$Critical evaluation of deep neural networks for wrist fracture detection$
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Critical evaluation of deep neural networks for wrist fracture detection

Abstract Wrist Fracture is the most common type of fracture with a high incidence rate. Conventional radiography (i.e. X-ray imaging) is used for wrist fracture detection routinely, but occasionally fracture delineation poses issues and an additional confirmation by computed tomography (CT) is neede...

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Autores principales:	Abu Mohammed Raisuddin, Elias Vaattovaara, Mika Nevalainen, Marko Nikki, Elina Järvenpää, Kaisa Makkonen, Pekka Pinola, Tuula Palsio, Arttu Niemensivu, Osmo Tervonen, Aleksei Tiulpin
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e86336d998a043168ba26f739ea5a6ac
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spelling	oai:doaj.org-article:e86336d998a043168ba26f739ea5a6ac2021-12-02T11:39:20ZCritical evaluation of deep neural networks for wrist fracture detection10.1038/s41598-021-85570-22045-2322https://doaj.org/article/e86336d998a043168ba26f739ea5a6ac2021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-85570-2https://doaj.org/toc/2045-2322Abstract Wrist Fracture is the most common type of fracture with a high incidence rate. Conventional radiography (i.e. X-ray imaging) is used for wrist fracture detection routinely, but occasionally fracture delineation poses issues and an additional confirmation by computed tomography (CT) is needed for diagnosis. Recent advances in the field of Deep Learning (DL), a subfield of Artificial Intelligence (AI), have shown that wrist fracture detection can be automated using Convolutional Neural Networks. However, previous studies did not pay close attention to the difficult cases which can only be confirmed via CT imaging. In this study, we have developed and analyzed a state-of-the-art DL-based pipeline for wrist (distal radius) fracture detection—DeepWrist, and evaluated it against one general population test set, and one challenging test set comprising only cases requiring confirmation by CT. Our results reveal that a typical state-of-the-art approach, such as DeepWrist, while having a near-perfect performance on the general independent test set, has a substantially lower performance on the challenging test set—average precision of 0.99 (0.99–0.99) versus 0.64 (0.46–0.83), respectively. Similarly, the area under the ROC curve was of 0.99 (0.98–0.99) versus 0.84 (0.72–0.93), respectively. Our findings highlight the importance of a meticulous analysis of DL-based models before clinical use, and unearth the need for more challenging settings for testing medical AI systems.Abu Mohammed RaisuddinElias VaattovaaraMika NevalainenMarko NikkiElina JärvenpääKaisa MakkonenPekka PinolaTuula PalsioArttu NiemensivuOsmo TervonenAleksei TiulpinNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Abu Mohammed Raisuddin Elias Vaattovaara Mika Nevalainen Marko Nikki Elina Järvenpää Kaisa Makkonen Pekka Pinola Tuula Palsio Arttu Niemensivu Osmo Tervonen Aleksei Tiulpin Critical evaluation of deep neural networks for wrist fracture detection
description	Abstract Wrist Fracture is the most common type of fracture with a high incidence rate. Conventional radiography (i.e. X-ray imaging) is used for wrist fracture detection routinely, but occasionally fracture delineation poses issues and an additional confirmation by computed tomography (CT) is needed for diagnosis. Recent advances in the field of Deep Learning (DL), a subfield of Artificial Intelligence (AI), have shown that wrist fracture detection can be automated using Convolutional Neural Networks. However, previous studies did not pay close attention to the difficult cases which can only be confirmed via CT imaging. In this study, we have developed and analyzed a state-of-the-art DL-based pipeline for wrist (distal radius) fracture detection—DeepWrist, and evaluated it against one general population test set, and one challenging test set comprising only cases requiring confirmation by CT. Our results reveal that a typical state-of-the-art approach, such as DeepWrist, while having a near-perfect performance on the general independent test set, has a substantially lower performance on the challenging test set—average precision of 0.99 (0.99–0.99) versus 0.64 (0.46–0.83), respectively. Similarly, the area under the ROC curve was of 0.99 (0.98–0.99) versus 0.84 (0.72–0.93), respectively. Our findings highlight the importance of a meticulous analysis of DL-based models before clinical use, and unearth the need for more challenging settings for testing medical AI systems.
format	article
author	Abu Mohammed Raisuddin Elias Vaattovaara Mika Nevalainen Marko Nikki Elina Järvenpää Kaisa Makkonen Pekka Pinola Tuula Palsio Arttu Niemensivu Osmo Tervonen Aleksei Tiulpin
author_facet	Abu Mohammed Raisuddin Elias Vaattovaara Mika Nevalainen Marko Nikki Elina Järvenpää Kaisa Makkonen Pekka Pinola Tuula Palsio Arttu Niemensivu Osmo Tervonen Aleksei Tiulpin
author_sort	Abu Mohammed Raisuddin
title	Critical evaluation of deep neural networks for wrist fracture detection
title_short	Critical evaluation of deep neural networks for wrist fracture detection
title_full	Critical evaluation of deep neural networks for wrist fracture detection
title_fullStr	Critical evaluation of deep neural networks for wrist fracture detection
title_full_unstemmed	Critical evaluation of deep neural networks for wrist fracture detection
title_sort	critical evaluation of deep neural networks for wrist fracture detection
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/e86336d998a043168ba26f739ea5a6ac
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Critical evaluation of deep neural networks for wrist fracture detection

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