Dependency of lower limb joint reaction forces on femoral version

Background Musculoskeletal (MSK) models based on literature data are meant to represent a generic anatomy and are a popular tool employed by biomechanists to estimate the internal loads occurring in the lower limb joints, such as joint reaction forces (JRFs). However, since these models are normally just linearly scaled to an individual’s anthropometry, it is unclear how their estimations would be affected by the personalization of key features of the MSK anatomy, one of which is the femoral version angle. Research Question How are the lower limb JRF magnitudes computed through a generic MSK model affected by changes in the femoral version? Methods We developed a bone-deformation tool in MATLAB (shared at https://simtk.org/projects/bone_deformity) and used it to create a set of seven OpenSim models spanning from 2˚ femoral retroversion to 40˚ anteversion. We used these models to simulate the gait of an elderly individual with an instrumented prosthesis implanted at their knee joint (5th Grand Challenge dataset) and quantified both the changes in JRFs magnitude due to varying the skeletal anatomy and their accuracy against the correspondent in vivo measurements at the knee joint. Results Hip and knee JRF magnitudes were affected by the femoral version with variations from the unmodified generic model up to 17.9 ± 4.5% at the hip and 43.4 ± 27.1% at the knee joint. The ankle joint was unaffected by the femoral geometry. The MSK models providing the most accurate knee JRFs (root mean squared error: 0.370 ± 0.068 body weight, coefficient of determination: 0.757 ± 0.104, peak error range: 0.09−0.42 body weight) were those with femoral anteversion angle closer to that measured on the segmented bone of the individual. Significance Femoral version substantially affects hip and knee JRFs estimated with generic MSK models, suggesting that personalizing key MSK anatomical features might be necessary for accurate estimation of JRFs with these models

Sentinel lymph node biopsy in squamous cell carcinoma of the head and neck: 10 years of experience

Sentinel node (SN) biopsy of head and neck cancer is still considered investigational, and agreement on the width of the surgical sampling has not yet been reached. From May 1999 to Dec 2009, 209 consecutive patients entered a prospective study: 61.7% had primary tumour of the oral cavity and 23.9% of the oropharynx. SN was not found in 26 patients. Based on these data and definitive histopathological analysis, we proposed six hypothetic scenarios to understand the percentage of neck recurrences following different treatments Among patients with identified SN, 54 cases were pN+: 47 in SN and 7 in a different node. Considering the six hypothetic scenarios: "only SN removal", "SN level dissection", "neck dissection from the tumour site to SN level", "selective neck dissection of three levels (SND)", "dissection from level I to IV" and "comprehensive I-V dissection", neck recurrences could be expected in 6.5%, 3.8%, 2.18%, 2.73%, 1.09% and 1.09% of cases, respectively. SN biopsy can be considered a useful tool to personalize the surgical approach to a N0 carcinoma. The minimum treatment of the neck is probably dissection of the levels between the primary tumour and the level containing the SN(s). Outside the framework of a clinical study, the best treatment can still be considered SND

AI-Driven Resource Allocation in Optical Wireless Communication Systems

Visible light communication (VLC) is a promising solution to satisfy the extreme demands of emerging applications. VLC offers bandwidth that is orders of magnitude higher than what is offered by the radio spectrum, hence making best use of the resources is not a trivial matter. There is a growing interest to make next generation communication networks intelligent using AI based tools to automate the resource management and adapt to variations in the network automatically as opposed to conventional handcrafted schemes based on mathematical models assuming prior knowledge of the network. In this article, a reinforcement learning (RL) scheme is developed to intelligently allocate resources of an optical wireless communication (OWC) system in a HetNet environment. The main goal is to maximise the total reward of the system which is the sum rate of all users. The results of the RL scheme are compared with that of an optimization scheme that is based on Mixed Integer Linear Programming (MILP) model.Comment: 6 pages, 2 Figures, Conferenc