While conventional in-office phototherapy has long been utilized as a successful treatment for atopic dermatitis (AD), it is associated with potential barriers including inconvenience, poor adherence, time and financial expense. In this retrospective study, we examine the efficacy, adherence, and patient-satisfaction of using adjunctive at-home, self-administered phototherapy utilizing a novel handheld narrow-band ultraviolet B (NB-UVB) device for the treatment of refractory mild to severe AD. Included AD patients were initially trained on proper use of the device. These patients treated involved areas three times per week for a period of 12 weeks. Phototherapy dosing protocol was based on skin type. The cohort included 52 patients, who were aged 20–69 and represented all skin types. They were initially categorized by disease involvement as mild, moderate, and severe. Patients were also queried to self-score their disease severity and level of satisfaction. Compared to baseline, at 12 weeks, 48

Coronary computed tomography angiography (cCTA) is an evolving, noninvasive first-line strategy for the evaluation of suspected coronary artery disease (CAD). A technique named computed tomography angiography-derived fractional flow reserve (CT-FFR) is an alternative method for detecting hemodynamically significant coronary stenosis, based in coronary geometries extracted from conventional CT images. Recent studies have shown that CT-FFR can be interpreted in the same way as the invasive FFR, which is the gold standard of coronary hemodynamics assessment. In recent years, artificial intelligence (AI) and, in particular, the application of machine learning (ML) algorithms have been developed as a technical approach of CT-FFR for improved decision pathways, risk stratification, and outcome prediction in a more objective, reproducible, and rational manner. AI is based on computer science and mathematics depending on big data, high performance computational infrastructure, and applied algorithms. The application of ML in daily routine clinical practice may hold potential to improve imaging workflow and to promote better outcome prediction and more effective decision-making in patient management, like improved therapeutic guidance to efficiently justify the management of patients with suspected coronary artery disease.

Purpose of Review To summarize current artificial intelligence (AI)-based applications for coronary artery calcium scoring (CACS) and their potential clinical impact. Recent Findings Recent evolution of AI-based technologies in medical imaging has accelerated progress in CACS performed in diverse types of CT examinations, providing promising results for future clinical application in this field. CACS plays a key role in risk stratification of coronary artery disease (CAD) and patient management. Recent emergence of AI algorithms, particularly deep learning (DL)-based applications, have provided considerable progress in CACS. Many investigations have focused on the clinical role of DL models in CACS and showed excellent agreement between those algorithms and manual scoring, not only in dedicated coronary calcium CT but also in coronary CT angiography (CCTA), low-dose chest CT, and standard chest CT. Therefore, the potential of AI-based CACS may become more influential in the future.

BACKGROUND:On March 9th, 2020, the Italian government decided to go into lockdown due to the COVID-19 pandemic, which led to changes in the workflow of radiological examinations.AIMS:Aim of the study is to illustrate how the workload and outcome of radiological exams changed in a community hospital during the pandemic.METHODS AND MATERIAL:The exams performed in the radiology department from March 9th to March 29th, 2020 were retrospectively reviewed and compared to the exams conducted during the same time-period in 2019. Only exams coming from the emergency department (ED) were included. Two radiologists defined the cases as positive or negative findings, based on independent blind readings of the imaging studies. Categorical measurements are presented as frequency and percentages, and p-values are calculated using the Chi-squared test.RESULTS AND CONCLUSIONS:There was a significant reduction in the amount of exams performed in 2020: there were 143 (93|65% male, 60.7±21.5 years) patients who underwent radiological examinations from the ED vs. 485 (255|53% male, 51.2±24.8 years) in 2019. Furthermore, the total number of ED exams dropped from 699 (2019) to 215 (2020). However, the percentage of patients with a positive result was significantly higher in 2020 (69|48%) compared to 2019 (151|31%) (p<.001). The reduction of emergency radiological examinations might be a result of the movement restrictions enforced during the lockdown, and possible fear of the hospital as a contagious place. This translated to a relative increase of positive cases as only patients with very serious conditions were accessing the ED.

Coronary computed tomography angiography (cCTA) is a reliable and clinically proven method for the evaluation of coronary artery disease. cCTA data sets can be used to derive fractional flow reserve (FFR) as CT-FFR. This method has respectable results when compared in previous trials to invasive FFR, with the aim of detecting lesion-specific ischemia. Results from previous studies have shown many benefits, including improved therapeutic guidance to efficiently justify the management of patients with suspected coronary artery disease and enhanced outcomes and reduced health care costs. More recently, a technical approach to the calculation of CT-FFR using an artificial intelligence deep machine learning (ML) algorithm has been introduced. ML algorithms provide information in a more objective, reproducible, and rational manner and with improved diagnostic accuracy in comparison to cCTA. This review gives an overview of the technical background, clinical validation, and implementation of ML applications in CT-FFR.

Coronary computed tomographic angiography (CCTA) is an effective examination with high sensitivity to rule out obstructive coronary artery disease (CAD). It can provide anatomical information of coronary artery disease, such as the degree of stenosis, plaque characteristics, but has poor discriminatory power for hemodynamically significant lesions, which may lead to unnecessary referrals for invasive coronary angiography (ICA). Invasive fractional flow reserve (FFR) is generally considered as the gold standard for the functional evaluation of CAD. However, high cost and invasive characteristics of FFR limit its wide clinical application. Recently, non-invasive CT-derived fractional flow reserve (CT-FFR), a novel image post-processing technique combining the advantages of CCTA and FFR, allows us to obtain both anatomic and functional information of a coronary lesion. CT-FFR has been used in diagnosing and guiding clinical management of CAD patients. This review introduces the basic principles, key points of CT-FFR analysis, and current research progress of CT-FFR.

To evaluate the long-term prognostic value of coronary CT angiography (cCTA)-derived plaque measures and clinical parameters on major adverse cardiac events (MACE) using machine learning (ML). Datasets of 361 patients (61.9 ± 10.3 years, 65% male) with suspected coronary artery disease (CAD) who underwent cCTA were retrospectively analyzed. MACE was recorded. cCTA-derived adverse plaque features and conventional CT risk scores together with cardiovascular risk factors were provided to a ML model to predict MACE. A boosted ensemble algorithm (RUSBoost) utilizing decision trees as weak learners with repeated nested cross-validation to train and validate the model was used. Performance of the ML model was calculated using the area under the curve (AUC). MACE was observed in 31 patients (8.6%) after a median follow-up of 5.4 years. Discriminatory power was significantly higher for the ML model (AUC 0.96 [95%CI 0.93–0.98]) compared with conventional CT risk scores including Agatston calcium score (AUC 0.84 [95%CI 0.80–0.87]), segment involvement score (AUC 0.88 [95%CI 0.84–0.91]), and segment stenosis score (AUC 0.89 [95%CI 0.86–0.92], all p < 0.05). Similar results were shown for adverse plaque measures (AUCs 0.72–0.82, all p < 0.05) and clinical parameters including the Framingham risk score (AUCs 0.71–0.76, all p < 0.05). The ML model yielded significantly higher diagnostic performance compared with logistic regression analysis (AUC 0.96 vs. 0.92, p = 0.024). Integration of a ML model improves the long-term prediction of MACE when compared with conventional CT risk scores, adverse plaque measures, and clinical information. ML algorithms may improve the integration of patient’s information to enhance risk stratification. • A machine learning (ML) model portends high discriminatory power to predict major adverse cardiac events (MACE). • ML-based risk stratification shows superior diagnostic performance for MACE prediction over coronary CT angiography (cCTA)-derived risk scores or clinical parameters alone. • A ML model outperforms conventional logistic regression analysis for the prediction of MACE.

To review the current applications of artificial intelligence (AI) for coronary artery calcium scoring (CACS) and plaque analysis with their achievements and potential clinical impacts. Recent advances of AI-based technologies especially deep learning (DL) approaches in medical imaging have achieved substantial progress in automated detection and characterization of coronary atherosclerotic plaques, providing promising results for AI application in diagnosis and management of coronary artery disease. To date, many studies investigated the potential role of DL in CACS and showed promising results for clinical application in a variety of CT examinations, demonstrating excellent agreement compared with manual scoring. DL-based approaches have also provided considerable progress in automated analysis of non-calcified plaque. Many investigations have shown that automated localization and classification of non-calcified plaque and luminal stenosis is feasible in cardiac CT angiography, although still challenging.

Dual-Energy computed tomography (DECT) of the heart has become a widely used imaging technique following CT hardware and software advancements, particularly using scanners with greater detector and faster gantry rotation times. 1 Vliegenthart R. Pelgrim G.J. Ebersberger U. Rowe G.W. Oudkerk M. Schoepf U.J. Dual-energy CT of the heart. AJR Am J Roentgenol. 2012; 199: S54-63 Crossref PubMed Google Scholar Each of the major vendors currently provide the ability for acquiring DECT data (also termed spectral or multi-energy CT). In general, DECT uses dual image acquisition at different kilovoltage levels of the same scan volume to facilitate material separation. Based on that principle, DECT provides additional image reconstructions that potentially allow for more comprehensive, accurate and robust CT analysis. However, only dual-layer spectral detector and dual-source DECT technologies allow for entirely, or almost completely simultaneous data acquisition and are therefore especially suitable for cardiovascular imaging. 2 De Cecco C.N. Schoepf U.J. Steinbach L. et al. White paper of the society of computed body tomography and magnetic resonance on dual-energy CT, Part 3: vascular, cardiac, pulmonary, and musculoskeletal applications. J Comput Assist Tomogr. 2017; 41: 1-7 Crossref PubMed Scopus (23) Google Scholar

HomeRadiology: Cardiothoracic ImagingVol. 1, No. 4 PreviousNext CommentaryFree AccessCT Angiography–derived Fractional Flow Reserve: The Global Game of ThronesU. Joseph Schoepf , Hunter N. Gray, Christian TescheU. Joseph Schoepf , Hunter N. Gray, Christian TescheAuthor AffiliationsFrom the Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, 25 Courtenay Dr, Charleston, SC 29425-2260.Address correspondence to U.J.S. (e-mail: [email protected]).U. Joseph Schoepf Hunter N. GrayChristian TeschePublished Online:Oct 31 2019https://doi.org/10.1148/ryct.2019190197MoreSectionsPDF ToolsImage ViewerAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked InEmail See also the article by van Hamersvelt et al in this issue.Joe Schoepf, MD, FACR, FAHA, FNASCI, FSCBT-MR, FSCCT, is a professor with appointments in radiology, medicine, and pediatrics at the Medical University of South Carolina (MUSC) in Charleston. At MUSC, Dr Schoepf serves as the director of the division of cardiovascular imaging and vice chair for research, as well as assistant dean for clinical research. His main scientific interest is the use of advanced CT, MRI, image postprocessing, and artificial intelligence techniques for diagnosing disorders of the heart and lung.Download as PowerPointOpen in Image Viewer Hunter Gray, BS, is a member of Dr Joseph Schoepf’s research group at the Medical University of South Carolina, department of radiology, and serves as a coordinator and researcher.Download as PowerPointOpen in Image Viewer Christian Tesche, MD, is an assistant professor in cardiology at the Heart Center Munich-Bogenhausen in Munich, Germany. He serves as the head of cardiovascular imaging and his main scientific interest is the use of CT and artificial intelligence algorithms for optimizing public health by integration of technical innovations into streamlined clinical workflows for improved patient care.Download as PowerPointOpen in Image Viewer The mere anatomic evaluation of coronary stenosis with invasive conventional angiography or coronary CT angiography is an insufficient means to steer patient management toward or away from revascularization. This explains the vast global interest in functional approaches based on coronary CT angiography, which may yield a more relevant noninvasive diagnosis of coronary artery disease (CAD). Fractional flow reserve (FFR) derived from standard coronary CT angiographic data sets (CT FFR) has been validated in previous trials as a reliable method for the noninvasive detection of lesion-specific ischemia in comparison to invasive FFR (1–4). Previous results have shown improved therapeutic guidance to streamline and rationalize the management of patients suspected of having CAD and improved outcomes, while overall health care costs are reduced (5–7). Newer trials focusing on the clinical applicability of CT FFR in real-world scenarios have largely confirmed the evidence created on the use of CT FFR since its inception; the 1-year outcomes from the ADVANCE (Assessing Diagnostic Value of Noninvasive FFRCT in Coronary Care) registry (real-world clinical utility and impact on clinical decision-making of coronary CT angiography–derived FFR) show low rates of events in all patients, with less revascularization and a trend toward lower rate of major adverse cardiac events and significantly lower rates of cardiovascular death or myocardial infarction in patients with a negative result at CT FFR compared with patients with abnormal CT FFR values (8). CT FFR modified treatment recommendation in two-thirds of patients as compared with coronary CT angiography alone, was associated with fewer negative results at invasive angiography, predicted revascularization, and identified those at low risk of adverse events through 90 days (7). Gaps in knowledge and evidence still exist, such as large-scale comparative-effectiveness studies on the application of CT FFR to a broader patient population with low-to-intermediate risk and more rationalized guidance on the management of patients with lesions in the “gray-zone” FFR range of 0.75 to 0.80 (7,8). Practicing clinicians may also be confused when confronted with abnormal CT FFR values isolated to small-diameter distal segments in the most extreme portions of the coronary circulation; however, these should rarely lead to clinical uncertainties.Regardless of the areas that warrant additional study, the prospect of rationalized, noninvasive, safe, and cost-effective management of patients suspected of having CAD enabled by CT FFR has prompted health care carriers in the United States and in certain European and Asian economies to initiate health insurance coverage of CT FFR. So far, the evidence-creation toward the clinical implementation of CT FFR has been largely, if not exclusively, driven by a single vendor (HeartFlow; Redwood City, Calif), which holds the intellectual property rights behind the only solution available for clinical use, which has gained approval by the U.S. Food and Drug Administration (FDA). Some practitioners are voicing their wariness and discomfort with a single vendor providing this type of analysis in the health care market and decry the lack of competition; yet, it is important to note that such monopolies are far from unique, nor are they necessarily detrimental per se. In fact, there is a long history of highly disruptive technologies initially coming to market as single-vendor solutions, most notably drug-eluting stents, transcatheter aortic valves, and, more recently, transcatheter mitral valves.Because the prospect of replacing invasive FFR with a noninvasive method is so attractive and foreseeably becoming a reimbursable service, this application is fascinating researchers, developers, and industries around the world to propose their own technical approaches toward CT FFR. For instance, we, among others, were instrumental in the refinement and validation of deep machine learning–based, artificial intelligence solutions of this clinical problem (3,9).The investigation led by van Hamersvelt et al (10), which is featured in this issue of Radiology: Cardiothoracic Imaging, is the latest in a series of proposals toward the development of algorithms to identify lesion-specific ischemia at coronary CT angiography. In this single-center study, a total of 57 patients with 77 vessels were included. For CT FFR analysis, an on-site CT FFR algorithm based on patient-specific lumped parameter models was tested (FFR CT, IntelliSpace Portal; Philips Healthcare, Cambridge, Mass). This algorithm uses a lumped parameter model to represent the complete coronary artery tree with boundary conditions being represented as inflow and outflow resistors and are determined based on the ostium and outflow segment geometry. On a per-vessel level, the algorithm demonstrated an area under the curve (AUC) for CT FFR of 0.87 (95% confidence interval [CI]: 0.77, 0.94), which was superior to that of coronary CT angiography (AUC, 0.70; 95% CI: 0.58, 0.80). This is in line with previous investigations on different CT FFR algorithms, showing AUCs ranging from 0.87 to 0.93 for CT FFR (2, 3). Moreover, in the present study the performance of CT FFR in intermediate lesions (25%–69% stenosis) was assessed, demonstrating a high accuracy with an AUC of 0.89 (95% CI: 0.79, 0.95). Additionally, the diagnostic performance of CT FFR depending on the Agatston calcium score was investigated with no significant differences in the diagnostic performance between vessels with low Agatston score (0–100, accuracy 78%) and vessels with high Agatston score (≥101, accuracy 86%). However, authors stated that outliers with a large difference between CT FFR and invasive FFR (>20% difference) were found in vessels with Agatston score of 101 or greater, which has recently been validated in a multicenter study showing a significant impact of calcium burden on the diagnostic accuracy of CT FFR (11).By using this CT FFR algorithm based on patient-specific lumped parameter models, operating time was found to be 36 minutes per patient, which is comparable to prior studies showing computational times from 15 minutes up to 4 hours (2,9).The major limitation of the current study, as in many early CT FFR investigations, is the high rate of cases excluded due to poor image quality and inability to investigate ostial lesions (7% and 19%, respectively, in the present study). This limitation, together with the high prevalence of CAD in the study cohort, hampers the generalizability of the results to a broader patient population, as it does not truly reflect the real-world outpatient population with low-to-intermediate risk undergoing coronary CT angiography.Despite these limitations, the results are further testimony to the global excitement that surrounds the refinement and clinical use of CT FFR. The worldwide ingenuity and zeal to develop ever more effective and accurate CT FFR algorithms is certainly inspiring, healthy, and helpful for furthering this field. However, there can be little doubt that the current FDA-approved commercial solution based on computational fluid dynamics will dominate the clinical market for the foreseeable future, at least in the United States and in those economies that have made recent coverage decisions or are at the brink of doing so. So protective are intellectual property regulations that erstwhile contenders in the medical imaging solutions market have tabled their own CT FFR ambitions and chosen to engage in partnerships rather than competition. Whether there is a space for competing approaches to create alternative commercial opportunities here or in areas of the world that espouse different health care market dynamics than our own will remain to be seen.Disclosures of Conflicts of Interest: U.J.S. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: author receives institutional research support and/or honoraria for speaking and consulting from Astellas, Bayer, Bracco, Elucid BioImaging, Guerbet, HeartFlow, and Siemens Healthineers. Other relationships: disclosed no relevant relationships. Activities related to the present article: editorial board member of Radiology: Cardiothoracic Imaging. H.N.G. disclosed no relevant relationships. C.T. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: author receives honoraria for speaking and consulting from HeartFlow and Siemens Healthineers. Other relationships: disclosed no relevant relationships.References1. Nakazato R, Park HB, Berman DS, et al. Noninvasive fractional flow reserve derived from computed tomography angiography for coronary lesions of intermediate stenosis severity: results from the DeFACTO study. Circ Cardiovasc Imaging 2013;6(6):881–889. Crossref, Medline, Google Scholar2. Nørgaard BL, Leipsic J, Gaur S, et al. Diagnostic performance of noninvasive fractional flow reserve derived from coronary computed tomography angiography in suspected coronary artery disease: the NXT trial (Analysis of Coronary Blood Flow Using CT Angiography: Next Steps). J Am Coll Cardiol 2014;63(12):1145–1155. Crossref, Medline, Google Scholar3. Coenen A, Kim YH, Kruk M, et al. Diagnostic Accuracy of a Machine-Learning Approach to Coronary Computed Tomographic Angiography-Based Fractional Flow Reserve: Result From the MACHINE Consortium. Circ Cardiovasc Imaging 2018;11(6):e007217. Crossref, Medline, Google Scholar4. Benton SM Jr, Tesche C, De Cecco CN, Duguay TM, Schoepf UJ, Bayer RR 2nd. Noninvasive Derivation of Fractional Flow Reserve From Coronary Computed Tomographic Angiography: A Review. J Thorac Imaging 2018;33(2):88–96. Crossref, Medline, Google Scholar5. Douglas PS, De Bruyne B, Pontone G, et al. 1-Year Outcomes of FFRCT-Guided Care in Patients With Suspected Coronary Disease: The PLATFORM Study. J Am Coll Cardiol 2016;68(5):435–445. Crossref, Medline, Google Scholar6. Hlatky MA, De Bruyne B, Pontone G, et al. Quality-of-Life and Economic Outcomes of Assessing Fractional Flow Reserve With Computed Tomography Angiography: PLATFORM. J Am Coll Cardiol 2015;66(21):2315–2323. Crossref, Medline, Google Scholar7. Fairbairn TA, Nieman K, Akasaka T, et al. Real-world clinical utility and impact on clinical decision-making of coronary computed tomography angiography-derived fractional flow reserve: lessons from the ADVANCE Registry. Eur Heart J 2018;39(41):3701–3711. Crossref, Medline, Google Scholar8. Patel MR, Nørgaard BL, Fairbairn TA, et al. 1-Year Impact on Medical Practice and Clinical Outcomes of FFRCT: The ADVANCE Registry. JACC Cardiovasc Imaging 2019 Mar 17 [Epub ahead of print]. Crossref, Google Scholar9. Tesche C, De Cecco CN, Baumann S, et al. Coronary CT Angiography-derived Fractional Flow Reserve: Machine Learning Algorithm versus Computational Fluid Dynamics Modeling. Radiology 2018;288(1):64–72. Link, Google Scholar10. van Hamersvelt RW, Voskuil M, de Jong PA, et al. Diagnostic performance of on-site coronary CT angiography–derived fractional flow reserve based on patient-specific lumped parameter models. Radiol Cardiothorac Imaging 2019;1(4):e190036. Link, Google Scholar11. Tesche C, Otani K, De Cecco CN, et al. Influence of Coronary Calcium on Diagnostic Performance of Machine Learning CT-FFR: Results From MACHINE Registry. JACC Cardiovasc Imaging 2019 Aug 14 [Epub ahead of print]. 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