Temat: diagnoza złamania - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Femoral neck stress fracture during sport climbing
Autorzy:: Küpper, Thomas
Rutten, Steven
Morrison, Audry
Schöffl, Volker
Powiązania:: https://bibliotekanauki.pl/articles/2098278.pdf
Data publikacji:: 2021-11-29
Wydawca:: Państwowa Wyższa Szkoła Zawodowa w Tarnowie
Tematy:: wspinaczka sportowa
urazy
diagnoza
rehabilitacja
fizjoterapia
złamania
sport climbing
injury
diagnosis
rehabilitation
physiotherapy
occult fracture
Opis:: While the epiphyseal stress fracture of the finger’s middle phalanx is a known sport-specific injury occurring only in adolescent climbers, and in other locations it’s rare, no femoral neck stress fracture (FNSF) in sports climbing has yet been reported. An experienced female sport climber (37y, 160 cm, 45 kg, BMI 17.5) suffered from pain in the left inguinal region while climbing, and later, also required a stick to walk. Routine radiography missed the FNSF and it was many weeks before a MRI accurately provided that diagnosis. The time between the X-ray and MRI should have been minimized as it resulted in a delayed diagnosis, unnecessary pain and delayed healing. In this situation the initial clinical investigation, the patient’s history and the X-ray did not lead to a clear diagnosis, and the initial treatment was ineffective. Further investigation by MRI and / or CT scans should have taken place sooner and would have been essential.
Źródło:: Health Promotion & Physical Activity; 2021, 17, 4; 1-5
2544-9117
Pojawia się w:: Health Promotion & Physical Activity
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: A supervised approach to musculoskeletal imaging fracture detection and classification using deep learning algorithms
Autorzy:: Karanam, Santoshachandra Rao
Srinivas, Y.
Chakravarty, S.
Powiązania:: https://bibliotekanauki.pl/articles/38702595.pdf
Data publikacji:: 2023
Wydawca:: Instytut Podstawowych Problemów Techniki PAN
Tematy:: musculoskeletal image
image processing
image enhancement
fracture diagnosis
fracture classification
deep neural network
obraz układu mięśniowo-szkieletowego
przetwarzanie obrazu
wzmocnienie obrazu
diagnoza złamania
klasyfikacja złamań
głęboka sieć neuronowa
Opis:: Bone fractures break bone continuity. Impact or stress causes numerous bone fractures. Fracture misdiagnosis is the most frequent mistake in emergency rooms, resulting in treatment delays and permanent impairment. According to the Indian population studies, fractures are becoming more common. In the last three decades, there has been a growth of 480 000, and by 2022, it will surpass 600 000. Classifying X-rays may be challenging, particularly in an emergency room when one must act quickly. Deep learning techniques have recently become more popular for image categorization. Deep neural networks (DNNs) can classify images and solve challenging problems. This research aims to build and evaluate a deep learning system for fracture identification and bone fracture classification (BFC). This work proposes an image-processing system that can identify bone fractures using X-rays. Images from the dataset are pre-processed, enhanced, and extracted. Then, DNN classifiers ResNeXt101, InceptionResNetV2, Xception, and NASNetLarge separate the images into the ones with unfractured and fractured bones (normal, oblique, spiral, comminuted, impacted, transverse, and greenstick). The most accurate model is InceptionResNetV2, with an accuracy of 94.58%.
Źródło:: Computer Assisted Methods in Engineering and Science; 2023, 30, 3; 369-385
2299-3649
Pojawia się w:: Computer Assisted Methods in Engineering and Science
Dostawca treści:: Biblioteka Nauki

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