthcare: A bright future or cause for concern?Bone Jt Res2018; 7: 223–225.
8.
ChartrandGChengPMVorontsovE, et al. Deep Learning: A Primer for Radiologists. Radiographics2017; 37: 2113–2131.
9.
HeoMSKimJEHwangJJ, et al. Artificial intelligence in oral and maxillofacial radiology: what is currently possible?Dentomaxilloc Radiol2021; 50: 20200375.
MahadevkarSVKhemaniBPatilS, et al. A review on machine learning styles in computer vision—techniques and future directions. IEEE Access2022; 10: 107293–107329.
13.
JohariMEsmaeiliFAndalibA, et al. Detection of vertical root fractures in intact and endodontically treated premolar teeth by designing a probabilistic neural network: an ex vivo study. Dentomaxillofac Radiol2017; 46: 20160107.
14.
YadavAKChandelSS. Solar radiation prediction using artificial neural network techniques: a review. Renew Sustain Energ Rev2014; 33: 772–781.
15.
MaoKChenLWangM, et al. Classification of hand-wrist maturity level based on similarity matching. IET Image Process2021; 15: 2866–2879.
16.
RawatWWangZ. Deep convolutional neural networks for image classification: A comprehensive review. Neural Comput2017; 29: 2352–2449.
17.
TannerJMGibbonsRD. Automatic bone age measurement using computerized image analysis. J Pediatr Endocrinol Metab1994; 7: 141–146.
18.
HalabiSSPrevedelloLMKalpathy-CramerJ, et al. The RSNA pediatric bone age machine learning challenge. Radiology2019; 290: 498–503.
19.
PanIThodbergHHHalabiSS, et al. Improving automated pediatric bone age estimation using ensembles of models from the 2017 RSNA machine learning challenge. Radiol Artif Intell2019; 1: e190053.
20.
LarsonDBChenMCLungrenMP, et al. Performance of a deep-learning neural network model in assessing skeletal maturity on pediatric hand radiographs. Radiology2018; 287: 313–322.
21.
LeeHTajmirSLeeJ, et al. Fully automated deep learning system for bone age assessment. J Digit Imaging2017; 30: 427–441.
22.
WangFGuXChenS, et al. Artificial intelligence system can achieve comparable results to experts for bone age assessment of Chinese children with abnormal growth and development. PeerJ2020; 8: e8854.
23.
ShinNY. Comparison among bone age assessment methods and development of a Fishman-based skeletal maturity determination system using deep learning in contemporary Korean children and adolescents. PhD Thesis, Seoul National University, South Korea, 2020.
24.
KokHIzgiMSAcilarAM. Evaluation of the artificial neural network and Naive Bayes models trained with vertebra ratios for growth and development determination. Turk J Orthod2021; 34: 2–9.
25.
AmasyaHYildirimDAydoganT, et al. Cervical vertebral maturation assessment on lateral cephalometric radiographs using artificial intelligence: comparison of machine learning classifier models. Dentomaxillofac Radiol2020; 49: 20190441.
26.
SantiagoRCCunhaARJúniorGC, et al. New software for cervical vertebral geometry assessment and its relationship to skeletal maturation–a pilot study. Dentomaxillofac Radiol2014; 43: 20130238.
27.
BaptistaRSQuaglioCLMouradLM, et al. A semi-automated method for bone age assessment using cervical vertebral maturation. Angle Orthod2012; 82: 658–662.
28.
MakaremiMLacauleCMohammad-DjafariA. Deep learning and artificial intelligence for the determination of the cervical vertebra maturation degree from lateral radiography. Entropy2019; 21: 1222.
29.
RieseFMKellerS. Supervised, semi-supervised, and unsupervised learning for hyperspectral regression. In: PrasadSChanussotJ (eds) Hyperspectral image analysis. Advances in computer vision and pattern recognition. Cham: Springer, 2020 , pp. 217–218.
30.
GaoYMosalamKM. Deep transfer learning for Image-based structural damage recognition. Comput Aided Civ Infrastruct Eng2018; 33: 748–768.
31.
PietkaEGertychAPospiechS, et al. Computer-assisted bone age assessment: image preprocessing and epiphyseal/metaphyseal ROI extraction. IEEE Trans Med Imaging2001; 20: 715–729.
32.
HumCY, et al. Adaptive crossed reconstructed (acr) k-mean clustering segmentation for computer-aided bone age assessment system. Int J Math Models Meth Appl Sci2011; 5: 628–635.
33.
CunhaACCevidanesLHSant’AnnaEF, et al. Staging hand-wrist and cervical vertebrae images: a comparison of reproducibility. Dentomaxillofac Radiol2018; 47: 20170301.
34.
GandiniPManciniMAndreaniF. A comparison of hand-wrist bone and cervical vertebral analyses in measuring skeletal maturation. Angle Orthod2006; 76: 984–989.
35.
AgnieszkaSWojtaszek-SłomińskaARacka-PilszakB. Is the cervical vertebral maturation (CVM) method effective enough to replace the hand-wrist maturation (HWM) method in determining skeletal maturation?-A systematic review. Eur J Radiol2018; 102: 125–128.
36.
CericatoGOBittencourtMAParanhosLR. Validity of the assessment method of skeletal maturation by cervical vertebrae: a systematic review and meta-analysis. Dentomaxillofac Radiol2015; 44: 20140270.
37.
GroellRLindbichlerFRieplT, et al. The reliability of bone age determination in central European children using the Greulich and Pyle method. Br J Radiol1999; 72: 461–464.
38.
SatohM. Bone age: assessment methods and clinical applications. Clin Pediatr Endocrinol2015; 24: 143–152.
39.
WangFCidanWGuX, et al. Performance of an artificial intelligence system for bone age assessment in Tibet. Br J Radiol2021; 94: 20201119.
40.
YasaYÇelikÖBayrakdarIS, et al. An artificial intelligence proposal to automatic teeth detection and numbering in dental bite-wing radiographs. Acta Odontol Scand2021; 79: 275–281.
41.
KılıcMCBayrakdarISÇelikÖ, et al. Artificial intelligence system for automatic deciduous tooth detection and numbering in panoramic radiographs. Dentomaxillofac Radiol2021; 50: 20200172.
42.
SinÇAkkayaNAksoyS, et al. A deep learning algorithm proposal to automatic pharyngeal airway detection and segmentation on CBCT images. Orthod Craniofac Res2021; 24Suppl 2: 117–123.
43.
TajmirSHLeeHShailamR, et al. Artificial intelligence-assisted interpretation of bone age radiographs improves accuracy and decreases variability. Skeletal Radiol2019; 48: 275–283.
