Variations of the soft tissue thicknesses external to the ribs in Pectus Excavatum patients

Rodrigues, Pedro Luiz Coelho; Direito-Santos, Bruno; Moreira, António H. J.; Fonseca, Jaime C.; Pinho, António C. M.; Rodrigues, Nélson; Henriques‐Coelho, Tiago; Correia‐Pinto, Jorge; Vilaça, João L.

doi:10.1016/j.jpedsurg.2013.01.047

Cited by 5 publications

(1 citation statement)

References 22 publications

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“…The necessity of modeling and bending the thoracic prosthesis using ribs position and dimension is of outmost importance, in particular when taking into account the asymmetry of each patient, instead of using a template that only copies the chest wall curvature [25].…”

Section: Discussionmentioning

confidence: 99%

Automatic modeling of pectus excavatum corrective prosthesis using artificial neural networks

Rodrigues¹,

Rodrigues²,

Pinho³

et al. 2014

Medical Engineering & Physics

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a b s t r a c tPectus excavatum is the most common deformity of the thorax. Pre-operative diagnosis usually includes Computed Tomography (CT) to successfully employ a thoracic prosthesis for anterior chest wall remodeling. Aiming at the elimination of radiation exposure, this paper presents a novel methodology for the replacement of CT by a 3D laser scanner (radiation-free) for prosthesis modeling.The complete elimination of CT is based on an accurate determination of ribs position and prosthesis placement region through skin surface points. The developed solution resorts to a normalized and combined outcome of an artificial neural network (ANN) set. Each ANN model was trained with data vectors from 165 male patients and using soft tissue thicknesses (STT) comprising information from the skin and rib cage (automatically determined by image processing algorithms). Tests revealed that ribs position for prosthesis placement and modeling can be estimated with an average error of 5.0 ± 3.6 mm. One also showed that the ANN performance can be improved by introducing a manually determined initial STT value in the ANN normalization procedure (average error of 2.82 ± 0.76 mm). Such error range is well below current prosthesis manual modeling (approximately 11 mm), which can provide a valuable and radiation-free procedure for prosthesis personalization.

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Section: Discussionmentioning

confidence: 99%