Bone transport using the Ilizarov method: a review of complications in 100 consecutive cases

We present the results of treatment of 100 patients (72 men, 28 women) by the Ilizarov method of bone transport using circular (55) and monolateral external fixators (45). A total of 26 femurs (18 monolateral, 8 circular) and 74 tibias (49 circular, 25 monolateral) was examined. There were no significant differences between the circular fixator and the monolateral fixator with regard to treatment time, complications in the treated bone segments or compliance with the presence of the fixator. The main complications (pseudoarthrosis at bone contact points after transport, insufficient ossification of lengthened bone, knee stiffness) were resolved with further treatment for all patients with the exception of one case which continued with repeated infections. The circular fixator allows for deformity corrections during bone transport but the monolateral fixator is tolerated better by patients, especially in those with femoral defects

Camera- and Viewpoint-Agnostic Evaluation of Axial Postural Abnormalities in People with Parkinson’s Disease through Augmented Human Pose Estimation

Axial postural abnormalities (aPA) are common features of Parkinson’s disease (PD) and manifest in over 20% of patients during the course of the disease. aPA form a spectrum of functional trunk misalignment, ranging from a typical Parkinsonian stooped posture to progressively greater degrees of spine deviation. Current research has not yet led to a sufficient understanding of pathophysiology and management of aPA in PD, partially due to lack of agreement on validated, user-friendly, automatic tools for measuring and analysing the differences in the degree of aPA, according to patients’ therapeutic conditions and tasks. In this context, human pose estimation (HPE) software based on deep learning could be a valid support as it automatically extrapolates spatial coordinates of the human skeleton keypoints from images or videos. Nevertheless, standard HPE platforms have two limitations that prevent their adoption in such a clinical practice. First, standard HPE keypoints are inconsistent with the keypoints needed to assess aPA (degrees and fulcrum). Second, aPA assessment either requires advanced RGB-D sensors or, when based on the processing of RGB images, they are most likely sensitive to the adopted camera and to the scene (e.g., sensor–subject distance, lighting, background–subject clothing contrast). This article presents a software that augments the human skeleton extrapolated by state-of-the-art HPE software from RGB pictures with exact bone points for posture evaluation through computer vision post-processing primitives. This article shows the software robustness and accuracy on the processing of 76 RGB images with different resolutions and sensor–subject distances from 55 PD patients with different degrees of anterior and lateral trunk flexion