In this section, you can access to the latest technical information related to the FUTURE project topic.

Deep learning-based classification of adequate sonographic images for self-diagnosing deep vein thrombosis

AbstractBackground

Pulmonary thromboembolism is a serious disease that often occurs in disaster victims evacuated to shelters. Deep vein thrombosis is the most common reason for pulmonary thromboembolism, and early prevention is important. Medical technicians often perform ultrasonography as part of mobile medical screenings of disaster victims but reaching all isolated and scattered shelters is difficult. Therefore, deep vein thrombosis medical screening methods that can be easily performed by anyone are needed. The purpose of this study was to develop a method to automatically identify cross-sectional images suitable for deep vein thrombosis diagnosis so disaster victims can self-assess their risk of deep vein thrombosis.

» Author: Yusuke Nakayama,  Mitsuru Sato,  Masashi Okamoto,  Yohan Kondo,  Manami Tamura,  Yasuko Minagawa,  Mieko Uchiyama,  Yosuke Horii

» Reference: https://doi.org/10.1371/journal.pone.0282747

» Publication Date: 06/03/2023

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