Chest CT is emerging as a valuable diagnostic tool for clinical management of COVID-19 associated lung disease. Artificial intelligence (AI) has the potential to aid in rapid evaluation of CT scans for differentiation of COVID-19 findings from other clinical entities. Here we show that a series of deep learning algorithms, trained in a diverse multinational cohort of 1280 patients to localize parietal pleura/lung parenchyma followed by classification of COVID-19 pneumonia, can achieve up to 90.8% accuracy, with 84% sensitivity and 93% specificity, as evaluated in an independent test set (not included in training and validation) of 1337 patients. Normal controls included chest CTs from oncology, emergency, and pneumonia-related indications. The false positive rate in 140 patients with laboratory confirmed other (non COVID-19) pneumonias was 10%. AI-based algorithms can readily identify CT scans with COVID-19 associated pneumonia, as well as distinguish non-COVID related pneumonias with high specificity in diverse patient populations.

MRI grading for extraprostatic extension combined with clinical variables (prostate-specific antigen, Gleason score, and digital rectal examination) showed the highest diagnostic performance for pr... PurposeTo evaluate MRI features associated with pathologically defined extraprostatic extension (EPE) of prostate cancer and to propose an MRI grading system for pathologic EPE.Materials and Method...

The future of multimodal artificial intelligence models for integrating imaging and clinical metadata: a narrative review - Diagnostic and Interventional Radiology

In recent years, artificial intelligence (AI) has achieved substantial progress in medical imaging field where clinical decisions often rely on imaging data, e.g., radiology, pathology, dermatology, and ophthalmology.¹ Within radiology, AI has shown promising results in quantitative interpretation of certain radiological tasks such as classification (diagnosis), segmentation, and quantification (severity analysis). It is usually a time-intensive, error-prone, and non-reproducible procedure when a radiologist evaluates scans visually to report findings and make diagnostic decisions.² Alternatively, AI algorithms outperform the conventional qualitative approaches with faster pattern recognition, quantitative assessments, and improved reproducibility.^{1 – 4} More specifically, the advent of deep neural networks along with the recent extensive computational capacity enabled AI to stand out by learning complex nonlinear relationships in complicated radiology problems. As a result, deep learning-based AI has met and even surpassed human-level performance in certain tasks. However, training these usually large models, requires massive amounts of data, which can be limited in medical imaging applications due to the concerns over data privacy as well as the paucity of annotation (labels) in supervised learning. With a continuing trend for developing universal data anonymization protocols in addition to open data sharing policies, larger clinical datasets have started to become available. Thus, training on massive dataset composed of different sources (institutions with different scanners, image qualities, and standards) is an ongoing potential pathway. Nonetheless, in cases with inevitable limited data, possible solutions are to use simpler designs or to employ transfer learning strategies based on giant datasets of clinical or natural images.⁵ Either scenario, training on a big dataset or adopting transfer learning, are only two other important reasons that resonate the demand for a series of preliminary pre-processing steps prior to training; however, the nature of radiology images intrinsically necessities the preprocessing phase by itself.

Diagnostic and Interventional Radiology