Deep Learning for Detection of Pneumothorax and Pleural Effusion on Chest Radiographs: Validation Against Computed Tomography, Impact on Resident Reading Time, and Interreader Concordance.

PURPOSE: To study the performance of artificial intelligence (AI) for detecting pleural pathology on chest radiographs (CXRs) using computed tomography as ground truth. PATIENTS AND METHODS: Retrospective study of subjects undergoing CXR in various clinical settings. Computed tomography obtained within 24 hours of the CXR was used to volumetrically quantify pleural effusions (PEfs) and pneumothoraxes (Ptxs). CXR was evaluated by AI software (INSIGHT CXR; Lunit) and by 3 second-year radiology residents, followed by AI-assisted reassessment after a 3-month washout period. We used the area under the receiver operating characteristics curve (AUROC) to assess AI versus residents' performance and mixed-model analyses to investigate differences in reading time and interreader concordance. RESULTS: There were 96 control subjects, 165 with PEf, and 101 with Ptx. AI-AUROC was noninferior to aggregate resident-AUROC for PEf (0.82 vs 0.86, P < 0.001) and Ptx (0.80 vs 0.84, P = 0.001) detection. AI-assisted resident-AUROC was higher but not significantly different from the baseline. AI-assisted reading time was reduced by 49% (157 vs 80 s per case, P = 0.009), and Fleiss kappa for Ptx detection increased from 0.70 to 0.78 ( P = 0.003). AI decreased detection error for PEf (odds ratio = 0.74, P = 0.024) and Ptx (odds ratio = 0.39, P < 0.001). CONCLUSION: Current AI technology for the detection of PEf and Ptx on CXR was noninferior to second-year resident performance and could help decrease reading time and detection error.

Detrimental effect of systemic vascular risk factors on brain hemodynamic function assessed with MRI.

Background and purpose Vascular risk factors have been associated with decreased cerebral blood flow (CBF) but this is etiologically nonspecific and may result from vascular insufficiency or a response to decreased brain metabolic activity. We apply new MRI techniques to measure oxygen extraction fraction (OEF) and cerebral metabolic rate of oxygen consumption (CMRO2), hypothesizing that decreased CBF related to these vascular risk factors will be associated with increased OEF, confirming a primary vascular insufficiency. Methods 3T MRI was obtained on 70 community-based participants in this IRB-approved study with informed consent, with previous assessment of systolic blood pressure, hypertension medication, elevated serum triglycerides, low serum HDL, and diabetes mellitus. CBF was measured using phase contrast adjusted for brain volume (ml/100 g/min), OEF (%) was obtained from T2-Relaxation-Under-Spin-Tagging (TRUST), and CMRO2 (µmol/100 g/min) was derived using the Fick principle. Stepwise linear regression identified optimal predictors of CBF with age, sex, and hematocrit included for adjustment. This predictive model was then evaluated against OEF and CMRO2. Results Hypertriglyceridemia was associated with low CBF and high OEF. High systolic blood pressure was associated with high CBF and low OEF, which was primarily attributable to those with pressures above 160 mmHg. Neither risk factor was associated with significant differences in cerebral metabolic rate. Conclusion Low CBF related to hypertriglyceridemia was accompanied by high OEF with no significant difference in CMRO2, confirming subclinical vascular insufficiency. High CBF related to high systolic blood pressure likely reflected limitations of autoregulation at higher blood pressures.

Noninvasive cardiac imaging in pulmonary hypertension.

Pulmonary artery hypertension is a rare disease with significant morbidity and mortality. Initial and serial noninvasive assessment of these patients can be accomplished with transthoracic echocardiography and/or cardiac magnetic resonance imaging. These complementary techniques provide the structural and functional information required to care for patients with pulmonary artery hypertension and are discussed in this review.