Narrative review of open source, proprietary, and experimental artificial intelligence algorithms in radiology

Introduction

Artificial intelligence (AI) encompasses the technological simulation of intelligence. As a highly technology reliant field, radiology has consistently been on the forefront of scientific and computational advancements. A core component of AI is its role in image recognition. From facial and object recognition to self-driving vehicles, AI has developed into a meaningful tool for streamlining otherwise tedious workflows. As a subset of AI, machine learning (ML) represents the specific algorithms which give rise to computational intelligence. ML has been used in precision medicine to predict patient success on various treatment protocols given specific characteristics of the treatment and the patient’s disease (1). ML works by fitting models to data and subsequently improving the model through iterative training with larger data sets. Neural networks refer to a specific type of ML which takes on a form reminiscent of neural interaction in the human brain. Complex artificial neural networks (ANNs) with multiple levels of computation and hierarchical nodal structure give rise to deep learning (DL). DL has revolutionized the field of radiomics by detecting clinically relevant imaging features which are imperceptible to the human eye (2). In other words, machine learning uses data to improve performance at an intended task without explicit coding but requires human intervention to adjust errors. Implementation facilitates prediction, modeling, and other related relatively simple tasks.

Deep learning takes this further, allowing the AI to adjust errors on its own, without direct human intervention, allowing for complex behavior-like performances. The most prevalent DL technique in imaging uses convolutional neural networks (CNNs) (3). CNNs comprise collections of neurons that are organized in a series of integrated layers. CNNs are dynamic learning systems which can emulate simpler patterns with lower layer depths, graduating to higher complexity with further layers (4). An input layer extracts information from the input array by multiplying with a filter. An array of smaller dimensions is produced, simplifying the input sequence. By simplifying a complex input into a smaller array, distinct feature maps of the original data are produced. The detected features are compared to the desired outputs, and the filter can be adjusted accordingly (5,6).

The aim of this paper is to delineate the current state of AI in radiology. The uses of AI in radiology are highly diverse: DL-based radiomics tools have proven useful in the diagnosis and risk stratification of various cancers (7). Multiple studies in lung cancer have demonstrated accurate prediction of distant metastasis, disease recurrence, and overall survival based on radiomics data derived from ANN models (8-10). As the influx of imaging data overwhelms radiologists’ capacity to interpret, the need for a more efficient system has become paramount. McDonald et al. suggests that an average radiologist must interpret one image every 3 s in an 8-h workday to fulfill their duties (11). AI-based pre-screening of radiological images may not only improve efficiency, but also catch errors inevitably made in such high-pressure, high-volume settings.

As image acquisition hardware technology advances, the potential for increased image detail increases. However, limitations in image reconstruction software remain a bottleneck (12). Further, in multimodal fusion imaging applications, AI-based techniques may aid image registration. Currently, image registration is typically guided by landmark and edge-based measures which are sensitive to initializations, predefined similarity features and the reference image (13). These fixed, predefined criteria are less capable than more advanced DL methods which have greater capacity for registering images featuring complex tissue deformation while compensating for motion artifacts (14).

Owing to the endless potential application of AI in radiology, the number of AI-based proprietary, open source and experimental software and products in radiology has rapidly increased. From 2017 to 2021, the number of AI exhibitors at the yearly meeting of the Radiological Society of North America (RSNA) and European Congress of Radiology (ECR) has nearly quadrupled (15,16). The extent to which these algorithms have been validated varies widely, with highly limited evidence available for commercially available AI software. Thus, the aim of this study is to educate the reader on the applications of AI-based algorithms, their basic functioning mechanism, and the efficacy of various algorithms which may be encountered in clinical practice. We present this article in accordance with the Narrative Review reporting checklist (available at https://jmai.amegroups.com/article/view/10.21037/jmai-22-89/rc).

Methods

A literature search for peer-reviewed publications in five databases including MEDLINE, Google Scholar, ACM Digital Library, IEEE Xplore, and PubMed was performed (Table 1). These databases were chosen to yield a comprehensive selection of peer-reviewed articles covering all aspects of the topic including various subspecialties in medicine and surgery as well as computer science and engineering. All articles older than 5 years were filtered out from the search. The article lists were generated by querying for the following relevant keywords, “artificial intelligence”, “machine learning”, “deep learning”, “convolutional neural networks”, “radiology”, “CT image reconstruction”, “AI image interpretation”, “AI in radiology”, “automated segmentation”, and “AI-assisted radiology”. One reviewer, AG, evaluated the resulting 205 articles for inclusion in this study. Non-English and non-peer-reviewed articles were excluded from this analysis. Further, papers concerning applications in radiation oncology were excluded.

Results

All the results included below are tabulated in Table 2.

Image reconstruction and optimization

There exists a schism in radiological technology between the hardware that captures the raw data and the software employed to process these data into the images which are examined by the radiologist. Furthermore, the advent of new low dose computed tomography (CT) protocols and low-field magnetic resonance imaging (MRI) has led to lower-quality studies compared to conventional imaging methods. In the last 30 years, CT reconstruction algorithms have remained relatively unchanged (84). New reconstruction algorithms employing deep learning techniques have shown promise in improving overall image quality, reducing artifactual distortion, reducing patient dose, and reducing runtime.

CT image reconstruction and denoising

Many of the articles included in this systematic review focus on the role of ML in reducing image noise with the end goal of improving diagnostic accuracy in various radiological modalities. Due to the recent increasing concern over the stochastic risks of CT scans, there has been a push for employing low dose techniques in CT imaging. AI-driven techniques have demonstrated value in enhancing imaging quality from low dose CT studies.

Liu and Tang demonstrated the efficacy of an expectation-maximization denoising algorithm (EM algorithm) in the detection and diagnosis of renal dysplasia. The underlying pathogenesis of renal dysplasia is related to defects in the formation of kidneys during their embryonic development. Although seemingly rare, the incidence of neonatal congenital renal dysplasia can be up to 0.2% of the population. Since renal dysplasia carries a high likelihood of progression to chronic kidney disease, the early diagnosis and treatment of this condition is paramount. Ultrasound is less sensitive than CT in the diagnosis of renal pathology. To minimize CT doses, denoising reconstructive techniques are employed. Liu and Tang’s work showed superior denoising effect of the EM algorithm compared to conventional denoising techniques. The peak signal to noise ratio (PSNR) of the EM algorithm was higher than the conventional techniques with a shorter runtime (22).

The advent of AI-driven CT reconstructive algorithms has spurred the clinical use of commercially available algorithms such as the iDose4 reconstruction algorithm (Philips Healthcare, Cleveland, OH). Zhang et al.’s work found superior signal-to-noise ratio (SNR) and carrier-to-noise ratio (CNR) using the iDose4 algorithm compared to conventional filtered back projection (FBP) techniques. The iDose4 reconstruction algorithm allowed for greater diagnostic accuracy with simultaneous reduced tube voltage. However, Zhang et al.’s work primarily centered around trauma patients recovering from anterior cruciate ligament injury, and may not be generalizable to all CT imaging applications (23). Further work by Fan et al. demonstrates the usefulness of an active contour segmentation algorithm in the evaluation of treatment response in patients with lumbar disc herniation (LDH). The authors demonstrated superior efficacy of the AI algorithm compared to traditional methods (25).

Jiang et al.’s work with ulcerative colitis (UC) and Crohn’s disease (CD) patients demonstrated significantly greater noise reduction by using edge-aware weighting factors in guided image filtering (GIF) algorithms. Compared to standard GIF, weighted GIF (WGIF), and gradient domain GIF (GGIF), the AI-modified GIF produced better results with optimized diagnostic capability in detecting abnormalities in the intestinal wall and intraluminal lesions associated with UC and CD (28).

Applications in MRI reconstruction

DL algorithms have demonstrated varying usefulness in the post processing of MRI studies. Harper et al. found that while DL enhancement can substantially increase the contrast-to-noise in reduced-quality images obtained from low-field MRI technology, there was an increased risk of introducing structural errors creating the risk of incorrect clinical interpretations (30). However, Jayachandran et al.’s work found that deep CNNs (dCNNs) are useful for applying synthetic contrast to MRIs performed without IV contrast. Studies have shown gadolinium-based contrast agents (GBCAs) accumulate in the brain from repeated contrast administration. Despite the unknown clinical significance of GBCA accumulation, avoiding contrast administration may be beneficial, especially for patients with contrast allergy or impaired renal function. Although segmentation of contrast-enhancing tumors from synthetic post-contrast T1-weighted sequences underestimated the tumor’s true volume, there was no difference in patients’ predicted overall survival (48).

dCNNs may also increase the accuracy and reduce computational time for single-shot fast spin echo (SSFSE) imaging reconstruction. Chen et al. compared the image quality of a dCNN-driven reconstruction approach compared to conventional techniques. There was an average 2.1-fold increase in computation speed with corresponding reduced perceived noise level with the data-driven self-calibration and reconstruction approach (64).

Image interpretation

Applications in MRI

ML has proven useful in interpreting magnetic resonance scans of the head. Applications in neuroradiology include non-invasive prediction of intracranial pressure (52), automated detection of unruptured intracranial aneurysms (40), DL-driven black blood (BB) imaging for brain metastasis detection (78), and diagnosis of ischemic penumbra (80).

Non-neuroradiology applications

Cardiovascular MR perfusion mapping

Other applications of DL in the MR modality include deducing risk of adverse cardiovascular outcomes from cardiovascular MR perfusion mapping. An existing commercially available software known as CV142 (Circle cardiovascular imaging, Calgary, AB, Canada) has shown to accurately predict myocardial blood flow and myocardial perfusion reserve—indicators of cardiovascular risk (21).

Endometriosis detection

In gynecology, Jiang et al. found greater sensitivity and specificity for the diagnosis of ovarian endometriosis with an AI-driven fuzzy C-means clustering algorithm used for pattern recognition compared to a conventional hard C-means (HCM) approach (27).

Prostate cancer

AI found further success in predicting pathological data from radiomic features in Toivonen et al.’s work on diffuse-weighted imaging texture feature analysis for the prediction of Gleason score in prostate cancer patients (74). Further work in prostate cancer by Ginsburg et al. showed the benefit of a zone-aware classifier for accurate detection of cancerous lesions in the peripheral zone. Logistic regression classifiers found distinct radiomic features associated with prostate cancer in the transitional zone versus the peripheral zone (79).

Neuroradiology applications

Glioma grading

The random forest ML algorithm can train on image data to effectively estimate local glioma grade. Gates et al. demonstrated accurate prediction of pathology data from imaging input using these predictive models, with up to 96% accuracy in tumor grade prediction (36).

Parotid lesion differentiation

Song et al. employed the support vector machine (SVM) ML algorithm, which was more capable of differentiating parotid pleomorphic adenoma from parotid adenolymphoma than traditional pathological and physical diagnostic methods (43).

Intracranial pressure prediction

Intracranial pressure (ICP) prediction through Levenberg-Marquardt and general global optimization methods demonstrated remarkable accuracy for predicting ICP—0.899 in the training cohort (n=97) and 0.861 in the independent validation cohort (n=41) (52).

Aneurysm detection

Joo et al.’s work employed a DL model for detection of unruptured intracranial aneurysms on MRA imaging with sensitivity of 87% and specificity of 92%. Using the open-source 3D ResNet ML framework, a DL model with diagnostic performance comparable to that of a human radiologist was trained and validated. Out of 168 aneurysms, 13 aneurysms from 12 examinations were missed in total (40).

Brain metastases detection

Jun et al. developed a “deep-learned” 3D BB imaging with corresponding 3D CNN for brain metastases detection without conducting an additional BB scan. The figure of merits, corresponding to the diagnostic performance of radiologists, were 0.9708 with DL-derived BB scan and 0.9437 with conventional BB imaging. There was no significant difference in diagnostic performance between the methods, suggesting that DL 3D BB imaging is effective for brain metastasis detection (78).

Applications in CT

This systematic review found several applications of ML in the interpretation of CT scans for the diagnosis and management of cancer patients. Additional uses included intracranial hemorrhage detection and cardiac evaluation.

Lung cancer

Chauvie et al. found greater positive predictive value in lung cancer detection with AI compared to the Lung CT Screening Reporting and Data Systems (lung-RADS) classification system (24). The transfer learning (TL) features were extracted using the widely available CNN model, LungTrans, which was pretrained on non-small cell lung cancer images. The fused DL-TL model achieving a 40% increase in area under the curve (AUC) compared to traditional feature fusion and reduction methods (39). Further work by Eresen et al. showed the superior performance of an ML-derived classification model compared to a clinical model in the diagnosis of lymph node metastasis (61).

Pancreatic cancer

Zhang et al.’s work focused on a prototype algorithm featuring radiomics combined with transfer learning feature detection for pancreatic ductal adenocarcinoma (PDAC) detection (39).

Cardiac sarcoidosis

Other benefits of a ML approach included the differentiation of cardiac sarcoidosis (CS) and non-CS using polar maps through use of the ReliefF algorithm (68).

Cardiac CTA

Dey et al. and Kurata et al. reported greater effectiveness of an ML-based approach in the evaluation of coronary computed tomographic angiography (CTA). Dey demonstrated improved prediction of lesion-specific ischemia through an ML-driven feature selection model built from quantitative CTA compared to methods using plaque measurement or clinical assessments of pre-test likelihood of coronary artery disease (CAD) (73). A commercially available prototype, cFFR version 3.0.0 (Siemens Healthcare) provided good diagnostic performance for detecting hemodynamically significant CAD (76).

Intracranial hemorrhage (ICH) detection

ML-driven algorithms found additional success in neuroradiological applications including the detection of extra-axial hematomas in cranial CT in the emergency trauma setting, detection of intracranial hemorrhage (ICH) detection, and prognostication of hypertensive ICH (26,50,81). Kundisch et al. found 12.2% increased ICH detection when human expertise was aided by the commercially available AIDOC (Tel Aviv, Israel) detection software (26).

Clinical decision making

The applications of AI in clinical decision making and prognostication are diverse with 15 articles covering oncology, 5 in neurology, and 2 in cardiology. Other relevant specialties in this topic included ophthalmology, urology, nephrology, and pulmonology.

In oncology, many authors focused on the use of ML algorithms in predicting tumor distribution for preoperative analysis (18,33,38,42,60,61,69,82,83). Other oncological papers used AI to predict prognosis, specifically time-to-event and mortality quantification (38,57). In the cardiology space, Jonas et al. found that quantitative coronary CTA identifies unique atherosclerotic plaque characteristics for personalized prevention and treatment of atherosclerotic cardiovascular disease (19). Further work by Min et al. showed successful use of DL algorithm to predict incomplete stent expansion to guide treatment decisions to avoid preventable stent failures (45). In ophthalmology, Schmidt-Erfurth et al. used validated, fully automated computational image analysis to extract biomarkers from optical coherence tomography (OCT) for accurate prediction of best-corrected visual acuity outcomes (63). ML-based prediction of recurrent urinary tract infections (UTIs) in pediatric patients to guide decision for voiding cystourethrogram (VCUG) is possible (62). Chen et al. built a newly improved wavelet transform algorithm for CT evaluation of chronic kidney disease (CKD) to guide nursing management (41). Lastly, Kim et al. developed a ML-driven quantitative CT score as a secondary efficacy endpoint for the treatment of idiopathic pulmonary fibrosis (IPF) (44). All authors employed a combination of open source, proprietary, and novel AI algorithms for data processing.

Conclusions

Our review highlights recent AI-based algorithms applied MRI and CT and their application within various medical specialties. Many of the articles in this review demonstrated preliminary success of AI-based algorithms. Many studies concluded that their findings have the potential to improve radiology by improving image quality through image reconstruction, providing accurate image interpretation, and evaluating characteristics in studies to aid in clinical decision making.

It is important to note that the use of AI in radiology for detection of pathology is not a new concept. For example, the Food and Drug Administration (FDA) approved the first computer aided detection (CAD) application for breast mammograms in 1998. Nowadays, most breast cancer screening mammograms are interpreted by radiologists who use CAD assistance (85). The majority of current AI applications primarily complete perception and reasoning tasks that include, but are not limited to, quantification of features, detecting suspicious areas, and diagnosing and classifying abnormalities (1).

Although AI in radiology presents itself as an avenue for improvement of patient care, there remains limitations and concerns regarding its use. One prominent limitation in the use of deep learning methods is the black box problem, in which AI applications are unable to explain their results. In comparison, radiologists are able to provide thorough explanations for their interpretations (86). Other limitations include the ability of most AI applications to target only one anatomic region while some applications have difficulty integrating into an existing workflow, which might compromise productivity (85,87). One concern is the potential use of improper data that might incorporate biases into the algorithm. For example, data with evident gender and racial biases may be used to create a biased algorithm. In addition, inadequate data pools can fail to provide enough variation to train and validate AI algorithms, which could lead to suboptimal detection and diagnosis of pathologies (88).

The advancement in the development of AI in radiology is promising in its ability to contribute to improved patient care through improved radiologist efficiency and accuracy. Future opportunities of AI in radiology include improving accuracy of classifiers to identify and flag irregularities with minimal radiologist oversight. Such irregularities may include abnormal chest radiograph, detection of intracranial hemorrhage, or low-quality images. Detection and flagging of imaging requiring urgent care would alert the radiologist and possible reduce the time from diagnosis to treatment, thereby providing faster care to patients (86). It is also predicted that deep learning may help with routine tasks, giving the radiologist more time to generate final decisions regarding imaging studies (89). Furthermore, if medical facilities around the world develop strategies to share databases that are absent of patient identifiers, then deep learning models would have a larger dataset with more variety that would provide a more robust better training, while minimizing the risk of introducing biases to the algorithm. As previously mentioned, there remains apprehension regarding the implementation of AI in radiology. Moving forward, collaboration between radiologists and developers should be encouraged to address the concerns and limitations regarding the realistic use of AI in radiology.

Acknowledgments

Funding: None.

Footnote

Reporting Checklist: The authors have completed the Narrative Review reporting checklist. Available at https://jmai.amegroups.com/article/view/10.21037/jmai-22-89/rc

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jmai.amegroups.com/article/view/10.21037/jmai-22-89/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

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