Advancing FAI Surgery Outcomes through AI-based Radiomics Analysis of Hip MRI

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Section 1 - Basic information about you and your application:

Title of research project
Advancing FAI Surgery Outcomes through AI-based Radiomics Analysis of Hip MRI

Project summary

Femoroacetabular impingement (FAI) poses a significant challenge in orthopaedics, often necessitating surgical intervention. However, a substantial proportion of patients do not experience the expected benefits post-FAI surgery. Current clinical assessments for FAI are hindered by symptom non-specificity and limited radiographic reliability, exacerbating the need for a more sophisticated preoperative approach. Radiomics, an emerging field in medical imaging, offers a promising avenue to extract nuanced information from images, surpassing human capabilities. Building upon a robust radiomic foundation, this collaborative study between Imperial College and The University of Oxford will apply advanced techniques to hip MRI data collected from the FAIT trial. Machine learning algorithms will then evaluate correlations between Patient-Reported Outcome Measures (PROMs) and radiomic features, providing valuable insights into the predictive potential of radiomics in evaluating clinical outcomes post-FAI surgery. This project represents a pioneering step in enhancing FAI surgery outcomes through cutting-edge technology and multidisciplinary collaboration.

Type of project

Type of research

Machine Learning, Radiomics, Femoroacetabular impingement

Start date

End date

Section 2 - Purpose of the research and originality

Aims / Objectives:

The aims of this study are to: 1) develop AI-based Radiomics analysis of hip MRI for the assessment of patients with FAIS, and 2) evaluate the association between radiomics features of FAI and patient-reported outcomes of FAIS patients who underwent hip arthroscopic surgery. 

Background to investigation

Femoroacetabular impingement (FAI) describes the pathological abutment of the femur on the acetabulum, either due to abnormal femoral head-neck morphology (cam-type FAI), acetabular morphology (pincer-type FAI), or a ‘mixed’ aetiology. FAI results in reduced range of motion and pain in deep flexion and internal rotation, and is often associated with damage to the labrum and the chondrolabral junction.  Symptomatic FAI typically presents in active, young adults (1). 

The magnitude of FAI pathomorphology can be measured on plain radiographs, CT imaging, and MR imaging (MRI). In 2D, the alpha-angle quantifies cam-type femoral morphology, and the lateral centre edge angle (LCEA) quantifies pincer-type acetabular over-coverage. 3D analysis of CT has enabled measurement of the alpha angle at multiple points around the femoral head-neck junction (Clinical Graphics, Den Haag, The Netherlands). However, these have highly variable sensitivity and specificity for diagnosing FAI, as 2D measurements of 3D anatomy are affected by patient position (2). 

The combination of imaging findings correlated with this clinical presentation is termed FAI Syndrome (FAIS) (3). Two seminal randomised controlled trials have demonstrated that repairing the labrum, and reshaping the femoral head-neck junction and/or acetabular rim through hip arthroscopy is more effective than conservative management.(5,6) However, approximately one third of patients undergoing FAI surgery do not gain a clinically meaningful benefit. Those with advanced arthritis are less likely to improve with surgery, but generally the negative predictors are less well understood (6). Plain radiographic assessments have demonstrated limited reliability for differentiating between a healthy hip joint and early osteoarthritis (OA); the magnitude of the cam- or pincer-morphology is also not predictive of outcome (7).

MRI is a reference modality for identifying labral tears, subchondral and paralabral cysts, and subtle osteophytes in patients with FAI undergoing arthroscopy. MRI changes of advanced arthritis are correlated surgical outcomes (8). However, these macroscopic anatomical findings in MRI have poor specificity and negative predictive value (9), and the utility of MRI in patients with early/no OA is unproven. 

This underscores the urgent need for a more sophisticated approach to preoperative assessment. Radiomics, a burgeoning field in medical imaging, offers a promising avenue for obtaining nuanced information from images that surpasses the ability of a human making direct measurements. A multitude of imaging features are extracted from a region of interest and subsequently reduced and selected to convey diagnostic or prognostic information. The assumption of radiomics is that image features quantify crucial information regarding pathologic conditions through intra-region heterogeneity. For the hip, radiomics takes 190 measurements; these are grouped into (i) intensity and histogram based first order statistics (FOS) features, (ii) texture features, and (iii) shape and size features. Deep radiomics uses convolutional neural networks (CNNs, a type of artificial intelligence (AI)) to directly extract features and obviate the need for predefined features. MRI-based radiomics has recently been validated for diagnosing FAI more accurately and rapidly than conventional measurement techniques (2). However, these have focused on shape and signal characteristics of the femoral and acetabular bone contour alone, without analyses of chondral surfaces, the labrum, or subchondral oedema - relevant to the clinical presentation of FAIS. 

The proposed study will develop AI-Radiomics to interrogate MRI for shape, and chondral, labral and subchondral features related to FAIS. It will develop AI-based techniques, particularly those employing shape and gradient analyses to extract radiomic features from MRI. Finally, it will validate if these features are predictive of patient-reported outcomes after hip arthroscopy - thus assessing applicability of AI-Radiomics to clinical practice.


  1. Palmer A, Fernquest S, Gimpel M, Birchall R, Judge A, Broomfield J, et al. Physical activity during adolescence and the development of cam morphology: a cross-sectional cohort study of 210 individuals. British Journal of Sports Medicine. 2018 May 1;52(9):601–10. 
  2. Montin E, Kijowski R, Youm T, Lattanzi R. A radiomics approach to the diagnosis of femoroacetabular impingement. Frontiers in Radiology.2023 Mar 20;3:1151258.
  3. Griffin DR, Dickenson EJ, O’Donnell J, Agricola R, Awan T, Beck M, et al. The Warwick Agreement on Femoroacetabular Impingement Syndrome (FAI syndrome): an International Consensus Statement. British Journal of Sports Medicine. 2016 Sep 14;50(19):1169–76.
  4. Palmer AJR, Ayyar Gupta V, Fernquest S, Rombach I, Dutton SJ, Mansour R, et al. Arthroscopic hip surgery compared with physiotherapy and activity modification for the treatment of symptomatic femoroacetabular impingement: multicentre randomised controlled trial. BMJ. 2019 Feb 7;185.
  5. Griffin DR, Dickenson EJ, Wall PDH, Achana F, Donovan JL, Griffin J, et al. Hip arthroscopy versus best conservative care for the treatment of femoroacetabular impingement syndrome (UK FASHIoN): a multicentre randomised controlled trial. The Lancet. 2018 Jun;391(10136):2225–35.
  6. Andronic O, Claydon-Mueller LS, Cubberley R, Karczewski D, Sunil-Kumar KH, Khanduja V. Inconclusive and Contradictory Evidence for Outcomes After Hip Arthroscopy in Patients With Femoroacetabular Impingement and Osteoarthritis of Tönnis Grade 2 or Greater: A Systematic Review. Arthroscopy: The Journal of Arthroscopic & Related Surgery. 2022 Jul;38(7):2307-2318.e1. 
  7. Jacobs CA, Burnham JM, Jochimsen KN, Molina D, Hamilton DA, Duncan ST. Preoperative Symptoms in Femoroacetabular Impingement Patients Are More Related to Mental Health Scores Than the Severity of Labral Tear or Magnitude of Bony Deformity. The Journal of Arthroplasty. 2017 Dec;32(12):3603–6.
  8. Conaway W, Agrawal R, Skelley NW, Waryasz GR, Small KM, Shah N, et al. MRA Findings Predictive of Hip Arthroscopy Outcomes for Femoroacetabular Impingement. Arthroscopy: The Journal of Arthroscopic & Related Surgery. 2018 Dec 1;34(12):e17–8.
  9. Annabell L, Master V, Rhodes A, Moreira B, Coetzee C, Tran P. Hip pathology: the diagnostic accuracy of magnetic resonance imaging. Journal of Orthopaedic Surgery and Research. 2018 May 29;13(1).

Purpose of the research and originality(Required)

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