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Ali Guermazi
Boston University
RadiologyMagnetic resonance imagingPhysical therapyOsteoarthritisMedicine
856Publications
73H-index
21.3kCitations
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Publications 878
Newest
#1Andreas Serner (Qatar Airways)H-Index: 10
#2Adam Weir (EUR: Erasmus University Rotterdam)H-Index: 26
Last. Per Hölmich (Qatar Airways)H-Index: 24
view all 8 authors...
Background:Time to return-to-sport (RTS) after acute adductor injuries varies among athletes, yet we know little about which factors determine this variance.Purpose:To investigate the association b...
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#1Natalie J. CollinsH-Index: 31
#2Tuhina NeogiH-Index: 47
Last. Joshua J. StefanikH-Index: 13
view all 9 authors...
OBJECTIVE: Determine the relation of symptomatic and structural features of patellofemoral osteoarthritis (PFOA) to psychological characteristics and measures of pain sensitisation, in older adults with or at risk of knee OA. METHODS: 1112 participants from the Multicenter Osteoarthritis Study were included (713 females; mean+/-SD age 66.8+/-7.6 years, body mass index 29.5+/-4.8 kg/m(2)). Participants were grouped based on presence of PFOA symptoms (anterior knee pain and pain on stairs) and MRI...
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#1Richard Kijowski (UW: University of Wisconsin-Madison)H-Index: 29
#2Shadpour Demehri (Johns Hopkins University)H-Index: 14
Last. Ali Guermazi (BU: Boston University)H-Index: 73
view all 4 authors...
Summary Objective To provide a narrative review of original articles on osteoarthritis (OA) imaging published between April 1, 2018 and March 30, 2019. Methods All original research articles on OA imaging published in English between April 1, 2018 and March 30, 2019 were identified using a PubMed database search. The search terms of "Osteoarthritis" or "OA" were combined with the search terms "Radiography", "X-Rays", "Magnetic Resonance Imaging", "MRI", "Ultrasound", "US", "Computed Tomography",...
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#1Victoria L. Johnson (USYD: University of Sydney)H-Index: 5
#2Ali Guermazi (BU: Boston University)H-Index: 73
Last. David H. Hunter (USYD: University of Sydney)H-Index: 184
view all 4 authors...
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#1Farhad Pishgar (Tehran University of Medical Sciences)
#2R. M. Kwee (JHUSOM: Johns Hopkins University School of Medicine)H-Index: 2
Last. Shadpour Demehri (JHUSOM: Johns Hopkins University School of Medicine)H-Index: 14
view all 6 authors...
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#2Tobias BäuerleH-Index: 23
Last. Frank W. RoemerH-Index: 52
view all 10 authors...
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#1Gary H. Chang (BU: Boston University)H-Index: 2
#2David T. Felson (University of Manchester)H-Index: 137
Last. Vijaya B. KolachalamaH-Index: 14
view all 6 authors...
It remains difficult to characterize the source of pain in knee joints either using radiographs or magnetic resonance imaging (MRI). We sought to determine if advanced machine learning methods such as deep neural networks could distinguish knees with pain from those without it and identify the structural features that are associated with knee pain. We constructed a convolutional Siamese network to associate MRI scans obtained on subjects from the Osteoarthritis Initiative (OAI) with frequent uni...
1 CitationsSource
#1J. Li (Southern Medical University)
#1Jia Li (Southern Medical University)H-Index: 1
Last. David H. Hunter (Southern Medical University)H-Index: 184
view all 11 authors...
Summary Objective To determine if qualitative and quantitative measures of prefemoral fat pad (PFP) and quadriceps fat pad (QFP) are associated with incident radiographic osteoarthritis (iROA) over 4 years in the Osteoarthritis Initiative (OAI) study. Design Participants in this nested case-control study were selected from the OAI study with knees that had a Kellgren Lawrence grading (KLG) = 0 or 1 at baseline. Case knees were defined by iROA (KLG≥ 2) over 4 years. Control knees without iROA wer...
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#1Shanshan Li (BU: Boston University)H-Index: 13
#2Ann V. Schwartz (UCSF: University of California, San Francisco)H-Index: 63
Last. Ali GuermaziH-Index: 73
view all 13 authors...
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#1Bochen Guan (UW: University of Wisconsin-Madison)H-Index: 1
#2F. Liu (Harvard University)H-Index: 1
Last. Richard Kijowski (UW: University of Wisconsin-Madison)H-Index: 29
view all 8 authors...
Summary Objective To develop and evaluate deep learning (DL) risk assessment models for predicting the progression of radiographic medial joint space loss using baseline knee X-rays. Methods Knees from the Osteoarthritis Initiative without and with progression of radiographic joint space loss (defined as > 0.7mm decrease in medial joint space width measurement between baseline and 48-month follow-up X-rays) were randomly stratified into training (1400 knees) and hold-out testing (400 knees) data...
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