Citations

(1) Background: COVID-19 computed tomography (CT) lung segmentation is critical for COVID lung severity diagnosis. Earlier proposed approaches during 2020–2021 were semiautomated or automated but not accurate, user-friendly, and industry-standard benchmarked. The proposed study compared the COVID Lung Image Analysis System, COVLIAS 1.0 (GBTI, Inc., and AtheroPointTM, Roseville, CA, USA, referred to as COVLIAS), against MedSeg, a web-based Artificial Intelligence (AI) segmentation tool, where COVLIAS uses hybrid deep learning (HDL) models for CT lung segmentation. (2) Materials and Methods: The proposed study used 5000 ITALIAN COVID-19 positive CT lung images collected from 72 patients (experimental data) that confirmed the reverse transcription-polymerase chain reaction (RT-PCR) test. Two hybrid AI models from the COVLIAS system, namely, VGG-SegNet (HDL 1) and ResNet-SegNet (HDL 2), were used to segment the CT lungs. As part of the results, we compared both COVLIAS and MedSeg against two manual delineations (MD 1 and MD 2) using (i) Bland–Altman plots, (ii) Correlation coefficient (CC) plots, (iii) Receiver operating characteristic curve, and (iv) Figure of Merit and (v) visual overlays. A cohort of 500 CROATIA COVID-19 positive CT lung images (validation data) was used. A previously trained COVLIAS model was directly applied to the validation data (as part of Unseen-AI) to segment the CT lungs and compare them against MedSeg. (3) Result: For the experimental data, the four CCs between COVLIAS (HDL 1) vs. MD 1, COVLIAS (HDL 1) vs. MD 2, COVLIAS (HDL 2) vs. MD 1, and COVLIAS (HDL 2) vs. MD 2 were 0.96, 0.96, 0.96, and 0.96, respectively. The mean value of the COVLIAS system for the above four readings was 0.96. CC between MedSeg vs. MD 1 and MedSeg vs. MD 2 was 0.98 and 0.98, respectively. Both had a mean value of 0.98. On the validation data, the CC between COVLIAS (HDL 1) vs. MedSeg and COVLIAS (HDL 2) vs. MedSeg was 0.98 and 0.99, respectively. For the experimental data, the difference between the mean values for COVLIAS and MedSeg showed a difference of <2.5%, meeting the standard of equivalence. The average running times for COVLIAS and MedSeg on a single lung CT slice were ~4 s and ~10 s, respectively. (4) Conclusions: The performances of COVLIAS and MedSeg were similar. However, COVLIAS showed improved computing time over MedSeg.

Background: For COVID-19 lung severity, segmentation of lungs on computed tomography (CT) is the first crucial step. Current deep learning (DL)-based Artificial Intelligence (AI) models have a bias in the training stage of segmentation because only one set of ground truth (GT) annotations are evaluated. We propose a robust and stable inter-variability analysis of CT lung segmentation in COVID-19 to avoid the effect of bias. Methodology: The proposed inter-variability study consists of two GT tracers for lung segmentation on chest CT. Three AI models, PSP Net, VGG-SegNet, and ResNet-SegNet, were trained using GT annotations. We hypothesized that if AI models are trained on the GT tracings from multiple experience levels, and if the AI performance on the test data between these AI models is within the 5% range, one can consider such an AI model robust and unbiased. The K5 protocol (training to testing: 80%:20%) was adapted. Ten kinds of metrics were used for performance evaluation. Results: The database consisted of 5000 CT chest images from 72 COVID-19-infected patients. By computing the coefficient of correlations (CC) between the output of the two AI models trained corresponding to the two GT tracers, computing their differences in their CC, and repeating the process for all three AI-models, we show the differences as 0%, 0.51%, and 2.04% (all < 5%), thereby validating the hypothesis. The performance was comparable; however, it had the following order: ResNet-SegNet > PSP Net > VGG-SegNet. Conclusions: The AI models were clinically robust and stable during the inter-variability analysis on the CT lung segmentation on COVID-19 patients.

Background: Among patients receiving percutaneous coronary intervention (PCI), the role of a genotype-guided approach for antiplatelet therapy compared with usual care is unclear. We conducted a Bayesian analysis of the entire TAILOR-PCI (Tailored Antiplatelet Initiation to Lessen Outcomes Due to Decreased Clopidogrel Response After Percutaneous Coronary Intervention) randomized clinical trial population to evaluate the effect of the genotype-guided antiplatelet therapy post-PCI compared with the usual care on the risk of major adverse cardiovascular events (MACE). Methods: The primary outcome for our study was the composite of MACE (myocardial infarction, stroke, and cardiovascular death). Secondary outcomes included cardiovascular death, stroke, myocardial infarction, stent thrombosis, and major/minor bleeding. Bayesian modeling was used to estimate the probability of clinical benefit of genotype-guided therapy using (1) noninformative priors (ie, analyzing the TAILOR-PCI trial) and (2) informative priors derived from the ADAPT, POPular Genetics, IAC-PCI, and PHARMCLO trials (ie, analyzing TAILOR-PCI trial in the context of prior evidence). Risk ratio (RR: ratio of cumulative outcome incidence between genotype-guided and conventional therapy group) and 95% credible interval (CrI) were estimated for the study outcomes, and probability estimates for RR <1 were computed. Results: Using noninformative priors, in TAILOR-PCI the RR for MACE was 0.78 (95% CrI, 0.55–1.07) in genotype-guided therapy after PCI, and the probability of RR <1 was 94%. Using noninformative priors, the probability of RR <1 for cardiovascular death (RR, 0.95 [95% CrI, 0.52–1.74]), stroke (RR, 0.68 [95% CrI, 0.44–1.06]), myocardial infarction (RR, 0.84 [95% CrI, 0.37–1.89]), stent thrombosis (RR, 0.75 [95% CrI, 0.37–1.45]), and major or minor bleeding (RR, 1.22 [95% CrI, 0.84–1.77]) were 57%, 96%, 67%, 81%, and 15%, respectively. Using informative priors, the posterior probability of RR <1 for MACE, from genotype-guided therapy, was 99% (RR, 0.69 [95% CrI, 0.57–0.84]). Using informative priors, the posterior probability of RR <1 for cardiovascular death (RR, 0.86 [95% CrI, 0.61–1.19]), stroke (RR, 0.69 [95% CrI, 0.48–0.99]), myocardial infarction (RR:0.56 [95% CrI, 0.40–0.78]), stent thrombosis (RR, 0.59 [95% CrI, 0.38–0.94]), and major or minor bleeding (RR, 0.84 [95% CrI, 0.70–0.99]) were 81%, 99%, 99%, 99%, and 99%, respectively. Conclusions: Bayesian analysis of the TAILOR-PCI trial provides clinically meaningful data on the posterior probability of reducing MACE using genotype-guided P2Y 12 inhibitor therapy after PCI.

Background: COVID-19 lung segmentation using Computed Tomography (CT) scans is important for the diagnosis of lung severity. The process of automated lung segmentation is challenging due to (a) CT radiation dosage and (b) ground-glass opacities caused by COVID-19. The lung segmentation methodologies proposed in 2020 were semi- or automated but not reliable, accurate, and user-friendly. The proposed study presents a COVID Lung Image Analysis System (COVLIAS 1.0, AtheroPoint™, Roseville, CA, USA) consisting of hybrid deep learning (HDL) models for lung segmentation. Methodology: The COVLIAS 1.0 consists of three methods based on solo deep learning (SDL) or hybrid deep learning (HDL). SegNet is proposed in the SDL category while VGG-SegNet and ResNet-SegNet are designed under the HDL paradigm. The three proposed AI approaches were benchmarked against the National Institute of Health (NIH)-based conventional segmentation model using fuzzy-connectedness. A cross-validation protocol with a 40:60 ratio between training and testing was designed, with 10% validation data. The ground truth (GT) was manually traced by a radiologist trained personnel. For performance evaluation, nine different criteria were selected to perform the evaluation of SDL or HDL lung segmentation regions and lungs long axis against GT. Results: Using the database of 5000 chest CT images (from 72 patients), COVLIAS 1.0 yielded AUC of ~0.96, ~0.97, ~0.98, and ~0.96 (p-value < 0.001), respectively within 5% range of GT area, for SegNet, VGG-SegNet, ResNet-SegNet, and NIH. The mean Figure of Merit using four models (left and right lung) was above 94%. On benchmarking against the National Institute of Health (NIH) segmentation method, the proposed model demonstrated a 58% and 44% improvement in ResNet-SegNet, 52% and 36% improvement in VGG-SegNet for lung area, and lung long axis, respectively. The PE statistics performance was in the following order: ResNet-SegNet > VGG-SegNet > NIH > SegNet. The HDL runs in <1 s on test data per image. Conclusions: The COVLIAS 1.0 system can be applied in real-time for radiology-based clinical settings.

BACKGROUND The United States (US)‐Mexico border is a socioeconomically underserved area. We sought to investigate whether stroke‐related mortality varies between the US border and nonborder counties. METHODS AND RESULTS We used death certificates from the Centers for Disease Control and Prevention Wide‐Ranging Online Data for Epidemiologic Research database to examine stroke‐related mortality in border versus nonborder counties in California, Texas, New Mexico, and Arizona. We measured average annual percent changes (AAPCs) in age‐adjusted mortality rates (AAMRs) per 100 000 between 1999 and 2018. Overall, AAMRs were higher for nonborder counties, older adults, men, and non‐Hispanic Black adults than their counterparts. Between 1999 and 2018, AAMRs reduced from 55.8 per 100 000 to 34.4 per 100 000 in the border counties (AAPC, −2.70) and 64.5 per 100 000 to 37.6 per 100 000 in nonborder counties (AAPC, −2.92). The annual percent change in AAMR initially decreased, followed by stagnation in both border and nonborder counties since 2012. The AAPC in AAMR decreased in all 4 states; however, AAMR increased in California’s border counties since 2012 (annual percent change, 3.9). The annual percent change in AAMR decreased for older adults between 1999 and 2012 for the border (−5.10) and nonborder counties (−5.01), followed by a rise in border counties and stalling in nonborder counties. Although the AAPC in AAMR decreased for both sexes, the AAPC in AAMR differed significantly for non‐Hispanic White adults in border (−2.69) and nonborder counties (−2.86). The mortality decreased consistently for all other ethnicities/races in both border and nonborder counties. CONCLUSIONS Stroke‐related mortality varied between the border and nonborder counties. Given the substantial public health implications, targeted interventions aimed at vulnerable populations are required to improve stroke‐related outcomes in the US‐Mexico border area.

Objectives To assess the ability of the current classification system for popliteal entrapment syndrome to accurately capture all patients, and if not, to design an all-inclusive new classification. Methods Retrospective review of all interventions performed for popliteal entrapment syndrome between 1994 and 2013 at our institution was performed. Preoperative imaging and intraoperative findings were used to establish the compressive morphology of popliteal entrapment syndrome. Patients were categorized, when possible, into six types of the current classification system (Rich classification, modified by Levien) and into seven types of a new classification. Results Sixty-seven limbs of 49 patients were operated on for unilateral (31) or bilateral (18) popliteal entrapment syndrome. The current classification system captured the anatomy of only 43 (64%) of 67 limbs with popliteal entrapment syndrome. Compressive morphologies without a defined class included aberrant insertion of the lateral head of gastrocnemius muscle, muscle slip originating from the lateral head of gastrocnemius or hamstrings, hypertrophied hamstring muscle, abnormal fibrous bands, perivascular connective tissue, and prominent lateral femoral condyle. The new classification captured 100% of the limbs with popliteal entrapment syndrome. Conclusions Current classification of popliteal entrapment syndrome is inadequate as more than one-third of the cases reviewed fell outside of the standard classification system. Consideration of a more inclusive new anatomic classification system is warranted.

Objective: The objective of this study is to predict in vivo lung mass density for patients with interstitial lung disease using different gradient boosting decision tree (GBDT) algorithms based on measurements from lung ultrasound surface wave elastography (LUSWE) and pulmonary function testing (PFT). Methods: Age and weight of study subjects (57 patients with interstitial lung disease and 20 healthy subjects), surface wave speeds at three vibration frequencies (100, 150, and 200 Hz) from LUSWE, and predicted forced expiratory volume (FEV1% pre) and ratio of forced expiratory volume to forced vital capacity (FEV1%/FVC%) from PFT were used as inputs while lung mass densities based on the Hounsfield Unit from high resolution computed tomography (HRCT) were used as labels to train the regressor in three GBDT algorithms, XGBoost, CatBoost, and LightGBM. 80% (20%) of the dataset was used for training (testing). Results: The results showed that predictions using XGBoost regressor obtained an accuracy of 0.98 in the test dataset. Conclusion: The obtained results suggest that XGBoost regressor based on the measurements from LUSWE and PFT may be able to noninvasively assess lung mass density in vivo for patients with pulmonary disease.

Abstract Aim The 2018 American Heart Association/American College of Cardiology/Multi-Society Cholesterol Guidelines recommended the addition of non-statins to statin therapy for high-risk secondary prevention patients above a low-density lipoprotein cholesterol (LDL-C) threshold of ≥70 mg/dL (1.8 mmol/L). We compared effectiveness and safety of treatment to achieve lower (&lt;70) vs. higher (≥70 mg/dL) LDL-C among patients receiving intensive lipid-lowering therapy (statins alone or plus ezetimibe or proprotein convertase subtilisin/kexin type 9 inhibitors). Methods and results Eleven randomized controlled trials (130 070 patients), comparing intensive vs. less-intensive lipid-lowering therapy, with follow-up ≥6 months and sample size ≥1000 patients were selected. Meta-analysis was reported as random effects risk ratios (RRs) [95% confidence intervals] and absolute risk differences (ARDs) as incident cases per 1000 person-years. The median LDL-C levels achieved in lower LDL-C vs. higher LDL-C groups were 62 and 103 mg/dL, respectively. At median follow-up of 2 years, the lower LDL-C vs. higher LDL-C group was associated with significant reduction in all-cause mortality [ARD −1.56; RR 0.94 (0.89–1.00)], cardiovascular mortality [ARD −1.49; RR 0.90 (0.81–1.00)], and reduced risk of myocardial infarction, cerebrovascular events, revascularization, and major adverse cardiovascular events (MACE). These benefits were achieved without increasing the risk of incident cancer, diabetes mellitus, or haemorrhagic stroke. All-cause mortality benefit in lower LDL-C group was limited to statin therapy and those with higher baseline LDL-C (≥100 mg/dL). However, the RR reduction in ischaemic and safety endpoints was independent of baseline LDL-C or drug therapy. Conclusion This meta-analysis showed that treatment to achieve LDL-C levels below 70 mg/dL using intensive lipid-lowering therapy can safely reduce the risk of mortality and MACE.