Angular velocity analysis boosted by machine learning for helping in the differential diagnosis of Parkinson’s Disease and Essential Tremor

Recent research has shown that smartphones/smartwatches have a high potential to help physicians to identify and differentiate between different movement disorders. This work aims to develop Machine Learning models to improve the differential diagnosis between patients with Parkinson’s Disease and Essential Tremor. For this purpose, we use a mobile phone’s built-in gyroscope to record the angular velocity signals of two different arm positions during the patient’s follow-up, more precisely, in rest and posture positions. To develop and to find the best classification models, diverse factors were considered, such as the frequency range, the training and testing divisions, the kinematic features, and the classification method. We performed a two-stage kinematic analysis, first to differentiate between healthy and trembling subjects and then between patients with Parkinson’s Disease and Essential Tremor. The models developed reached an average accuracy of 97.2+/-3.7% (98.5% Sensitivity, 93.3% Specificity) to differentiate between Healthy and Trembling subjects and an average accuracy of 77.8+/-9.9% (75.7% Sensitivity, 80.0% Specificity) to discriminate between Parkinson’s Disease and Essential Tremor patients. Therefore, we conclude, that the angular velocity signal can be used to develop Machine Learning models for the differential diagnosis of Parkinson’s disease and Essential Tremor.Peer ReviewedPostprint (published version

Characterization of the volume and thickness of DIEP flap by CTA image processing

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksBreast cancer is the most commonly diagnosed cancer worldwide and is the leading cause of cancer-related death in more than 100 countries. Breast cancer surgery, especially when it involves a mastectomy, is associated with unaesthetic results that can be traumatic. Therefore, breast reconstruction is crucial for the patient to return to normal life, avoiding the psychological consequences. Based on free tissue transfer with microsurgery, autologous breast reconstruction is the gold standard for breast reconstruction, especially in irradiated patients. To plan the reconstruction surgery and locate the cutaneous perforating vessels supplying blood to the flap, preoperative Computed Tomography Angiography (CTA) is usually performed. However, only approximate and qualitative measurements are obtained and the location of the umbilical perforators reported by the radiologist. This paper advances a quantitative method to assess autologous Deep Inferior Epigastric Perforator (DIEP) flap volume and thickness from CTA images. This method is validated by measuring flap volume intraoperatively in the operating room of the Hospital Universitari de Bellvitge. These measurements could improve preoperative planning by reconstructive surgeons as they would know beforehand whether the amount of adipose tissue that can be harvested is sufficient to reconstruct the breast completely. This information could be crucial in thin and large breasted women or if bilateral breast reconstruction is planned.Peer ReviewedPostprint (author's final draft

Experimental Data-Driven Insertion Force Analyses of Hypodermic Needles in a Soft Tissue with an In-House Test Bench

Hypodermic needles are used in a wide range of medical procedures. In these procedures, an insertion force is applied to the needle to insert it through the soft tissue. In that way, when the tip of the needle encounters the surface of the tissue, several opposing forces are generated, such as stiffness, friction, and cutting forces, because of the tissue surface tension and viscoelastic properties. In addition, insertion force can influence tissue tearing and needle deflection. Thus, it is crucial to understand and analyze these forces and their effect on the tissue. We experimentally tested several commercial hypodermic needles at different diameters and bevel angles of the needle tips using an in-house test bench at different insertion velocities. In addition, chicken breasts were used as biological tissue to test the needles. As a result, the experimental cutting force increases linearly concerning the diameter of the needle and the insertion velocity. Also, the shear stress remains constant concerning insertion velocity and increases linearly with needle diameter and length. In conclusion, the results let us understand that the insertion mechanism is not trivial due to the influence of the geometry and relative velocity of the needle while penetrating the tissue and the mechanical properties of the soft tissue. All these combined parameters present challenging research to optimize medical procedures with needles.Peer ReviewedPostprint (published version