About: Deep Convolutional Neural Network–Based Diagnosis of Anterior Cruciate Ligament Tears: Performance Comparison of Homogenous Versus Heterogeneous Knee MRI Cohorts With Different Pulse Sequence Protocols and 1.5-T and 3-T Magnetic Field Strengths

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About: Deep Convolutional Neural Network–Based Diagnosis of Anterior Cruciate Ligament Tears: Performance Comparison of Homogenous Versus Heterogeneous Knee MRI Cohorts With Different Pulse Sequence Protocols and 1.5-T and 3-T Magnetic Field Strengths Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Deep Convolutional Neural Network–Based Diagnosis of Anterior Cruciate Ligament Tears: Performance Comparison of Homogenous Versus Heterogeneous Knee MRI Cohorts With Different Pulse Sequence Protocols and 1.5-T and 3-T Magnetic Field Strengths
Creator	Fritz, Benjamin Civardi, Francesco Fritz, Jan Fucentese, Sandro Germann, Christoph Marbach, Giuseppe Sutter, Reto Christian, † Pfirrmann, W
Source	PMC
abstract	OBJECTIVES: The aim of this study was to clinically validate a Deep Convolutional Neural Network (DCNN) for the detection of surgically proven anterior cruciate ligament (ACL) tears in a large patient cohort and to analyze the effect of magnetic resonance examinations from different institutions, varying protocols, and field strengths. MATERIALS AND METHODS: After ethics committee approval, this retrospective analysis of prospectively collected data was performed on 512 consecutive subjects, who underwent knee magnetic resonance imaging (MRI) in a total of 59 different institutions followed by arthroscopic knee surgery at our institution. The DCNN and 3 fellowship-trained full-time academic musculoskeletal radiologists evaluated the MRI examinations for full-thickness ACL tears independently. Surgical reports served as the reference standard. Statistics included diagnostic performance metrics, including sensitivity, specificity, area under the receiver operating curve (“AUC ROC”), and kappa statistics. P values less than 0.05 were considered to represent statistical significance. RESULTS: Anterior cruciate ligament tears were present in 45.7% (234/512) and absent in 54.3% (278/512) of the subjects. The DCNN had a sensitivity of 96.1%, which was not significantly different from the readers (97.5%–97.9%; all P ≥ 0.118), but significantly lower specificity of 93.1% (readers, 99.6%–100%; all P < 0.001) and “AUC ROC” of 0.935 (readers, 0.989–0.991; all P < 0.001) for the entire cohort. Subgroup analysis showed a significantly lower sensitivity, specificity, and “AUC ROC” of the DCNN for outside MRI (92.5%, 87.1%, and 0.898, respectively) than in-house MRI (99.0%, 94.4%, and 0.967, respectively) examinations (P = 0.026, P = 0.043, and P < 0.05, respectively). There were no significant differences in DCNN performance for 1.5-T and 3-T MRI examinations (all P ≥ 0.753, respectively). CONCLUSIONS: Deep Convolutional Neural Network performance of ACL tear diagnosis can approach performance levels similar to fellowship-trained full-time academic musculoskeletal radiologists at 1.5 T and 3 T; however, the performance may decrease with increasing MRI examination heterogeneity.
has issue date	2020-03-11(xsd:dateTime)
bibo:doi	10.1097/rli.0000000000000664
bibo:pmid	32168039
has license	cc-by
sha1sum (hex)	e719878570476448264e87a21efef870f351c456
schema:url	https://doi.org/10.1097/rli.0000000000000664
resource representing a document's title	Deep Convolutional Neural Network–Based Diagnosis of Anterior Cruciate Ligament Tears: Performance Comparison of Homogenous Versus Heterogeneous Knee MRI Cohorts With Different Pulse Sequence Protocols and 1.5-T and 3-T Magnetic Field Strengths
has PubMed Central identifier	PMC7343178
has PubMed identifier	32168039
schema:publication	Invest Radiol
resource representing a document's body	covid:e719878570476448264e87a21efef870f351c456#body_text
is schema:about of	named entity 'readers' named entity 'anterior cruciate ligament (ACL)' named entity 'performance' named entity 'AUC' named entity 'MRI' named entity 'reference' named entity 'sensitivity' named entity 'Methods' named entity 'Cohorts' named entity 'ABSENT' named entity 'PRESENT' named entity 'ANTERIOR CRUCIATE LIGAMENT' named entity '0.001' named entity 'OBJECTIVES' named entity 'COHORT' named entity 'prospectively' named entity 'Surgical' named entity 'total' named entity 'academic' named entity 'resonance' named entity 'arthroscopic knee surgery' named entity 'MRI' named entity 'subjects' named entity 'ethics committee' named entity 'MRI' named entity 'arthroscopic knee surgery' named entity 'fellowship-trained' named entity 'statistical significance' named entity 'sensitivity, specificity' named entity 'MRI' named entity 'Anterior cruciate ligament tears' named entity 'Anterior Cruciate Ligament Tears' named entity 'readers' named entity 'performance' named entity 'area' named entity 'performance' named entity 'ACL tear' named entity 'knee instability' named entity '0.21' named entity 'ACL' named entity 'MRI systems' named entity 'pooling layer' named entity 'physical examination' named entity 'MRI systems' named entity 'ACL tears' named entity 'musculoskeletal' named entity 'area under the curve' named entity 'false-negative' named entity 'MRI' named entity 'cross-entropy' named entity 'radiologists' named entity 'rectified linear unit' named entity 'MRI' named entity 'MRI' named entity 'learning rate' named entity 'big data management' named entity 'rectified linear unit' named entity 'MRI scans' named entity 'sensitivity, specificity' named entity 'MR arthrography' named entity 'ACL tear' named entity 'machine learning' named entity 'fellowship-trained' named entity 'musculoskeletal' named entity 'MRI' named entity 'radiologists' named entity 'ACL' named entity 'ACL tears' named entity 'convolutional neural networks' named entity 'MRI' named entity 'black arrow' named entity 'radiologists' named entity 'ACL tear'

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