Servi Michaela, Furferi Rocco, Santerelli Chiara, Uccheddu Francesca, Volpe Yary, Ghionzoli Marco, Messineo Antonio
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:5388-5393. doi: 10.1109/EMBC44109.2020.9176494.
Pectus Excavatum (PE) is a congenital anomaly of the ribcage, at the level of the sterno-costal plane, which consists of an inward angle of the sternum, in the direction of the spine. PE is the most common of all thoracic malformations, with an incidence of 1 in 300-400 people. To monitor the progress of the pathology, severity indices, or thoracic indices, have been used over the years. Among these indices, recent studies focus on the calculation of optical measures, calculated on the optical scan of the patient's chest, which can be very accurate without exposing the patient to invasive treatments such as CT scans. In this work, data from a sample of PE patients and corresponding doctors' severity assessments have been collected and used to create a decision tool to automatically assign a severity value to the patient. The idea is to provide the physician with an objective and easy to use measuring instrument that can be exploited in an outpatient clinic context. Among several classification tools, a Probabilistic Neural Network was chosen for this task for its simple structure and learning mode.
漏斗胸（PE）是一种发生在胸骨-肋骨平面的胸廓先天性畸形，其特征为胸骨向脊柱方向内陷成角。PE是所有胸廓畸形中最常见的，发病率为300-400人中的1人。多年来，为了监测病情进展，人们使用了严重程度指数或胸廓指数。在这些指数中，近期研究聚焦于基于患者胸部光学扫描计算的光学测量值，这种测量无需让患者接受如CT扫描等侵入性检查，且非常准确。在这项工作中，收集了一组漏斗胸患者的数据以及医生相应的严重程度评估结果，并用于创建一个决策工具，以自动为患者分配严重程度值。目的是为医生提供一种客观且易于使用的测量工具，可在门诊环境中使用。在多种分类工具中，由于其结构简单和学习模式，选择了概率神经网络来完成此任务。
