Optimising postoperative care: Same-day discharge after transurethral resection of the prostate.

INTRODUCTION: This study aims to assess the feasibility and safety of same-day discharge after transurethral resection of the prostate. MATERIALS AND METHODS: Five years of records were retrospectively analysed. Length of stay categorised patients into Groups 1 (same-day discharge) and 2 (standard-length discharge). Logistic regression analysis was performed, controlling for clinicodemographic factors. Student's t-test compared continuous bladder irrigation and catheter dwell times. RESULTS: A total of 459 patients were identified between 2016 and 2021, 280 in Group 1 and 179 in Group 2, with median ages of 71.0 (interquartile range 36-92) and 72.0 (interquartile range 47-101) years (p = 0.067), respectively. Same-day discharge rates notably increased post-2018 (p = 0.025). Median prostate tissue resected in Group 2 was 7.1g (3.4-12.4g) and in Group 1 was 4.9g (2.4-10.2g; p = 0.034). While continuous bladder irrigation >1 hour was significantly lower in Group 1 than Group 2 (96.8% versus 27.4%; p = 0.0001), catheter dwell times were comparable (70.1 and 70.8 hours, respectively). Control-adjusted results showed a 40% reduction in emergency department representation odds for Group 1 compared with Group 2 (odds ratio = 0.60; 95% confidence interval = 0.37-0.99; p = 0.04). Length of stay was not significantly associated with hospital readmissions (p = 0.11). Continuous bladder irrigation for <1 hour in Group 1 was associated with a reduced emergency department representation (odds ratio = 0.43; 95% confidence interval = 0.197-0.980) but not readmission (odds ratio = 0.413; 95% confidence interval = 0.166-1.104). CONCLUSIONS: Same-day discharge post-transurethral resection of the prostate may be a viable and safe option for carefully selected patients.

Harnessing artificial intelligence for prostate cancer management.

Prostate cancer (PCa) is a common malignancy in males. The pathology review of PCa is crucial for clinical decision-making, but traditional pathology review is labor intensive and subjective to some extent. Digital pathology and whole-slide imaging enable the application of artificial intelligence (AI) in pathology. This review highlights the success of AI in detecting and grading PCa, predicting patient outcomes, and identifying molecular subtypes. We propose that AI-based methods could collaborate with pathologists to reduce workload and assist clinicians in formulating treatment recommendations. We also introduce the general process and challenges in developing AI pathology models for PCa. Importantly, we summarize publicly available datasets and open-source codes to facilitate the utilization of existing data and the comparison of the performance of different models to improve future studies.

Ductal, intraductal, and cribriform carcinoma of the prostate: Molecular characteristics and clinical management.

Prostatic acinar adenocarcinoma accounts for approximately 95% of prostate cancer (CaP) cases. The remaining 5% of histologic subtypes of CaP are known to be more aggressive and have recently garnered substantial attention. These histologic subtypes - namely, prostatic ductal adenocarcinoma (PDA), intraductal carcinoma of the prostate (IDC-P), and cribriform carcinoma of the prostate (CC-P) - typically exhibit distinct growth characteristics, genomic features, and unique oncologic outcomes. For example, PTEN mutations, which cause uncontrolled cell growth, are frequently present in IDC-P and CC-P. Germline mutations in homologous DNA recombination repair (HRR) genes (e.g., BRCA1, BRCA2, ATM, PALB2, and CHEK2) are discovered in 40% of patients with IDC-P, while only 9% of patients without ductal involvement had a germline mutation. CC-P is associated with deletions in common tumor suppressor genes, including PTEN, TP53, NKX3-1, MAP3K7, RB1, and CHD1. Evidence suggests abiraterone may be superior to docetaxel as a first-line treatment for patients with IDC-P. To address these and other critical pathological attributes, this review examines the molecular pathology, genetics, treatments, and oncologic outcomes associated with CC-P, PDA, and IDC-P with the objective of creating a comprehensive resource with a centralized repository of information on PDA, IDC-P, and CC-P.

Less qualitative multiparametric magnetic resonance imaging in prostate cancer can underestimate extraprostatic extension in higher grade tumors.

BACKGROUND: Multiparametric magnetic resonance imaging (mpMRI) is increasingly used for risk stratification and preoperative staging of prostate cancer. It remains unclear how Grade Group (GG) interacts with the ability of mpMRI to determine the presence of extraprostatic extension (EPE) on surgical pathology. METHODS: A retrospective review of a robotic assisted laparoscopic radical prostatectomy (RALP) database from 2016-2020 was performed. Radiology mpMRI reports by multiple attending radiologists and without clear standardization or quality control were retrospectively assessed for EPE findings and compared with surgical pathology reports. The data were stratified by biopsy-based GG and a multivariable cluster analysis was performed to incorporate additional preoperative variables (age at diagnosis, PSA, etc.). Hazard ratios were calculated to determine how mpMRI findings and radiographic EPE relate to positive surgical margins. RESULTS: 289 patients underwent at least one mpMRI prior to RALP. Preoperative mpMRI demonstrated sensitivity of 39.3% and specificity of 88.8% for pathological EPE and had a negative predictive value (NPV) of 49.5%, and positive predictive value (PPV) of 84.0%. Stratification of NPV by GG yielded the following values: GG 1-5 (49.5%), GG 3-5 (40.8%), GG 4-5 (43.4%), and GG 5 (30.4%). Additionally, positive EPE on preoperative mpMRI was associated with a significantly decreased risk of positive surgical margins (RR: 0.655; 95% CI: 0.557-0.771). CONCLUSIONS: NPV of prostate mpMRI for EPE may be decreased for higher grade tumors. A detailed reference reading and image quality optimization may improve performance. However, urologists should exercise caution in nerve sparing approaches in these patients.

Epidemiology of and Risk Factors in Postoperative Complications from Robotically Assisted Laparoscopic Radical Prostatectomy in Contemporary National Surgical Quality Improvement Program Data.

Introduction: For localized clinically significant prostate cancer (csPCa), robotically assisted laparoscopic radical prostatectomy (RALP) is the gold standard surgical treatment. Despite low overall complication rate, continued quality assurance (QA) efforts to minimize complications of RALP are important, particularly given movement toward same-day discharge. In 2019, National Surgical Quality Improvement Program (NSQIP) began collecting RALP-specific data. In this study, we assessed pre- and perioperative factors associated with postoperative complications for RALP to further QA efforts. Materials and Methods: Surgical records of csPCa patients who underwent RALP were retrieved from the 2019 to 2021 NSQIP database, including new RALP-specific data. Multivariate logistic regression evaluated the association between risk factors and outcomes specific to RALP and pelvic lymph node dissection (PLND). Input variables included American Society of Anesthesiologists (ASA) class, age, operative time, and body mass index (BMI). Variables from the extended dataset with PLND information included number of nodes evaluated, perioperative antibiotics, postoperative venous thromboembolism (VTE) prophylaxis, history of prior pelvic surgery, and history of prior radiotherapy (RT). Outcomes of interest were any surgical complication, infection, pulmonary embolism, deep venous thrombosis, acute kidney injury, pneumonia, lymphocele, and urinary/anastomotic leak (UAL). Results: A total of 11,811 patients were included with 6.1% experiencing any complication. Prior RT, prior pelvic surgery, older age, higher BMI, lack of perioperative antibiotic therapy, longer operative time, PLND, and number of lymph nodes dissected were associated with higher risk of postoperative complications. Regarding procedure-specific complications, there were increased odds of UAL with prior RT, prior pelvic surgery, longer operative time, and higher BMI. Odds of developing lymphocele increased with prior pelvic surgery, performance of PLND, and increased number of nodes evaluated. Conclusion: In contemporary NSQIP data, RALP is associated with low complication rates; however, these rates have increased compared with historical studies. Attention to and counseling regarding risk factors for peri- and postoperative complications are important to set expectations and minimize risk of unplanned return to a health care setting after discharge.

"Shared decision-making" for prostate cancer screening: Is it a marker of quality preventative healthcare?

BACKGROUND: "Shared decision-making" (SDM) is a cornerstone of prostate cancer (PCa) screening guidelines due to tradeoffs between clinical benefits and concerns for over-diagnosis and over-treatment. SDM requires effort by primary-care-providers (PCP) in an often busy clinical setting to understand patient preferences with the backdrop of patient risk factors. We hypothesized that SDM for PCa screening, given its prominence in guidelines and practical challenges, may be associated with quality preventative healthcare in terms of other appropriate cancer screening and encouragement of other preventative health behaviors. METHODS: From the 2020 Behavioral Risk Factor Surveillance Survey, 50-75 year old men who underwent PSA screening were assessed for their participation in SDM, PCa and colorectal cancer (CRC) screening, and other preventative health behaviors, like vaccination, exercise, and smoking status. Adjusted odds ratio of likelihood of PSA testing as a function of SDM was calculated. Likelihoods of SDM and PSA testing as a function of preventative health behaviors were also calculated. RESULTS: Screening rates were 62 % for PCa and 88 % for CRC. Rates of SDM were 39.1 % in those with PSA screening, and 16.2 % in those without. Odds of PSA screening were higher when SDM was present (AOR = 2.68). History of colonoscopy was associated with higher odds of SDM (AOR = 1.16) and PSA testing (AOR = 1.94). Health behaviors, like regular exercise, were associated with increased odds of SDM (AOR = 1.14) and PSA testing (AOR = 1.28). History of flu vaccination (AOR = 1.29) and pneumonia vaccination (AOR = 1.19) were associated with higher odds of SDM. Those who received the flu vaccine were also more likely to have PSA testing (AOR = 1.36). Smoking was negatively associated with SDM (AOR = 0.86) and PSA testing (AOR = 0.93). Older age was associated with higher rates of PSA screening (AOR = 1.03, CI = 1.03-1.03). Black men were more likely than white men to have SDM (AOR = 1.6, CI = 1.59 - 1.6) and decreased odds of PSA testing (AOR = 0.94, CI = 0.94 - 0.95). CONCLUSIONS: SDM was associated with higher odds of PSA screening, CRC screening, and other appropriate preventative health behaviors. Racial disparities exist in both SDM and PSA screening usage. SDM may be a trackable metric that can lead to wider preference-sensitive care and improved preventative care.

Renal Imaging in Stone Disease: Which Modality to Choose?

Numerous imaging modalities are available to the provider when diagnosing or surveilling kidney stones. The decision to order one over the other can be nuanced and especially confusing to non-urologic practitioners. This manuscript reviews the main modalities used to image stones in the modern era - renal bladder ultrasound, Kidney Ureter Bladder plain film radiography (KUB), magnetic resonance imaging (MRI), and non-contrast computerized tomography (NCCT). While NCCT has become the most popular and familiar modality for most practitioners, particularly in the acute setting, ultrasound is a cost-effective technology that is adept at monitoring interval stone development in patients and evaluating for the presence of hydronephrosis. KUB and MRI also occupy unique niches in the management of urolithiasis. In the correct clinical setting, each of these modalities has a role in the acute workup and management of suspected nephrolithiasis.

The Surgical Management of Urolithiasis: A Review of the Literature.

The incidence of stone disease has increased significantly in the past 30 years, with a reported prevalence of 11% of the U.S. population in 2022, up from 9% in 2012 and 5.2% in 1994.1 While prevention is a vital aspect of management, many patients present with symptomatic urolithiasis requiring surgical management. Emerging advances in endoscopy and technology has led to a dynamic shift in the surgical management of stone disease. This paper will serve as a comprehensive review to inform urologic and non-urologic medical professionals alike, as well as the layperson, on the surgical treatment of nephrolithiasis, starting from the initial evaluation, laboratory and radiographic studies, and various surgical options. Additionally, the nuances of managing the pediatric and pregnant patient with nephrolithiasis will be explored. Using the most up-to-date urologic data, our aim is to provide a comprehensive resource for readers who interact with patients experiencing acute episodes of urolithiasis.

Vascular Implications of COVID-19: Role of Radiological Imaging, Artificial Intelligence, and Tissue Characterization: A Special Report.

The SARS-CoV-2 virus has caused a pandemic, infecting nearly 80 million people worldwide, with mortality exceeding six million. The average survival span is just 14 days from the time the symptoms become aggressive. The present study delineates the deep-driven vascular damage in the pulmonary, renal, coronary, and carotid vessels due to SARS-CoV-2. This special report addresses an important gap in the literature in understanding (i) the pathophysiology of vascular damage and the role of medical imaging in the visualization of the damage caused by SARS-CoV-2, and (ii) further understanding the severity of COVID-19 using artificial intelligence (AI)-based tissue characterization (TC). PRISMA was used to select 296 studies for AI-based TC. Radiological imaging techniques such as magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound were selected for imaging of the vasculature infected by COVID-19. Four kinds of hypotheses are presented for showing the vascular damage in radiological images due to COVID-19. Three kinds of AI models, namely, machine learning, deep learning, and transfer learning, are used for TC. Further, the study presents recommendations for improving AI-based architectures for vascular studies. We conclude that the process of vascular damage due to COVID-19 has similarities across vessel types, even though it results in multi-organ dysfunction. Although the mortality rate is ~2% of those infected, the long-term effect of COVID-19 needs monitoring to avoid deaths. AI seems to be penetrating the health care industry at warp speed, and we expect to see an emerging role in patient care, reduce the mortality and morbidity rate.

COVLIAS 2.0-cXAI: Cloud-Based Explainable Deep Learning System for COVID-19 Lesion Localization in Computed Tomography Scans.

Background: The previous COVID-19 lung diagnosis system lacks both scientific validation and the role of explainable artificial intelligence (AI) for understanding lesion localization. This study presents a cloud-based explainable AI, the "COVLIAS 2.0-cXAI" system using four kinds of class activation maps (CAM) models. Methodology: Our cohort consisted of ~6000 CT slices from two sources (Croatia, 80 COVID-19 patients and Italy, 15 control patients). COVLIAS 2.0-cXAI design consisted of three stages: (i) automated lung segmentation using hybrid deep learning ResNet-UNet model by automatic adjustment of Hounsfield units, hyperparameter optimization, and parallel and distributed training, (ii) classification using three kinds of DenseNet (DN) models (DN-121, DN-169, DN-201), and (iii) validation using four kinds of CAM visualization techniques: gradient-weighted class activation mapping (Grad-CAM), Grad-CAM++, score-weighted CAM (Score-CAM), and FasterScore-CAM. The COVLIAS 2.0-cXAI was validated by three trained senior radiologists for its stability and reliability. The Friedman test was also performed on the scores of the three radiologists. Results: The ResNet-UNet segmentation model resulted in dice similarity of 0.96, Jaccard index of 0.93, a correlation coefficient of 0.99, with a figure-of-merit of 95.99%, while the classifier accuracies for the three DN nets (DN-121, DN-169, and DN-201) were 98%, 98%, and 99% with a loss of ~0.003, ~0.0025, and ~0.002 using 50 epochs, respectively. The mean AUC for all three DN models was 0.99 (p < 0.0001). The COVLIAS 2.0-cXAI showed 80% scans for mean alignment index (MAI) between heatmaps and gold standard, a score of four out of five, establishing the system for clinical settings. Conclusions: The COVLIAS 2.0-cXAI successfully showed a cloud-based explainable AI system for lesion localization in lung CT scans.

The state of PSA counseling in male-to-female transgender patients in the U.S.

BACKGROUND: Morbidity and mortality from prostate cancer (PCa) are known to vary heavily based on socioeconomic and demographic risk factors. We sought to describe prescreening PSA (prostate-specific antigen) counseling (PPC) rates amongst male-to-female transgender (MtF-TG) patients and non-TG patients using the behavioral risk factor surveillance system (BRFSS). METHODS: We used the survey data from 2014, 2016, and 2018 BRFSS and included respondents aged 40-79 years who completed the "PCa screening" and "sexual orientation and gender identity" modules. We analyzed differences in age, education level, income level, marital status, and race/ethnicity using Pearson's χ2 tests. The association of PPC with MtF-TG status and other patient characteristics was evaluated using multivariate logistic regression. RESULTS: A total of 175,383 respondents were included, of which 0.3% identified as MtF-TG. Overall, 62.4% of respondents reported undergoing PPC. On univariate analysis, PPC rates were lower among MtF-TG respondents when compared to the non-TG group (58.3% vs. 62.4%, p = 0.03). MtF-TG respondents were also more likely to report lower education level (p < 0.01), lower-income level (p < 0.01), and were less likely to be white (p < 0.01) than non-TG respondents. However, multivariate analysis adjusting for these respondent features demonstrated an association between higher income and higher education levels with increased odds of PPC, but no association was demonstrated between MtF-TG status and PPC rates. PPC rates for the MtF-TG and non-TG populations did not change significantly over time. CONCLUSIONS: Although PPC was less frequently reported among MtF-TG respondents than in the non-TG group on univariate analysis, this association was not demonstrated when controlling for confounders, including education and income levels. Instead, on multivariate analysis, low education and income levels were more predictive of PPC rates. Further research is needed to ensure equivalent access to prescreening counseling for patients across the socioeconomic and gender identity spectrum.

Role of Artificial Intelligence in Radiogenomics for Cancers in the Era of Precision Medicine.

Radiogenomics, a combination of "Radiomics" and "Genomics," using Artificial Intelligence (AI) has recently emerged as the state-of-the-art science in precision medicine, especially in oncology care. Radiogenomics syndicates large-scale quantifiable data extracted from radiological medical images enveloped with personalized genomic phenotypes. It fabricates a prediction model through various AI methods to stratify the risk of patients, monitor therapeutic approaches, and assess clinical outcomes. It has recently shown tremendous achievements in prognosis, treatment planning, survival prediction, heterogeneity analysis, reoccurrence, and progression-free survival for human cancer study. Although AI has shown immense performance in oncology care in various clinical aspects, it has several challenges and limitations. The proposed review provides an overview of radiogenomics with the viewpoints on the role of AI in terms of its promises for computational as well as oncological aspects and offers achievements and opportunities in the era of precision medicine. The review also presents various recommendations to diminish these obstacles.

Cardiovascular Risk Stratification in Diabetic Retinopathy via Atherosclerotic Pathway in COVID-19/Non-COVID-19 Frameworks Using Artificial Intelligence Paradigm: A Narrative Review.

Diabetes is one of the main causes of the rising cases of blindness in adults. This microvascular complication of diabetes is termed diabetic retinopathy (DR) and is associated with an expanding risk of cardiovascular events in diabetes patients. DR, in its various forms, is seen to be a powerful indicator of atherosclerosis. Further, the macrovascular complication of diabetes leads to coronary artery disease (CAD). Thus, the timely identification of cardiovascular disease (CVD) complications in DR patients is of utmost importance. Since CAD risk assessment is expensive for low-income countries, it is important to look for surrogate biomarkers for risk stratification of CVD in DR patients. Due to the common genetic makeup between the coronary and carotid arteries, low-cost, high-resolution imaging such as carotid B-mode ultrasound (US) can be used for arterial tissue characterization and risk stratification in DR patients. The advent of artificial intelligence (AI) techniques has facilitated the handling of large cohorts in a big data framework to identify atherosclerotic plaque features in arterial ultrasound. This enables timely CVD risk assessment and risk stratification of patients with DR. Thus, this review focuses on understanding the pathophysiology of DR, retinal and CAD imaging, the role of surrogate markers for CVD, and finally, the CVD risk stratification of DR patients. The review shows a step-by-step cyclic activity of how diabetes and atherosclerotic disease cause DR, leading to the worsening of CVD. We propose a solution to how AI can help in the identification of CVD risk. Lastly, we analyze the role of DR/CVD in the COVID-19 framework.

Cardiovascular/Stroke Risk Assessment in Patients with Erectile Dysfunction-A Role of Carotid Wall Arterial Imaging and Plaque Tissue Characterization Using Artificial Intelligence Paradigm: A Narrative Review.

PURPOSE: The role of erectile dysfunction (ED) has recently shown an association with the risk of stroke and coronary heart disease (CHD) via the atherosclerotic pathway. Cardiovascular disease (CVD)/stroke risk has been widely understood with the help of carotid artery disease (CTAD), a surrogate biomarker for CHD. The proposed study emphasizes artificial intelligence-based frameworks such as machine learning (ML) and deep learning (DL) that can accurately predict the severity of CVD/stroke risk using carotid wall arterial imaging in ED patients. METHODS: Using the PRISMA model, 231 of the best studies were selected. The proposed study mainly consists of two components: (i) the pathophysiology of ED and its link with coronary artery disease (COAD) and CHD in the ED framework and (ii) the ultrasonic-image morphological changes in the carotid arterial walls by quantifying the wall parameters and the characterization of the wall tissue by adapting the ML/DL-based methods, both for the prediction of the severity of CVD risk. The proposed study analyzes the hypothesis that ML/DL can lead to an accurate and early diagnosis of the CVD/stroke risk in ED patients. Our finding suggests that the routine ED patient practice can be amended for ML/DL-based CVD/stroke risk assessment using carotid wall arterial imaging leading to fast, reliable, and accurate CVD/stroke risk stratification. SUMMARY: We conclude that ML and DL methods are very powerful tools for the characterization of CVD/stroke in patients with varying ED conditions. We anticipate a rapid growth of these tools for early and better CVD/stroke risk management in ED patients.

COVLIAS 1.0Lesion vs. MedSeg: An Artificial Intelligence Framework for Automated Lesion Segmentation in COVID-19 Lung Computed Tomography Scans.

Background: COVID-19 is a disease with multiple variants, and is quickly spreading throughout the world. It is crucial to identify patients who are suspected of having COVID-19 early, because the vaccine is not readily available in certain parts of the world. Methodology: Lung computed tomography (CT) imaging can be used to diagnose COVID-19 as an alternative to the RT-PCR test in some cases. The occurrence of ground-glass opacities in the lung region is a characteristic of COVID-19 in chest CT scans, and these are daunting to locate and segment manually. The proposed study consists of a combination of solo deep learning (DL) and hybrid DL (HDL) models to tackle the lesion location and segmentation more quickly. One DL and four HDL models­namely, PSPNet, VGG-SegNet, ResNet-SegNet, VGG-UNet, and ResNet-UNet­were trained by an expert radiologist. The training scheme adopted a fivefold cross-validation strategy on a cohort of 3000 images selected from a set of 40 COVID-19-positive individuals. Results: The proposed variability study uses tracings from two trained radiologists as part of the validation. Five artificial intelligence (AI) models were benchmarked against MedSeg. The best AI model, ResNet-UNet, was superior to MedSeg by 9% and 15% for Dice and Jaccard, respectively, when compared against MD 1, and by 4% and 8%, respectively, when compared against MD 2. Statistical tests­namely, the Mann−Whitney test, paired t-test, and Wilcoxon test­demonstrated its stability and reliability, with p < 0.0001. The online system for each slice was <1 s. Conclusions: The AI models reliably located and segmented COVID-19 lesions in CT scans. The COVLIAS 1.0Lesion lesion locator passed the intervariability test.

COVLIAS 1.0 vs. MedSeg: Artificial Intelligence-Based Comparative Study for Automated COVID-19 Computed Tomography Lung Segmentation in Italian and Croatian Cohorts.

(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.

Nutrition, atherosclerosis, arterial imaging, cardiovascular risk stratification, and manifestations in COVID-19 framework: a narrative review.

Background: Atherosclerosis is the primary cause of the cardiovascular disease (CVD). Several risk factors lead to atherosclerosis, and altered nutrition is one among those. Nutrition has been ignored quite often in the process of CVD risk assessment. Altered nutrition along with carotid ultrasound imaging-driven atherosclerotic plaque features can help in understanding and banishing the problems associated with the late diagnosis of CVD. Artificial intelligence (AI) is another promisingly adopted technology for CVD risk assessment and management. Therefore, we hypothesize that the risk of atherosclerotic CVD can be accurately monitored using carotid ultrasound imaging, predicted using AI-based algorithms, and reduced with the help of proper nutrition. Layout: The review presents a pathophysiological link between nutrition and atherosclerosis by gaining a deep insight into the processes involved at each stage of plaque development. After targeting the causes and finding out results by low-cost, user-friendly, ultrasound-based arterial imaging, it is important to (i) stratify the risks and (ii) monitor them by measuring plaque burden and computing risk score as part of the preventive framework. Artificial intelligence (AI)-based strategies are used to provide efficient CVD risk assessments. Finally, the review presents the role of AI for CVD risk assessment during COVID-19. Conclusions: By studying the mechanism of low-density lipoprotein formation, saturated and trans fat, and other dietary components that lead to plaque formation, we demonstrate the use of CVD risk assessment due to nutrition and atherosclerosis disease formation during normal and COVID times. Further, nutrition if included, as a part of the associated risk factors can benefit from atherosclerotic disease progression and its management using AI-based CVD risk assessment.

Inter-Variability Study of COVLIAS 1.0: Hybrid Deep Learning Models for COVID-19 Lung Segmentation in Computed Tomography.

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.