Three-dimensional data has a wide range of applications in medicine. For the particular case of cranial deformation in infants, it is becoming a common tool for evaluation. However, there is a need for low-cost solutions that provide accurate information even with uncoll aborative infants with ultrafast movement reactions. As cranial deformation is often linked to facial abnormalities, facial information is required for comprehensive evaluation. In this study, the integration of target-based close-range photogrammetry and facial landmark machine learning detection is carried out. The resulting tool is automatic and smartphone-based and provides 3D information of the head and face. This methodology opens a new path for the effective integration of machine learning and photogrammetry in medicine and, in particular, for overall head analysis.

Combining machine learning and close-range photogrammetry for infant’s head 3D measurement: a smartphone-based solution / Barbero-García, I; Pierdicca, R.; Paolanti, M.; Felicetti, A.; Lerma, J. L.. - In: MEASUREMENT. - ISSN 0263-2241. - 182:(2021). [10.1016/j.measurement.2021.109686]

Combining machine learning and close-range photogrammetry for infant’s head 3D measurement: a smartphone-based solution

Pierdicca R.;Felicetti A.;
2021-01-01

Abstract

Three-dimensional data has a wide range of applications in medicine. For the particular case of cranial deformation in infants, it is becoming a common tool for evaluation. However, there is a need for low-cost solutions that provide accurate information even with uncoll aborative infants with ultrafast movement reactions. As cranial deformation is often linked to facial abnormalities, facial information is required for comprehensive evaluation. In this study, the integration of target-based close-range photogrammetry and facial landmark machine learning detection is carried out. The resulting tool is automatic and smartphone-based and provides 3D information of the head and face. This methodology opens a new path for the effective integration of machine learning and photogrammetry in medicine and, in particular, for overall head analysis.
2021
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11566/291739
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