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Diagnostics
Volume 10
Issue 7
10.3390/diagnostics10070495
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Article

Two-Dimensional Image-Based Screening Tool for Infants with Positional Cranial Deformities: A Machine Learning Approach

by
Cecilia A. Callejas Pastor
1,†,
Il-Young Jung
2,†,
Shinhye Seo
1,
Soon Bin Kwon
3,
Yunseo Ku
1,* and
Jayoung Choi
2,*
1
Department of Biomedical Engineering, Chungnam National University College of Medicine, Daejeon 35015, Korea
2
Department of Rehabilitation Medicine, Chungnam National University College of Medicine, Daejeon 35015, Korea
3
Interdisciplinary Program in Bioengineering, Graduate School, Seoul National University, Seoul 03080, Korea
*
Authors to whom correspondence should be addressed.
†
These authors contribute equally to this work.
Diagnostics 2020, 10(7), 495; https://doi.org/10.3390/diagnostics10070495
Received: 9 June 2020 / Revised: 17 July 2020 / Accepted: 17 July 2020 / Published: 19 July 2020
(This article belongs to the Section Machine Learning and Artificial Intelligence in Diagnostics)
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Abstract

Positional cranial deformities are relatively common conditions, characterized by asymmetry and changes in skull shape. Although three-dimensional (3D) scanning is the gold standard for diagnosing such deformities, it requires expensive laser scanners and skilled maneuvering. We therefore developed an inexpensive, fast, and convenient screening method to classify cranial deformities in infants, based on single two-dimensional vertex cranial images. In total, 174 measurements from 80 subjects were recorded. Our screening software performs image processing and machine learning-based estimation related to the deformity indices of the cranial ratio (CR) and cranial vault asymmetry index (CVAI) to determine the severity levels of brachycephaly and plagiocephaly. For performance evaluations, the estimated CR and CVAI values were compared to the reference data obtained using a 3D cranial scanner. The CR and CVAI correlation coefficients obtained via support vector regression were 0.85 and 0.89, respectively. When the trained model was evaluated using the unseen test data for the three CR and three CVAI classes, an 86.7% classification accuracy of the proposed method was obtained for both brachycephaly and plagiocephaly. The results showed that our method for screening cranial deformities in infants could aid clinical evaluations and parental monitoring of the progression of deformities at home. View Full-Text
Keywords: plagiocephaly; brachycephaly; positional cranial deformities; cephalic ratio; cranial vault asymmetry index; machine learning plagiocephaly; brachycephaly; positional cranial deformities; cephalic ratio; cranial vault asymmetry index; machine learning
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MDPI and ACS Style

Callejas Pastor, C.A.; Jung, I.-Y.; Seo, S.; Kwon, S.B.; Ku, Y.; Choi, J. Two-Dimensional Image-Based Screening Tool for Infants with Positional Cranial Deformities: A Machine Learning Approach. Diagnostics 2020, 10, 495. https://doi.org/10.3390/diagnostics10070495

AMA Style

Callejas Pastor CA, Jung I-Y, Seo S, Kwon SB, Ku Y, Choi J. Two-Dimensional Image-Based Screening Tool for Infants with Positional Cranial Deformities: A Machine Learning Approach. Diagnostics. 2020; 10(7):495. https://doi.org/10.3390/diagnostics10070495

Chicago/Turabian Style

Callejas Pastor, Cecilia A., Il-Young Jung, Shinhye Seo, Soon B. Kwon, Yunseo Ku, and Jayoung Choi. 2020. "Two-Dimensional Image-Based Screening Tool for Infants with Positional Cranial Deformities: A Machine Learning Approach" Diagnostics 10, no. 7: 495. https://doi.org/10.3390/diagnostics10070495

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