Qualitative versus quantitative lumbar spinal stenosis grading by machine learning supported texture analysis—Experience from the LSOS study cohort

Huber, Florian A.; Stutz, Shanon; Martini, Isabella; Mannil, Manoj; Becker, Anton S.; Winklhofer, Sebastian; Burgstaller, Jakob M; Guggenberger, Roman

doi:10.1016/j.ejrad.2019.02.023

Cited by 28 publications

(31 citation statements)

References 24 publications

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“…A related study of this cohort similarly demonstrated increased accuracy and reproducibility of ML-supported TA for grading of LSS, compared to qualitative assessment (Fig. 5) [56][57][58]. Furthermore, TA was able to outperform advanced qualitative scores that take the compression of the epidural fat into consideration, as proposed by Schizas et al [59].…”

Section: Clinical Translation and Outcome Predictionsupporting

confidence: 63%

AI MSK clinical applications: spine imaging

Huber

Guggenberger

2021

Skeletal Radiol

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Recent investigations have focused on the clinical application of artificial intelligence (AI) for tasks specifically addressing the musculoskeletal imaging routine. Several AI applications have been dedicated to optimizing the radiology value chain in spine imaging, independent from modality or specific application. This review aims to summarize the status quo and future perspective regarding utilization of AI for spine imaging. First, the basics of AI concepts are clarified. Second, the different tasks and use cases for AI applications in spine imaging are discussed and illustrated by examples. Finally, the authors of this review present their personal perception of AI in daily imaging and discuss future chances and challenges that come along with AI-based solutions.

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Section: Clinical Translation and Outcome Predictionsupporting

confidence: 63%

AI MSK clinical applications: spine imaging

Huber

Guggenberger

2021

Skeletal Radiol

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“…Texture analysis (TA) as part of ambitions to extract quantitative information from medical images characterizes signal patterns of pixels or voxels in a region or volume of interest mostly imperceptible to the human eye and is able to quantify parameters with high reproducibility [9]. TA has proven to be successful in characterization of lesion malignancy as well as in quantification of degenerative musculoskeletal disorders [10][11][12] and could potentially help in differentiating MR findings in SIJs.…”

Section: Introductionmentioning

confidence: 99%

Differentiation of inflammatory from degenerative changes in the sacroiliac joints by machine learning supported texture analysis

Kepp

Huber

Wurnig

et al. 2021

European Journal of Radiology

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To compare the diagnostic performance of texture analysis (TA) against visual qualitative assessment in the differentiation of spondyloarthritis (SpA) from degenerative changes in the sacroiliac joints (SIJ). Method: Ninety patients referred for suspected inflammatory lower back pain from the rheumatology department were retrospectively included at our university hospital institution. MRI at 3 T of the lumbar spine and SIJ was performed with oblique coronal T1-weighted (w), fluid-sensitive fat-saturated (fs) TIRM and fsT1w intravenously contrast-enhanced (CE) images. Subjects were divided into three age-and gender-matched groups (30 each) based on definite clinical diagnosis serving as clinical reference standard with either degenerative, inflammatory (SpA) or no changes of the SIJ. SIJ were rated qualitatively by two independent radiologists and quantitatively by region-of-interest-based TA with 304 features subjected to machine learning logistic regression with randomized ten-fold selection of training and validation data. Qualitative and quantitative results were evaluated for diagnostic performance and compared against clinical reference standard. Results: Agreement of radiologist's diagnose with clinical reference was fair for both readers (κ = 0.32 and 0.44). ROC statistics revealed significant outperformance of TA compared to qualitative ratings for differentiation of SpA from remainder (AUC = 0.89 vs. 0.75), SpA from degenerative (AUC = 0.91 vs. 0.67) and TIRM-positive SpA (i.e. with bone marrow edema) from remainder cases (AUC = 0.95 vs. 0.76). T1w-CE images were the most important discriminator for detection of SpA. Conclusions: TA is superior to qualitative assessment for the differentiation of inflammatory from degenerative changes of the SIJ. Intravenous CE-images increase diagnostic yield in quantitative TA.

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“…More recently the scope of AI to aid diagnostic imaging has expanded outside of the spine, with uses ranging from the identification of hip fractures to soft tissue meniscal tears in the knee [17][18][19]. There has also been a shift to algorithms providing a more nuanced grading of disease, rather than binary outputs [20].…”

Section: Imagingmentioning

confidence: 99%

Artificial intelligence in orthopaedics: A scoping review

Federer

Jones

2021

PLoS ONE

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There is a growing interest in the application of artificial intelligence (AI) to orthopaedic surgery. This review aims to identify and characterise research in this field, in order to understand the extent, range and nature of this work, and act as springboard to stimulate future studies. A scoping review, a form of structured evidence synthesis, was conducted to summarise the use of AI in orthopaedics. A literature search (1946–2019) identified 222 studies eligible for inclusion. These studies were predominantly small and retrospective. There has been significant growth in the number of papers published in the last three years, mainly from the USA (37%). The majority of research used AI for image interpretation (45%) or as a clinical decision tool (25%). Spine (43%), knee (23%) and hip (14%) were the regions of the body most commonly studied. The application of artificial intelligence to orthopaedics is growing. However, the scope of its use so far remains limited, both in terms of its possible clinical applications, and the sub-specialty areas of the body which have been studied. A standardized method of reporting AI studies would allow direct assessment and comparison. Prospective studies are required to validate AI tools for clinical use.

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Qualitative versus quantitative lumbar spinal stenosis grading by machine learning supported texture analysis—Experience from the LSOS study cohort

Cited by 28 publications

References 24 publications

AI MSK clinical applications: spine imaging

AI MSK clinical applications: spine imaging

Differentiation of inflammatory from degenerative changes in the sacroiliac joints by machine learning supported texture analysis

Artificial intelligence in orthopaedics: A scoping review

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