סמינר מחלקה של רחמיאל אורן - ניתוח אלמנטים סופיים אוטונומיים של עצם הירך עם גידולים גרורתיים

06 בדצמבר 2023, 14:00 - 15:00

אוניברסיטה

סמינר מחלקה של רחמיאל אורן - ניתוח אלמנטים סופיים אוטונומיים של עצם הירך עם גידולים גרורתיים

School of Mechanical Engineering Seminar
Wednesday December 06.12.2023 at 14:00

ZOOM SEMINAR

Autonomous Finite Element Analyses of Femurs with Metastatic Tumors

Oren Rachmil

M.Sc. research under the supervision of Prof. Zohar Yosibash

Computational Mechanics & Experimental Biomechanics Lab

School of Mechanical Engineering, Tel Aviv University, Israel

Metastatic femoral tumors may lead to pathological fractures during daily activities. A CT-based finite element (FE) analysis of a patient’s femurs was shown to assist orthopedic surgeons in making informed decisions about the risk of fracture and the need for a prophylactic fixation. Improving the accuracy of such analyses requires an automatic and accurate segmentation of the tumors and their automatic inclusion in the FE model.

We present herein a deep learning (DL) algorithm (nnU-Net) to automatically segment lytic tumors within the femur that are then integrated into Simfini, an autonomous finite element (AFE) framework that is used in clinical practice to assist orthopedic oncologists in determining the risk of pathological femoral fractures due to metastatic tumors. A comprehensive evaluation of the DL model’s segmentation performance was assessed against two experienced musculoskeletal radiologists from Tel-Aviv Sourasky Medical Center.

The segmented lytic lesions were then integrated into Simfini that autonomously generated finite element models. Rigorous analyses were conducted using three methodologies: the original approach without tumor segmentation, manual segmentation of lytic tumors, and the innovative automatic DL algorithm.

Results indicate that the DL algorithm may segment lytic femoral tumors in CT scans as accurately as experienced radiologists with similar DSC scores. Additionally, the incorporation of segmented tumors into an AFE model led to elevated principal strains, offering potential enhancements in predicting the risk of pathological femoral fractures. The clinical significance of tumor inclusion in autonomous FE algorithms should be further investigated in follow-on clinical research.

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https://tau-ac-il.zoom.us/j/86497933118

סמינר מחלקה של יקיר קנפו - שלד חיצוני אוטונומי אנרגטית המבוסס על אנרגיה ביומכנית.

06 בדצמבר 2023, 14:00 - 15:00

אוניברסיטה

סמינר מחלקה של יקיר קנפו - שלד חיצוני אוטונומי אנרגטית המבוסס על אנרגיה ביומכנית.

SCHOOL OF MECHANICAL ENGINEERING SEMINAR
Wednesday Dec 06.12.2023 at 14:00

Wolfson Building of Mechanical Engineering, Room 206

Toward an Exoskeleton with Full Energy Autonomous Based on Biomechanical Energy

Yakir Knafo

M.Sc. research under the supervision of Prof. Raziel Rimer

Ben Gurion University, Department of Mechanical Engineering

With the growing demand for real-world uses and solutions for exoskeletons, there has been a significant interest in the development of technologies for exoskeletons with full energy autonomy. In the field of wearable robotics, exoskeletons have emerged as a promising solution to enhance human performance and reduce physical fatigue. However, one major challenge for active exoskeletons is the need for a power source. This demand is typically met with batteries, which limit the operational time of the device. A novel solution to this challenge is a design that enables the generation of electricity during motions where the muscles work as brakes, with the energy stored and subsequently returned to assist when the muscles act as motors. This could lead to a fully autonomous exoskeleton. To achieve this goal, a knee exoskeleton design with a direct drive and a novel electronic board was designed and manufactured to capture the energy generated by the wearer’s movements and convert it into electrical energy. The harvested energy is stored in a power bank, and, later, the motion is used to power the exoskeleton motor. Further, the device has torque control and can change the assistive profile and magnitude as needed for different assistance scenarios.

Sit-to-stand (STS) motion was chosen as a test case for the exoskeleton device. It was found that, during rising (from sit to stand), the exoskeleton provided up to 7.6 Nm and harvested 9.4 J. During lowering (from stand to sit, (it provided up to 10 Nm and was able to return 6.8 J of the harvested energy. Therefore, the cycle efficiency of the exoskeleton system (return divided by harvesting) is 72.3%. The results show that this technology has the potential to revolutionize exoskeletons and reduce the need for external energy sources.

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https://tau-ac-il.zoom.us/j/86497933118