A Practical Guide to Artificial Intelligence–Based Image Analysis in Radiology

Weikert, Thomas; Cyriac, Joshy; Yang, Shan; Nešić, Ivan; Parmar, Victor; Stieltjes, Bram

doi:10.1097/rli.0000000000000600

Cited by 42 publications

(51 citation statements)

References 52 publications

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“…Artificial intelligence, including machine learning and deep learning, has been increasingly applied to medical imaging. 219 Promising results have been shown in various tasks related to radiological images, such as the detection of lesions, 220 segmentation (eg, labeling organs), 221 classification (eg, pneumonia vs cancer), 222 reconstruction (eg, MRI k-space to clinical image), 223 and noise reduction. 224 In relation to standardization of QIB, an AI algorithm that automates the process of QIB extraction has the capability to decrease variability, such as through an automated pipeline that can reduce ambiguity and variability in lesion segmentation.…”

Section: Artificial Intelligencementioning

confidence: 99%

“…There are several recommendations and guidelines for the development and evaluation of an AI algorithm in the medical field. 219 , 227 , 228 In brief, desired steps to develop a reliable AI algorithm include the following: (1) using reliable reference standards, (2) using a training dataset that matches the intended use, (3) tuning hyperparameters on a dataset independent of the training dataset, and (4) using external datasets to evaluate the model performance. To develop an AI algorithm that is robust to variability in acquisition parameters, machine settings, and clinical conditions, the algorithm should be trained with a heterogeneous dataset.…”

Section: Artificial Intelligencementioning

confidence: 99%

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Variability and Standardization of Quantitative Imaging

et al. 2020

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Radiological images have been assessed qualitatively in most clinical settings by the expert eyes of radiologists and other clinicians. On the other hand, quantification of radiological images has the potential to detect early disease that may be difficult to detect with human eyes, complement or replace biopsy, and provide clear differentiation of disease stage. Further, objective assessment by quantification is a prerequisite of personalized/precision medicine. This review article aims to summarize and discuss how the variability of quantitative values derived from radiological images are induced by a number of factors and how these variabilities are mitigated and standardization of the quantitative values are achieved. We discuss the variabilities of specific biomarkers derived from magnetic resonance imaging and computed tomography, and focus on diffusion-weighted imaging, relaxometry, lung density evaluation, and computer-aided computed tomography volumetry. We also review the sources of variability and current efforts of standardization of the rapidly evolving techniques, which include radiomics and artificial intelligence.

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Section: Artificial Intelligencementioning

confidence: 99%

Section: Artificial Intelligencementioning

confidence: 99%

Variability and Standardization of Quantitative Imaging

et al. 2020

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“…Driven by increasing computing power and improving big data management, machine and deep learning-based convolutional neural networks (such as the Deep Convolutional Neural Network [DCNN]) can recognize and localize objects in medical images, 13 – 15 enabling disease characterization, tissue and lesion segmentation, and improved image reconstruction. 16 – 19 A single-center study using a homogenous dataset consisting of a standardized pulse sequence protocol from the same 3-T MRI scanner has shown that machine learning-based software can detect ACL tears with high accuracy.…”

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confidence: 99%

Deep Convolutional Neural Network–Based Diagnosis of Anterior Cruciate Ligament Tears

Germann

Marbach²,

Civardi³

et al. 2020

Invest Radiol

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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.

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“…Increasing computing power and improved big data management have led to substantial advances of artificial intelligence (AI) [18,19]. Machine learning and deep learning are subcategories of the broader field of AI, which describe concepts of self-learning computer algorithms with the capability of solving specific tasks without being programmed with explicit rules [20,21]. In particular, great progress has been made in the field of image classification over the past decade.…”

Section: Introductionmentioning

confidence: 99%

Deep convolutional neural network-based detection of meniscus tears: comparison with radiologists and surgery as standard of reference

Fritz

Marbach²,

Civardi³

et al. 2020

Skeletal Radiol

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Objective To clinically validate a fully automated deep convolutional neural network (DCNN) for detection of surgically proven meniscus tears. Materials and methods One hundred consecutive patients were retrospectively included, who underwent knee MRI and knee arthroscopy in our institution. All MRI were evaluated for medial and lateral meniscus tears by two musculoskeletal radiologists independently and by DCNN. Included patients were not part of the training set of the DCNN. Surgical reports served as the standard of reference. Statistics included sensitivity, specificity, accuracy, ROC curve analysis, and kappa statistics. Results Fifty-seven percent (57/100) of patients had a tear of the medial and 24% (24/100) of the lateral meniscus, including 12% (12/100) with a tear of both menisci. For medial meniscus tear detection, sensitivity, specificity, and accuracy were for reader 1: 93%, 91%, and 92%, for reader 2: 96%, 86%, and 92%, and for the DCNN: 84%, 88%, and 86%. For lateral meniscus tear detection, sensitivity, specificity, and accuracy were for reader 1: 71%, 95%, and 89%, for reader 2: 67%, 99%, and 91%, and for the DCNN: 58%, 92%, and 84%. Sensitivity for medial meniscus tears was significantly different between reader 2 and the DCNN (p = 0.039), and no significant differences existed for all other comparisons (all p ≥ 0.092). The AUC-ROC of the DCNN was 0.882, 0.781, and 0.961 for detection of medial, lateral, and overall meniscus tear. Inter-reader agreement was very good for the medial (kappa = 0.876) and good for the lateral meniscus (kappa = 0.741). Conclusion DCNN-based meniscus tear detection can be performed in a fully automated manner with a similar specificity but a lower sensitivity in comparison with musculoskeletal radiologists.

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A Practical Guide to Artificial Intelligence–Based Image Analysis in Radiology

Cited by 42 publications

References 52 publications

Variability and Standardization of Quantitative Imaging

Variability and Standardization of Quantitative Imaging

Deep Convolutional Neural Network–Based Diagnosis of Anterior Cruciate Ligament Tears

Deep convolutional neural network-based detection of meniscus tears: comparison with radiologists and surgery as standard of reference

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