Augmented Radiologist Workflow Improves Report Value and Saves Time: A Potential Model for Implementation of Artificial Intelligence

M., Huy; Spear, Lillian G; Nikpanah, Moozhan; Mirmomen, S. Mojdeh; Machado, Laura B.; Toscano, Alexandra P.; Türkbey, Barış; Bagheri, Mohammad Hadi; Gulley, James L.; Folio, Les

doi:10.1016/j.acra.2019.09.014

Cited by 50 publications

(27 citation statements)

References 22 publications

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“…Furthermore, variations in NM physicians' recognition of and the lack of defined standardized features on the images also contribute to variability in thyroid disease diagnosis. Machine learning will be an effective solution to the shortage of medical resources, 21 especially with regard to automatic diagnosis of thyroid scintigraphy studies. [22][23][24][25] The principal purpose of this paper is to describe the design of classifier models using different DL algorithms for the diagnosis of Graves' disease and subacute thyroiditis by thyroid scintigraphy.…”

Section: Discussionmentioning

confidence: 99%

Deep learning for intelligent diagnosis in thyroid scintigraphy

et al. 2021

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Objective To construct deep learning (DL) models to improve the accuracy and efficiency of thyroid disease diagnosis by thyroid scintigraphy. Methods We constructed DL models with AlexNet, VGGNet, and ResNet. The models were trained separately with transfer learning. We measured each model’s performance with six indicators: recall, precision, negative predictive value (NPV), specificity, accuracy, and F1-score. We also compared the diagnostic performances of first- and third-year nuclear medicine (NM) residents with assistance from the best-performing DL-based model. The Kappa coefficient and average classification time of each model were compared with those of two NM residents. Results The recall, precision, NPV, specificity, accuracy, and F1-score of the three models ranged from 73.33% to 97.00%. The Kappa coefficient of all three models was >0.710. All models performed better than the first-year NM resident but not as well as the third-year NM resident in terms of diagnostic ability. However, the ResNet model provided “diagnostic assistance” to the NM residents. The models provided results at speeds 400 to 600 times faster than the NM residents. Conclusion DL-based models perform well in diagnostic assessment by thyroid scintigraphy. These models may serve as tools for NM residents in the diagnosis of Graves’ disease and subacute thyroiditis.

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Section: Discussionmentioning

confidence: 99%

Deep learning for intelligent diagnosis in thyroid scintigraphy

et al. 2021

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“…Such annotations have been used effectively for labeling of open-source data sets (60). Measurements can be performed in advance of radiologists by radiology preprocessors that improve annotation quality while saving radiologists time (61). Preliminary work on prospective labeling is showing that twodiameter measurements and ovals are better than one-diameter measurements and much better than arrows (62).…”

Section: Ground Truth or Label Qualitymentioning

confidence: 99%

Preparing Medical Imaging Data for Machine Learning

Willemink¹,

Koszek²,

Hardell³

et al. 2020

Radiology

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Artificial intelligence (AI) continues to garner substantial interest in medical imaging. The potential applications are vast and include the entirety of the medical imaging life cycle from image creation to diagnosis to outcome prediction. The chief obstacles to development and clinical implementation of AI algorithms include availability of sufficiently large, curated, and representative training data that includes expert labeling (eg, annotations). Current supervised AI methods require a curation process for data to optimally train, validate, and test algorithms. Currently, most research groups and industry have limited data access based on small sample sizes from small geographic areas. In addition, the preparation of data is a costly and time-intensive process, the results of which are algorithms with limited utility and poor generalization. In this article, the authors describe fundamental steps for preparing medical imaging data in AI algorithm development, explain current limitations to data curation, and explore new approaches to address the problem of data availability.

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“…This can help expedite workflows, improve accuracy, and ultimately reduce overall costs. 3 Recent work has focused on chest radiography, the most common imaging examination conducted in emergency departments (EDs) and urgent care settings. Several studies have reported machine learning models achieving radiologist-level performance for different chest radiograph findings.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Chest Radiograph Interpretations by Artificial Intelligence Algorithm vs Radiology Residents

Wong

Gur

et al. 2020

JAMA Netw Open

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IMPORTANCE Chest radiography is the most common diagnostic imaging examination performed in emergency departments (EDs). Augmenting clinicians with automated preliminary read assistants could help expedite their workflows, improve accuracy, and reduce the cost of care. OBJECTIVE To assess the performance of artificial intelligence (AI) algorithms in realistic radiology workflows by performing an objective comparative evaluation of the preliminary reads of anteroposterior (AP) frontal chest radiographs performed by an AI algorithm and radiology residents. DESIGN, SETTING, AND PARTICIPANTS This diagnostic study included a set of 72 findings assembled by clinical experts to constitute a full-fledged preliminary read of AP frontal chest radiographs. A novel deep learning architecture was designed for an AI algorithm to estimate the findings per image. The AI algorithm was trained using a multihospital training data set of 342 126 frontal chest radiographs captured in ED and urgent care settings. The training data were labeled from their associated reports. Image-based F1 score was chosen to optimize the operating point on the receiver operating characteristics (ROC) curve so as to minimize the number of missed findings and overcalls per image read. The performance of the model was compared with that of 5 radiology residents recruited from multiple institutions in the US in an objective study in which a separate data set of 1998 AP frontal chest radiographs was drawn from a hospital source representative of realistic preliminary reads in inpatient and ED settings. A triple consensus with adjudication process was used to derive the ground truth labels for the study data set. The performance of AI algorithm and radiology residents was assessed by comparing their reads with ground truth findings. All studies were conducted through a web-based clinical study application system. The triple consensus data set

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Augmented Radiologist Workflow Improves Report Value and Saves Time: A Potential Model for Implementation of Artificial Intelligence

Cited by 50 publications

References 22 publications

Deep learning for intelligent diagnosis in thyroid scintigraphy

Deep learning for intelligent diagnosis in thyroid scintigraphy

Preparing Medical Imaging Data for Machine Learning

Comparison of Chest Radiograph Interpretations by Artificial Intelligence Algorithm vs Radiology Residents

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