Faster Region-Based Convolutional Neural Network in the Classification of Different Parkinsonism Patterns of the Striatum on Maximum Intensity Projection Images of [<sup<18</sup<F]FP-CIT Positron Emission Tomography

The aim of this study was to compare the performance of a deep-learning convolutional neural network (Faster R-CNN) model to detect imaging findings suggestive of idiopathic Parkinson’s disease (PD) based on [<sup<18</sup<F]FP-CIT PET maximum intensity projection (MIP) images versus that...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Byung Wook Choi [verfasserIn] Sungmin Kang [verfasserIn] Hae Won Kim [verfasserIn] Oh Dae Kwon [verfasserIn] Huy Duc Vu [verfasserIn] Sung Won Youn [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	artificial intelligence dopamine transporter deep learning Parkinson’s disease positron emission tomography

Übergeordnetes Werk:	In: Diagnostics - MDPI AG, 2012, 11(2021), 9, p 1557
Übergeordnetes Werk:	volume:11 ; year:2021 ; number:9, p 1557

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/diagnostics11091557

Katalog-ID:	DOAJ085562386

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allfields	10.3390/diagnostics11091557 doi (DE-627)DOAJ085562386 (DE-599)DOAJ552ed95652ab45068bd1481771d73010 DE-627 ger DE-627 rakwb eng R5-920 Byung Wook Choi verfasserin aut Faster Region-Based Convolutional Neural Network in the Classification of Different Parkinsonism Patterns of the Striatum on Maximum Intensity Projection Images of [<sup<18</sup<F]FP-CIT Positron Emission Tomography 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The aim of this study was to compare the performance of a deep-learning convolutional neural network (Faster R-CNN) model to detect imaging findings suggestive of idiopathic Parkinson’s disease (PD) based on [<sup<18</sup<F]FP-CIT PET maximum intensity projection (MIP) images versus that of nuclear medicine (NM) physicians. The anteroposterior MIP images of the [<sup<18</sup<F]FP-CIT PET scan of 527 patients were classified as having PD (139 images) or non-PD (388 images) patterns according to the final diagnosis. Non-PD patterns were classified as overall-normal (ONL, 365 images) and vascular parkinsonism with definite defects or prominently decreased dopamine transporter binding (dVP, 23 images) patterns. Faster R-CNN was trained on 120 PD, 320 ONL, and 16 dVP pattern images and tested on the 19 PD, 45 ONL, and seven dVP patterns images. The performance of the Faster R-CNN and three NM physicians was assessed using receiver operating characteristics curve analysis. The difference in performance was assessed using Cochran’s Q test, and the inter-rater reliability was calculated. Faster R-CNN showed high accuracy in differentiating PD from non-PD patterns and also from dVP patterns, with results comparable to those of NM physicians. There were no significant differences in the area under the curve and performance. The inter-rater reliability among Faster R-CNN and NM physicians showed substantial to almost perfect agreement. The deep-learning model accurately differentiated PD from non-PD patterns on MIP images of [<sup<18</sup<F]FP-CIT PET, and its performance was comparable to that of NM physicians. artificial intelligence dopamine transporter deep learning Parkinson’s disease positron emission tomography Medicine (General) Sungmin Kang verfasserin aut Hae Won Kim verfasserin aut Oh Dae Kwon verfasserin aut Huy Duc Vu verfasserin aut Sung Won Youn verfasserin aut In Diagnostics MDPI AG, 2012 11(2021), 9, p 1557 (DE-627)718627814 (DE-600)2662336-5 20754418 nnns volume:11 year:2021 number:9, p 1557 https://doi.org/10.3390/diagnostics11091557 kostenfrei https://doaj.org/article/552ed95652ab45068bd1481771d73010 kostenfrei https://www.mdpi.com/2075-4418/11/9/1557 kostenfrei https://doaj.org/toc/2075-4418 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2021 9, p 1557
spelling	10.3390/diagnostics11091557 doi (DE-627)DOAJ085562386 (DE-599)DOAJ552ed95652ab45068bd1481771d73010 DE-627 ger DE-627 rakwb eng R5-920 Byung Wook Choi verfasserin aut Faster Region-Based Convolutional Neural Network in the Classification of Different Parkinsonism Patterns of the Striatum on Maximum Intensity Projection Images of [<sup<18</sup<F]FP-CIT Positron Emission Tomography 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The aim of this study was to compare the performance of a deep-learning convolutional neural network (Faster R-CNN) model to detect imaging findings suggestive of idiopathic Parkinson’s disease (PD) based on [<sup<18</sup<F]FP-CIT PET maximum intensity projection (MIP) images versus that of nuclear medicine (NM) physicians. The anteroposterior MIP images of the [<sup<18</sup<F]FP-CIT PET scan of 527 patients were classified as having PD (139 images) or non-PD (388 images) patterns according to the final diagnosis. Non-PD patterns were classified as overall-normal (ONL, 365 images) and vascular parkinsonism with definite defects or prominently decreased dopamine transporter binding (dVP, 23 images) patterns. Faster R-CNN was trained on 120 PD, 320 ONL, and 16 dVP pattern images and tested on the 19 PD, 45 ONL, and seven dVP patterns images. The performance of the Faster R-CNN and three NM physicians was assessed using receiver operating characteristics curve analysis. The difference in performance was assessed using Cochran’s Q test, and the inter-rater reliability was calculated. Faster R-CNN showed high accuracy in differentiating PD from non-PD patterns and also from dVP patterns, with results comparable to those of NM physicians. There were no significant differences in the area under the curve and performance. The inter-rater reliability among Faster R-CNN and NM physicians showed substantial to almost perfect agreement. The deep-learning model accurately differentiated PD from non-PD patterns on MIP images of [<sup<18</sup<F]FP-CIT PET, and its performance was comparable to that of NM physicians. artificial intelligence dopamine transporter deep learning Parkinson’s disease positron emission tomography Medicine (General) Sungmin Kang verfasserin aut Hae Won Kim verfasserin aut Oh Dae Kwon verfasserin aut Huy Duc Vu verfasserin aut Sung Won Youn verfasserin aut In Diagnostics MDPI AG, 2012 11(2021), 9, p 1557 (DE-627)718627814 (DE-600)2662336-5 20754418 nnns volume:11 year:2021 number:9, p 1557 https://doi.org/10.3390/diagnostics11091557 kostenfrei https://doaj.org/article/552ed95652ab45068bd1481771d73010 kostenfrei https://www.mdpi.com/2075-4418/11/9/1557 kostenfrei https://doaj.org/toc/2075-4418 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2021 9, p 1557
allfields_unstemmed	10.3390/diagnostics11091557 doi (DE-627)DOAJ085562386 (DE-599)DOAJ552ed95652ab45068bd1481771d73010 DE-627 ger DE-627 rakwb eng R5-920 Byung Wook Choi verfasserin aut Faster Region-Based Convolutional Neural Network in the Classification of Different Parkinsonism Patterns of the Striatum on Maximum Intensity Projection Images of [<sup<18</sup<F]FP-CIT Positron Emission Tomography 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The aim of this study was to compare the performance of a deep-learning convolutional neural network (Faster R-CNN) model to detect imaging findings suggestive of idiopathic Parkinson’s disease (PD) based on [<sup<18</sup<F]FP-CIT PET maximum intensity projection (MIP) images versus that of nuclear medicine (NM) physicians. The anteroposterior MIP images of the [<sup<18</sup<F]FP-CIT PET scan of 527 patients were classified as having PD (139 images) or non-PD (388 images) patterns according to the final diagnosis. Non-PD patterns were classified as overall-normal (ONL, 365 images) and vascular parkinsonism with definite defects or prominently decreased dopamine transporter binding (dVP, 23 images) patterns. Faster R-CNN was trained on 120 PD, 320 ONL, and 16 dVP pattern images and tested on the 19 PD, 45 ONL, and seven dVP patterns images. The performance of the Faster R-CNN and three NM physicians was assessed using receiver operating characteristics curve analysis. The difference in performance was assessed using Cochran’s Q test, and the inter-rater reliability was calculated. Faster R-CNN showed high accuracy in differentiating PD from non-PD patterns and also from dVP patterns, with results comparable to those of NM physicians. There were no significant differences in the area under the curve and performance. The inter-rater reliability among Faster R-CNN and NM physicians showed substantial to almost perfect agreement. The deep-learning model accurately differentiated PD from non-PD patterns on MIP images of [<sup<18</sup<F]FP-CIT PET, and its performance was comparable to that of NM physicians. artificial intelligence dopamine transporter deep learning Parkinson’s disease positron emission tomography Medicine (General) Sungmin Kang verfasserin aut Hae Won Kim verfasserin aut Oh Dae Kwon verfasserin aut Huy Duc Vu verfasserin aut Sung Won Youn verfasserin aut In Diagnostics MDPI AG, 2012 11(2021), 9, p 1557 (DE-627)718627814 (DE-600)2662336-5 20754418 nnns volume:11 year:2021 number:9, p 1557 https://doi.org/10.3390/diagnostics11091557 kostenfrei https://doaj.org/article/552ed95652ab45068bd1481771d73010 kostenfrei https://www.mdpi.com/2075-4418/11/9/1557 kostenfrei https://doaj.org/toc/2075-4418 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2021 9, p 1557
allfieldsGer	10.3390/diagnostics11091557 doi (DE-627)DOAJ085562386 (DE-599)DOAJ552ed95652ab45068bd1481771d73010 DE-627 ger DE-627 rakwb eng R5-920 Byung Wook Choi verfasserin aut Faster Region-Based Convolutional Neural Network in the Classification of Different Parkinsonism Patterns of the Striatum on Maximum Intensity Projection Images of [<sup<18</sup<F]FP-CIT Positron Emission Tomography 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The aim of this study was to compare the performance of a deep-learning convolutional neural network (Faster R-CNN) model to detect imaging findings suggestive of idiopathic Parkinson’s disease (PD) based on [<sup<18</sup<F]FP-CIT PET maximum intensity projection (MIP) images versus that of nuclear medicine (NM) physicians. The anteroposterior MIP images of the [<sup<18</sup<F]FP-CIT PET scan of 527 patients were classified as having PD (139 images) or non-PD (388 images) patterns according to the final diagnosis. Non-PD patterns were classified as overall-normal (ONL, 365 images) and vascular parkinsonism with definite defects or prominently decreased dopamine transporter binding (dVP, 23 images) patterns. Faster R-CNN was trained on 120 PD, 320 ONL, and 16 dVP pattern images and tested on the 19 PD, 45 ONL, and seven dVP patterns images. The performance of the Faster R-CNN and three NM physicians was assessed using receiver operating characteristics curve analysis. The difference in performance was assessed using Cochran’s Q test, and the inter-rater reliability was calculated. Faster R-CNN showed high accuracy in differentiating PD from non-PD patterns and also from dVP patterns, with results comparable to those of NM physicians. There were no significant differences in the area under the curve and performance. The inter-rater reliability among Faster R-CNN and NM physicians showed substantial to almost perfect agreement. The deep-learning model accurately differentiated PD from non-PD patterns on MIP images of [<sup<18</sup<F]FP-CIT PET, and its performance was comparable to that of NM physicians. artificial intelligence dopamine transporter deep learning Parkinson’s disease positron emission tomography Medicine (General) Sungmin Kang verfasserin aut Hae Won Kim verfasserin aut Oh Dae Kwon verfasserin aut Huy Duc Vu verfasserin aut Sung Won Youn verfasserin aut In Diagnostics MDPI AG, 2012 11(2021), 9, p 1557 (DE-627)718627814 (DE-600)2662336-5 20754418 nnns volume:11 year:2021 number:9, p 1557 https://doi.org/10.3390/diagnostics11091557 kostenfrei https://doaj.org/article/552ed95652ab45068bd1481771d73010 kostenfrei https://www.mdpi.com/2075-4418/11/9/1557 kostenfrei https://doaj.org/toc/2075-4418 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2021 9, p 1557
allfieldsSound	10.3390/diagnostics11091557 doi (DE-627)DOAJ085562386 (DE-599)DOAJ552ed95652ab45068bd1481771d73010 DE-627 ger DE-627 rakwb eng R5-920 Byung Wook Choi verfasserin aut Faster Region-Based Convolutional Neural Network in the Classification of Different Parkinsonism Patterns of the Striatum on Maximum Intensity Projection Images of [<sup<18</sup<F]FP-CIT Positron Emission Tomography 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The aim of this study was to compare the performance of a deep-learning convolutional neural network (Faster R-CNN) model to detect imaging findings suggestive of idiopathic Parkinson’s disease (PD) based on [<sup<18</sup<F]FP-CIT PET maximum intensity projection (MIP) images versus that of nuclear medicine (NM) physicians. The anteroposterior MIP images of the [<sup<18</sup<F]FP-CIT PET scan of 527 patients were classified as having PD (139 images) or non-PD (388 images) patterns according to the final diagnosis. Non-PD patterns were classified as overall-normal (ONL, 365 images) and vascular parkinsonism with definite defects or prominently decreased dopamine transporter binding (dVP, 23 images) patterns. Faster R-CNN was trained on 120 PD, 320 ONL, and 16 dVP pattern images and tested on the 19 PD, 45 ONL, and seven dVP patterns images. The performance of the Faster R-CNN and three NM physicians was assessed using receiver operating characteristics curve analysis. The difference in performance was assessed using Cochran’s Q test, and the inter-rater reliability was calculated. Faster R-CNN showed high accuracy in differentiating PD from non-PD patterns and also from dVP patterns, with results comparable to those of NM physicians. There were no significant differences in the area under the curve and performance. The inter-rater reliability among Faster R-CNN and NM physicians showed substantial to almost perfect agreement. The deep-learning model accurately differentiated PD from non-PD patterns on MIP images of [<sup<18</sup<F]FP-CIT PET, and its performance was comparable to that of NM physicians. artificial intelligence dopamine transporter deep learning Parkinson’s disease positron emission tomography Medicine (General) Sungmin Kang verfasserin aut Hae Won Kim verfasserin aut Oh Dae Kwon verfasserin aut Huy Duc Vu verfasserin aut Sung Won Youn verfasserin aut In Diagnostics MDPI AG, 2012 11(2021), 9, p 1557 (DE-627)718627814 (DE-600)2662336-5 20754418 nnns volume:11 year:2021 number:9, p 1557 https://doi.org/10.3390/diagnostics11091557 kostenfrei https://doaj.org/article/552ed95652ab45068bd1481771d73010 kostenfrei https://www.mdpi.com/2075-4418/11/9/1557 kostenfrei https://doaj.org/toc/2075-4418 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2021 9, p 1557
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callnumber-first	R - Medicine
author	Byung Wook Choi
spellingShingle	Byung Wook Choi misc R5-920 misc artificial intelligence misc dopamine transporter misc deep learning misc Parkinson’s disease misc positron emission tomography misc Medicine (General) Faster Region-Based Convolutional Neural Network in the Classification of Different Parkinsonism Patterns of the Striatum on Maximum Intensity Projection Images of [<sup<18</sup<F]FP-CIT Positron Emission Tomography
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topic_title	R5-920 Faster Region-Based Convolutional Neural Network in the Classification of Different Parkinsonism Patterns of the Striatum on Maximum Intensity Projection Images of [<sup<18</sup<F]FP-CIT Positron Emission Tomography artificial intelligence dopamine transporter deep learning Parkinson’s disease positron emission tomography
topic	misc R5-920 misc artificial intelligence misc dopamine transporter misc deep learning misc Parkinson’s disease misc positron emission tomography misc Medicine (General)
topic_unstemmed	misc R5-920 misc artificial intelligence misc dopamine transporter misc deep learning misc Parkinson’s disease misc positron emission tomography misc Medicine (General)
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title	Faster Region-Based Convolutional Neural Network in the Classification of Different Parkinsonism Patterns of the Striatum on Maximum Intensity Projection Images of [<sup<18</sup<F]FP-CIT Positron Emission Tomography
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title_full	Faster Region-Based Convolutional Neural Network in the Classification of Different Parkinsonism Patterns of the Striatum on Maximum Intensity Projection Images of [<sup<18</sup<F]FP-CIT Positron Emission Tomography
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author_browse	Byung Wook Choi Sungmin Kang Hae Won Kim Oh Dae Kwon Huy Duc Vu Sung Won Youn
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title_sort	faster region-based convolutional neural network in the classification of different parkinsonism patterns of the striatum on maximum intensity projection images of [<sup<18</sup<f]fp-cit positron emission tomography
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title_auth	Faster Region-Based Convolutional Neural Network in the Classification of Different Parkinsonism Patterns of the Striatum on Maximum Intensity Projection Images of [<sup<18</sup<F]FP-CIT Positron Emission Tomography
abstract	The aim of this study was to compare the performance of a deep-learning convolutional neural network (Faster R-CNN) model to detect imaging findings suggestive of idiopathic Parkinson’s disease (PD) based on [<sup<18</sup<F]FP-CIT PET maximum intensity projection (MIP) images versus that of nuclear medicine (NM) physicians. The anteroposterior MIP images of the [<sup<18</sup<F]FP-CIT PET scan of 527 patients were classified as having PD (139 images) or non-PD (388 images) patterns according to the final diagnosis. Non-PD patterns were classified as overall-normal (ONL, 365 images) and vascular parkinsonism with definite defects or prominently decreased dopamine transporter binding (dVP, 23 images) patterns. Faster R-CNN was trained on 120 PD, 320 ONL, and 16 dVP pattern images and tested on the 19 PD, 45 ONL, and seven dVP patterns images. The performance of the Faster R-CNN and three NM physicians was assessed using receiver operating characteristics curve analysis. The difference in performance was assessed using Cochran’s Q test, and the inter-rater reliability was calculated. Faster R-CNN showed high accuracy in differentiating PD from non-PD patterns and also from dVP patterns, with results comparable to those of NM physicians. There were no significant differences in the area under the curve and performance. The inter-rater reliability among Faster R-CNN and NM physicians showed substantial to almost perfect agreement. The deep-learning model accurately differentiated PD from non-PD patterns on MIP images of [<sup<18</sup<F]FP-CIT PET, and its performance was comparable to that of NM physicians.
abstractGer	The aim of this study was to compare the performance of a deep-learning convolutional neural network (Faster R-CNN) model to detect imaging findings suggestive of idiopathic Parkinson’s disease (PD) based on [<sup<18</sup<F]FP-CIT PET maximum intensity projection (MIP) images versus that of nuclear medicine (NM) physicians. The anteroposterior MIP images of the [<sup<18</sup<F]FP-CIT PET scan of 527 patients were classified as having PD (139 images) or non-PD (388 images) patterns according to the final diagnosis. Non-PD patterns were classified as overall-normal (ONL, 365 images) and vascular parkinsonism with definite defects or prominently decreased dopamine transporter binding (dVP, 23 images) patterns. Faster R-CNN was trained on 120 PD, 320 ONL, and 16 dVP pattern images and tested on the 19 PD, 45 ONL, and seven dVP patterns images. The performance of the Faster R-CNN and three NM physicians was assessed using receiver operating characteristics curve analysis. The difference in performance was assessed using Cochran’s Q test, and the inter-rater reliability was calculated. Faster R-CNN showed high accuracy in differentiating PD from non-PD patterns and also from dVP patterns, with results comparable to those of NM physicians. There were no significant differences in the area under the curve and performance. The inter-rater reliability among Faster R-CNN and NM physicians showed substantial to almost perfect agreement. The deep-learning model accurately differentiated PD from non-PD patterns on MIP images of [<sup<18</sup<F]FP-CIT PET, and its performance was comparable to that of NM physicians.
abstract_unstemmed	The aim of this study was to compare the performance of a deep-learning convolutional neural network (Faster R-CNN) model to detect imaging findings suggestive of idiopathic Parkinson’s disease (PD) based on [<sup<18</sup<F]FP-CIT PET maximum intensity projection (MIP) images versus that of nuclear medicine (NM) physicians. The anteroposterior MIP images of the [<sup<18</sup<F]FP-CIT PET scan of 527 patients were classified as having PD (139 images) or non-PD (388 images) patterns according to the final diagnosis. Non-PD patterns were classified as overall-normal (ONL, 365 images) and vascular parkinsonism with definite defects or prominently decreased dopamine transporter binding (dVP, 23 images) patterns. Faster R-CNN was trained on 120 PD, 320 ONL, and 16 dVP pattern images and tested on the 19 PD, 45 ONL, and seven dVP patterns images. The performance of the Faster R-CNN and three NM physicians was assessed using receiver operating characteristics curve analysis. The difference in performance was assessed using Cochran’s Q test, and the inter-rater reliability was calculated. Faster R-CNN showed high accuracy in differentiating PD from non-PD patterns and also from dVP patterns, with results comparable to those of NM physicians. There were no significant differences in the area under the curve and performance. The inter-rater reliability among Faster R-CNN and NM physicians showed substantial to almost perfect agreement. The deep-learning model accurately differentiated PD from non-PD patterns on MIP images of [<sup<18</sup<F]FP-CIT PET, and its performance was comparable to that of NM physicians.
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container_issue	9, p 1557
title_short	Faster Region-Based Convolutional Neural Network in the Classification of Different Parkinsonism Patterns of the Striatum on Maximum Intensity Projection Images of [<sup<18</sup<F]FP-CIT Positron Emission Tomography
url	https://doi.org/10.3390/diagnostics11091557 https://doaj.org/article/552ed95652ab45068bd1481771d73010 https://www.mdpi.com/2075-4418/11/9/1557 https://doaj.org/toc/2075-4418
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Faster Region-Based Convolutional Neural Network in the Classification of Different Parkinsonism Patterns of the Striatum on Maximum Intensity Projection Images of [<sup<18</sup<F]FP-CIT Positron Emission Tomography

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