AI in Cardiopulmonary Imaging; A Review of Deep Learning Developments

The partnership between radiology and artificial intelligence (AI) has been developing for some time. The availability of increasingly powerful deep learning algorithms and computer hardware, along with accessible databases, is driving this partnership. Potential applications for organ-specific imaging analyses are increasing daily. Thus, it is not surprising to see AI and deep learning applied to cardiopulmonary imaging [1].

Pulmonary AI applications include the identity and characterizations of pulmonary nodules, characterization of lung malignancies, identification of possible pneumonia, and the detection and quantification of obstructive lung disease and emphysema.

It is highly likely that future radiologists will benefit from the automatic detection, measurement, characterization, comparison, and recording of pulmonary nodules on chest CT scans. Presumably with the help of deep learning, computer-assisted nodule detection programs will reach acceptable reliability levels. The addition of automatic texture analysis might enhance Fleischner Society guidelines [2] for more effective radiology reports with specific clinical follow-up recommendations.

Although considerable effort has been placed on radiomic analysis of pulmonary malignancies for potential tissue genotype prediction, findings of image-based statistical correlation with specific tumor genes are unlikely to compete with biopsy-confirmed results. It will be interesting to see if a clinical role develops from the radiomic analysis of lung cancers [3].

Can AI methods be used for detecting pulmonary opacities likely to represent pneumonia? This was the basis for the Radiological Society of North America’s (RSNA) Pneumonia Detection Challenge, where more than 1400 teams from around the world participated. With 346 teams submitting results during the evaluation phase, the finalists interrogated a training set of 25,684 radiographs and a test set of 1000 radiographs, where 5659 of the training set images were reported to have pneumonia by a panel of nonthoracic radiologists [4]. The goal was to place bounding boxes around appropriate pulmonary opacities as accurately as possible. Successful competitors created training models and selected methods for optimal performance with a sensitivity approaching 90%.

The advancements demonstrated at RSNA’s Pneumonia Detection Challenge revealed that we are on the path to automatic detection of suspicious pulmonary opacities. One potential clinical role for such an advancement will be in prescreening and prioritizing chest radiographs. This would allow for earlier communication of possible pneumonia to appropriate practitioners and patients with a report that includes annotated imaging [4].

It is remarkable that ordinary chest CT images may be analyzed for air trapping by locating and summing appropriate voxels with attenuations of less than –940 HU. Apparently, this could be performed automatically and now more efficiently with deep learning methods that might be able to locate and quantitate findings of emphysema directly to the CT report [4].

The best example of a clinically useful application of AI in cardiac imaging is the success of Tao and colleagues in developing a deep learning–based approach to the automatic ROI selection and analysis of left ventricular volumes and ejection fraction, as measured from routine cardiac MRI [5]. Though it is fairly easy to manually perform this task, typically 10 minutes of operator time is required to outline all of the appropriate ROIs. Tao’s deep learning–trained system reliably performs this task automatically in a fraction of a second [5, 6]. This robust cardiac MR quantitation program is now available for workstation application for use with any MR system.

CT-based fractional flow reserve (FFR) uses computational fluid dynamics to quantify coronary artery stenosis. Physics-based models can noninvasively estimate FFR from patient-specific CT attenuation values. CT FFR analysis is a complex iterative process with high computational demand. CT FFR processing is particularly slow when performed on many existing radiology workstations. Computation time using deep learning–prepared programs solve CT FFR computation flow analysis in 20% of the time required by standard computational systems [7, 8]. Thus, with a deep learning–trained computer system, CT FFR calculations can be available in a fraction of the time required by current software analysis using typical workstations. It is predictable that the combination of faster computer systems coupled with deep learning–trained software will allow for all patients undergoing coronary CT angiography to benefit from efficient FFR processing and automatic incorporation of results into the radiology report.

As the partnership between radiology and AI continues to advance daily, both the patient and the radiologist stand to benefit. The integration of AI and deep learning into practice will lead to more efficient, effective, and valuable quantitative cardiopulmonary radiology reporting.