Machine Learning for the Interventional Radiologist

Meek, Ryan D.; Lungren, Matthew P.; Gichoya, Judy Wawira

doi:10.2214/ajr.19.21527

Cited by 21 publications

(17 citation statements)

References 5 publications

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“…Machine learning combined with augmented reality systems could give trainees a new platform for honing their procedural skills, leading to novel ways of assessing performance and improving their competency [37]. In particular, AR and VR applications have been shown to improve upon current training methods in motivation, interactivity and learning of material; advanced 3D rendering and manipulation of imaging in space allows trainees to conceptualize anatomy and to improve procedural skills in a simulated environment, with no risk to patients.…”

Section: Training and Educationmentioning

confidence: 99%

Interventional Radiology ex-machina: impact of Artificial Intelligence on practice

et al. 2021

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Artificial intelligence (AI) is a branch of Informatics that uses algorithms to tirelessly process data, understand its meaning and provide the desired outcome, continuously redefining its logic. AI was mainly introduced via artificial neural networks, developed in the early 1950s, and with its evolution into "computational learning models." Machine Learning analyzes and extracts features in larger data after exposure to examples; Deep Learning uses neural networks in order to extract meaningful patterns from imaging data, even deciphering that which would otherwise be beyond human perception. Thus, AI has the potential to revolutionize the healthcare systems and clinical practice of doctors all over the world. This is especially true for radiologists, who are integral to diagnostic medicine, helping to customize treatments and triage resources with maximum effectiveness. Related in spirit to Artificial intelligence are Augmented Reality, mixed reality, or Virtual Reality, which are able to enhance accuracy of minimally invasive treatments in image guided therapies by Interventional Radiologists. The potential applications of AI in IR go beyond computer vision and diagnosis, to include screening and modeling of patient selection, predictive tools for treatment planning and navigation, and training tools. Although no new technology is widely embraced, AI may provide opportunities to enhance radiology service and improve patient care, if studied, validated, and applied appropriately.

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Section: Training and Educationmentioning

confidence: 99%

Interventional Radiology ex-machina: impact of Artificial Intelligence on practice

et al. 2021

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“…55 The use of ML in robotics and augmented reality systems has also been studied for use in trainee education, and has been proposed for future use in IR. 13 For example, researchers in urology have utilized ML to process procedural data in robot-assisted radical prostatectomy to evaluate performance and predict outcomes. 57 This study collected automated performance metrics (obtained from the surgical robot system) such as camera idle time, dominant instrument moving time, and camera moving time.…”

Section: Future Directions and Conclusionmentioning

confidence: 99%

“…These studies (including review articles) primarily focus on optimizing patient identification and selecting prognostic predictors of IR procedure's efficacy for treating cancers. [8][9][10][11][12][13] Research in AI has transformed the prevailing paradigm by using large datasets to uncover hidden associations and build predictive models that would be very difficult to do via the traditional technique of conducting prospective clinical trials. While clinicians are often familiar with the intricacies of logistic regression and hazard ratios, AI research has inherent complexity and specialized nomenclature that makes it difficult for many clinicians to critically analyze AI literature.…”

mentioning

confidence: 99%

Use of Artificial Intelligence in Nononcologic Interventional Radiology: Current State and Future Directions

Malpani

Petty

Bhatt

et al. 2021

Digestive Disease Interventions

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The future of radiology is disproportionately linked to the applications of artificial intelligence (AI). Recent exponential advancements in AI are already beginning to augment the clinical practice of radiology. Driven by a paucity of review articles in the area, this article aims to discuss applications of AI in nononcologic IR across procedural planning, execution, and follow-up along with a discussion on the future directions of the field. Applications in vascular imaging, radiomics, touchless software interactions, robotics, natural language processing, postprocedural outcome prediction, device navigation, and image acquisition are included. Familiarity with AI study analysis will help open the current “black box” of AI research and help bridge the gap between the research laboratory and clinical practice.

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“…In the field of hepatology, ANNs were superior in predicting mortality of patients with end‐stage liver disease compared to model for end‐stage liver disease (MELD) as well as in predicting HCC tumour grade and microvascular invasion compared to a conventional linear model . Recently, Meek et al suggested stronger implementation of such techniques in interventional oncology . Yet, only very few studies have used similar approaches in patients with HCC in the setting of TACE.…”

Section: Introductionmentioning

confidence: 99%

“…12,13 Recently, Meek et al suggested stronger implementation of such techniques in interventional oncology. 14 To the best of our knowledge, no attempt has yet been made to develop a survival prediction model for patients with HCC undergoing TACE using neural networks. Therefore, the purpose of this study was to implement such a novel approach for treatment stratification and to compare it to conventional prediction scores.…”

Section: Introductionmentioning

confidence: 99%

Predicting survival after transarterial chemoembolization for hepatocellular carcinoma using a neural network: A Pilot Study

Mähringer‐Kunz

Wagner

Hähn

et al. 2020

Liver International

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Background and aims Deciding when to repeat and when to stop transarterial chemoembolization (TACE) in patients with hepatocellular carcinoma (HCC) can be difficult even for experienced investigators. Our aim was to develop a survival prediction model for such patients undergoing TACE using novel machine learning algorithms and to compare it to conventional prediction scores, ART, ABCR and SNACOR. Methods For this retrospective analysis, 282 patients who underwent TACE for HCC at our tertiary referral centre between January 2005 and December 2017 were included in the final analysis. We built an artificial neural network (ANN) including all parameters used by the aforementioned risk scores and other clinically meaningful parameters. Following an 80:20 split, the first 225 patients were used for training; the more recently treated 20% were used for validation. Results The ANN had a promising performance at predicting 1‐year survival, with an area under the ROC curve (AUC) of 0.77 ± 0.13. Internal validation yielded an AUC of 0.83 ± 0.06, a positive predictive value of 87.5% and a negative predictive value of 68.0%. The sensitivity was 77.8% and specificity 81.0%. In a head‐to‐head comparison, the ANN outperformed the aforementioned scoring systems, which yielded lower AUCs (SNACOR 0.73 ± 0.07, ABCR 0.70 ± 0.07 and ART 0.54 ± 0.08). This difference reached significance for ART (P < .001); for ABCR and SNACOR significance was not reached (P = .143 and P = .201). Conclusions Artificial neural networks could be better at predicting patient survival after TACE for HCC than traditional scoring systems. Once established, such prediction models could easily be deployed in clinical routine and help determine optimal patient care.

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Machine Learning for the Interventional Radiologist

Cited by 21 publications

References 5 publications

Interventional Radiology ex-machina: impact of Artificial Intelligence on practice

Interventional Radiology ex-machina: impact of Artificial Intelligence on practice

Use of Artificial Intelligence in Nononcologic Interventional Radiology: Current State and Future Directions

Predicting survival after transarterial chemoembolization for hepatocellular carcinoma using a neural network: A Pilot Study

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