Virtual simulation of the postsurgical cosmetic outcome in patients with pectus excavatum

Pectus excavatum is the most common congenital deformity of the anterior chest wall, in which several ribs and the sternum grow abnormally. Nowadays, the surgical correction is carried out in children and adults through Nuss technic. This technic has been shown to be safe with major drivers as cosmesis and the prevention of psychological problems and social stress. Nowadays, no application is known to predict the cosmetic outcome of the pectus excavatum surgical correction. Such tool could be used to help the surgeon and the patient in the moment of deciding the need for surgery correction. This work is a first step to predict postsurgical outcome in pectus excavatum surgery correction. Facing this goal, it was firstly determined a point cloud of the skin surface along the thoracic wall using Computed Tomography (before surgical correction) and the Polhemus FastSCAN (after the surgical correction). Then, a surface mesh was reconstructed from the two point clouds using a Radial Basis Function algorithm for further affine registration between the meshes. After registration, one studied the surgical correction influence area (SCIA) of the thoracic wall. This SCIA was used to train, test and validate artificial neural networks in order to predict the surgical outcome of pectus excavatum correction and to determine the degree of convergence of SCIA in different patients. Often, ANN did not converge to a satisfactory solution (each patient had its own deformity characteristics), thus invalidating the creation of a mathematical model capable of estimating, with satisfactory results, the postsurgical outcome.Fundação para a Ciência e a Tecnologia, Portugal (FCT) through the Postdoc grant referenced SFRH/BPD/46851/2008 and R&D project referenced PTDC/SAU-BEB/103368/2008

Inside the NIGM Grid Service: Implementation, Evaluation and Extension

Chinese and Western medicine s have a different understanding and approach to life, health, and illness -joining their complementary work and support them by an advanced information technology could result in an improved health system. The Non-Invasive Blood Glucose Measurement (NIGM) Service is a grid based implementation of a novel non-invasive method for measuring human blood glucose values exploiting Chinese meridian theory. In this paper, we describe the implementation of the NIGM service in detail, present an initial performance evaluation and discuss an extension towards other non-invasive long term diabetic relevant measurement. Additionally, the adaption of the ontology-based Medical records Annotation Tool (MedAT) framework towards usage in NIGM trails is elaborated. ? 2008 IEEE.EI

Fascicle localisation within peripheral nerves through evoked activity recordings: A comparison between electrical impedance tomography and multi-electrode arrays

BACKGROUND: The lack of understanding of fascicular organisation in peripheral nerves limits the potential of vagus nerve stimulation therapy. Two promising methods may be employed to identify the functional anatomy of fascicles within the nerve: fast neural electrical impedance tomography (EIT), and penetrating multi-electrode arrays (MEA). These could provide a means to image the compound action potential within fascicles in the nerve. NEW METHOD: We compared the ability to localise fascicle activity between silicon shanks (SS) and carbon fibre (CF) multi-electrode arrays and fast neural EIT, with micro-computed tomography (MicroCT) as an independent reference. Fast neural EIT in peripheral nerves was only recently developed and MEA technology has been used only sparingly in nerves and not for source localisation. Assessment was performed in rat sciatic nerves while evoking neural activity in the tibial and peroneal fascicles. RESULTS: Recorded compound action potentials were larger with CF compared to SS (∼700μV vs ∼300μV); however, background noise was greater (6.3μV vs 1.7μV) leading to lower SNR. Maximum spatial discrimination between Centres-of-Mass of fascicular activity was achieved by fast neural EIT (402±30μm) and CF MEA (414±123μm), with no statistical difference between MicroCT (625±17μm) and CF (p>0.05) and between CF and EIT (p>0.05). Compared to CF MEAs, SS MEAs had a lower discrimination power (103±51μm, p<0.05). COMPARISON WITH EXISTING METHODS: EIT and CF MEAs showed localisation power closest to MicroCT. Silicon MEAs adopted in this study failed to discriminate fascicle location. Re-design of probe geometry may improve results. CONCLUSIONS: Nerve EIT is an accurate tool for assessment of fascicular position within nerves. Accuracy of EIT and CF MEA is similar to the reference method. We give technical recommendations for performing multi-electrode recordings in nerves

Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table