Deep learning for image-based liver analysis — A comprehensive review focusing on malignant lesions

Deep learning-based methods, in particular, convolutional neural networks and fully convolutional networks are now widely used in the medical image analysis domain. The scope of this review focuses on the analysis using deep learning of focal liver lesions, with a special interest in hepatocellular carcinoma and metastatic cancer; and structures like the parenchyma or the vascular system. Here, we address several neural network architectures used for analyzing the anatomical structures and lesions in the liver from various imaging modalities such as computed tomography, magnetic resonance imaging and ultrasound. Image analysis tasks like segmentation, object detection and classification for the liver, liver vessels and liver lesions are discussed. Based on the qualitative search, 91 papers were filtered out for the survey, including journal publications and conference proceedings. The papers reviewed in this work are grouped into eight categories based on the methodologies used. By comparing the evaluation metrics, hybrid models performed better for both the liver and the lesion segmentation tasks, ensemble classifiers performed better for the vessel segmentation tasks and combined approach performed better for both the lesion classification and detection tasks. The performance was measured based on the Dice score for the segmentation, and accuracy for the classification and detection tasks, which are the most commonly used metrics.publishedVersio

Clinical presentation of acute coronary syndrome in patients previously treated with nitrates

Aims: Several reports have suggested that nitrates limit acute ischaemic damage by a mechanism similar to preconditioning. This study aims to evaluate the effect of chronic oral nitrates on the clinical presentation and short-term outcomes of patients admitted with acute coronary syndrome (ACS).Methods: A retrospective cohort study was conducted in patients with ACS admitted to 62 acute care units from 2010 to 2011. A propensity score-matched samples analysis was performed.Results: We analysed 3171 consecutive patients, of whom 298 (9.4%) were chronically treated with nitrates. Patients previously treated with nitrates had higher comorbidity and disease severity at admission, lower prevalence of ACS with ST elevation, lower troponin elevation, higher prevalence of initial Killip class 2-4 and higher hospital mortality. The propensity score-matched analysis confirmed that previous use of nitrates is independently associated with a lower prevalence of ST-elevation ACS [odds ratio (OR) 0.53, 95% confidence interval (CI) 0.36-0.78; P = 0.0014] and a lower troponin elevation (OR 0.61, 95% CI 0.41-0.92) but not with Killip class on admission (OR 1.18, 95% CI 0.83-1.67, P = 0.3697) or mortality (OR 0.71, 95% CI 0.37-1.38, P = 0.3196).Conclusion: The results support the hypothesis that nitrates have a protective effect on acute ischaemic injury

Early Tracheostomy for Managing ICU Capacity During the COVID-19 Outbreak: A Propensity-Matched Cohort Study

10 p.Background: During the first wave of the COVID-19 pandemic, shortages of ventilators and ICU beds overwhelmed health care systems. Whether early tracheostomy reduces the duration of mechanical ventilation and ICU stay is controversial. Research question: Can failure-free day outcomes focused on ICU resources help to decide the optimal timing of tracheostomy in overburdened health care systems during viral epidemics? Study design and methods: This retrospective cohort study included consecutive patients with COVID-19 pneumonia who had undergone tracheostomy in 15 Spanish ICUs during the surge, when ICU occupancy modified clinician criteria to perform tracheostomy in Patients with COVID-19. We compared ventilator-free days at 28 and 60 days and ICU- and hospital bed-free days at 28 and 60 days in propensity score-matched cohorts who underwent tracheostomy at different timings (≤ 7 days, 8-10 days, and 11-14 days after intubation). Results: Of 1,939 patients admitted with COVID-19 pneumonia, 682 (35.2%) underwent tracheostomy, 382 (56%) within 14 days. Earlier tracheostomy was associated with more ventilator-free days at 28 days (≤ 7 days vs > 7 days [116 patients included in the analysis]: median, 9 days [interquartile range (IQR), 0-15 days] vs 3 days [IQR, 0-7 days]; difference between groups, 4.5 days; 95% CI, 2.3-6.7 days; 8-10 days vs > 10 days [222 patients analyzed]: 6 days [IQR, 0-10 days] vs 0 days [IQR, 0-6 days]; difference, 3.1 days; 95% CI, 1.7-4.5 days; 11-14 days vs > 14 days [318 patients analyzed]: 4 days [IQR, 0-9 days] vs 0 days [IQR, 0-2 days]; difference, 3 days; 95% CI, 2.1-3.9 days). Except hospital bed-free days at 28 days, all other end points were better with early tracheostomy. Interpretation: Optimal timing of tracheostomy may improve patient outcomes and may alleviate ICU capacity strain during the COVID-19 pandemic without increasing mortality. Tracheostomy within the first work on a ventilator in particular may improve ICU availability

Natural History of MYH7-Related Dilated Cardiomyopathy

BACKGROUND Variants in myosin heavy chain 7 (MYH7) are responsible for disease in 1% to 5% of patients with dilated cardiomyopathy (DCM); however, the clinical characteristics and natural history of MYH7-related DCM are poorly described. OBJECTIVES We sought to determine the phenotype and prognosis of MYH7-related DCM. We also evaluated the influence of variant location on phenotypic expression. METHODS We studied clinical data from 147 individuals with DCM-causing MYH7 variants (47.6% female; 35.6 +/- 19.2 years) recruited from 29 international centers. RESULTS At initial evaluation, 106 (72.1%) patients had DCM (left ventricular ejection fraction: 34.5% +/- 11.7%). Median follow-up was 4.5 years (IQR: 1.7-8.0 years), and 23.7% of carriers who were initially phenotype-negative developed DCM. Phenotypic expression by 40 and 60 years was 46% and 88%, respectively, with 18 patients (16%) first diagnosed at <18 years of age. Thirty-six percent of patients with DCM met imaging criteria for LV noncompaction. During follow-up, 28% showed left ventricular reverse remodeling. Incidence of adverse cardiac events among patients with DCM at 5 years was 11.6%, with 5 (4.6%) deaths caused by end-stage heart failure (ESHF) and 5 patients (4.6%) requiring heart transplantation. The major ventricular arrhythmia rate was low (1.0% and 2.1% at 5 years in patients with DCM and in those with LVEF of <= 35%, respectively). ESHF and major ventricular arrhythmia were significantly lower compared with LMNA-related DCM and similar to DCM caused by TTN truncating variants. CONCLUSIONS MYH7-related DCM is characterized by early age of onset, high phenotypic expression, low left ventricular reverse remodeling, and frequent progression to ESHF. Heart failure complications predominate over ventricular arrhythmias, which are rare. (C) 2022 The Authors. Published by Elsevier on behalf of the American College of Cardiology Foundation

Early mobilisation in critically ill COVID-19 patients: a subanalysis of the ESICM-initiated UNITE-COVID observational study

Background Early mobilisation (EM) is an intervention that may improve the outcome of critically ill patients. There is limited data on EM in COVID-19 patients and its use during the first pandemic wave. Methods This is a pre-planned subanalysis of the ESICM UNITE-COVID, an international multicenter observational study involving critically ill COVID-19 patients in the ICU between February 15th and May 15th, 2020. We analysed variables associated with the initiation of EM (within 72 h of ICU admission) and explored the impact of EM on mortality, ICU and hospital length of stay, as well as discharge location. Statistical analyses were done using (generalised) linear mixed-effect models and ANOVAs. Results Mobilisation data from 4190 patients from 280 ICUs in 45 countries were analysed. 1114 (26.6%) of these patients received mobilisation within 72 h after ICU admission; 3076 (73.4%) did not. In our analysis of factors associated with EM, mechanical ventilation at admission (OR 0.29; 95% CI 0.25, 0.35; p = 0.001), higher age (OR 0.99; 95% CI 0.98, 1.00; p ≤ 0.001), pre-existing asthma (OR 0.84; 95% CI 0.73, 0.98; p = 0.028), and pre-existing kidney disease (OR 0.84; 95% CI 0.71, 0.99; p = 0.036) were negatively associated with the initiation of EM. EM was associated with a higher chance of being discharged home (OR 1.31; 95% CI 1.08, 1.58; p = 0.007) but was not associated with length of stay in ICU (adj. difference 0.91 days; 95% CI − 0.47, 1.37, p = 0.34) and hospital (adj. difference 1.4 days; 95% CI − 0.62, 2.35, p = 0.24) or mortality (OR 0.88; 95% CI 0.7, 1.09, p = 0.24) when adjusted for covariates. Conclusions Our findings demonstrate that a quarter of COVID-19 patients received EM. There was no association found between EM in COVID-19 patients' ICU and hospital length of stay or mortality. However, EM in COVID-19 patients was associated with increased odds of being discharged home rather than to a care facility. Trial registration ClinicalTrials.gov: NCT04836065 (retrospectively registered April 8th 2021)

Mortality from gastrointestinal congenital anomalies at 264 hospitals in 74 low-income, middle-income, and high-income countries: a multicentre, international, prospective cohort study

Summary Background Congenital anomalies are the fifth leading cause of mortality in children younger than 5 years globally. Many gastrointestinal congenital anomalies are fatal without timely access to neonatal surgical care, but few studies have been done on these conditions in low-income and middle-income countries (LMICs). We compared outcomes of the seven most common gastrointestinal congenital anomalies in low-income, middle-income, and high-income countries globally, and identified factors associated with mortality. Methods We did a multicentre, international prospective cohort study of patients younger than 16 years, presenting to hospital for the first time with oesophageal atresia, congenital diaphragmatic hernia, intestinal atresia, gastroschisis, exomphalos, anorectal malformation, and Hirschsprung’s disease. Recruitment was of consecutive patients for a minimum of 1 month between October, 2018, and April, 2019. We collected data on patient demographics, clinical status, interventions, and outcomes using the REDCap platform. Patients were followed up for 30 days after primary intervention, or 30 days after admission if they did not receive an intervention. The primary outcome was all-cause, in-hospital mortality for all conditions combined and each condition individually, stratified by country income status. We did a complete case analysis. Findings We included 3849 patients with 3975 study conditions (560 with oesophageal atresia, 448 with congenital diaphragmatic hernia, 681 with intestinal atresia, 453 with gastroschisis, 325 with exomphalos, 991 with anorectal malformation, and 517 with Hirschsprung’s disease) from 264 hospitals (89 in high-income countries, 166 in middleincome countries, and nine in low-income countries) in 74 countries. Of the 3849 patients, 2231 (58·0%) were male. Median gestational age at birth was 38 weeks (IQR 36–39) and median bodyweight at presentation was 2·8 kg (2·3–3·3). Mortality among all patients was 37 (39·8%) of 93 in low-income countries, 583 (20·4%) of 2860 in middle-income countries, and 50 (5·6%) of 896 in high-income countries (p<0·0001 between all country income groups). Gastroschisis had the greatest difference in mortality between country income strata (nine [90·0%] of ten in lowincome countries, 97 [31·9%] of 304 in middle-income countries, and two [1·4%] of 139 in high-income countries; p≤0·0001 between all country income groups). Factors significantly associated with higher mortality for all patients combined included country income status (low-income vs high-income countries, risk ratio 2·78 [95% CI 1·88–4·11], p<0·0001; middle-income vs high-income countries, 2·11 [1·59–2·79], p<0·0001), sepsis at presentation (1·20 [1·04–1·40], p=0·016), higher American Society of Anesthesiologists (ASA) score at primary intervention (ASA 4–5 vs ASA 1–2, 1·82 [1·40–2·35], p<0·0001; ASA 3 vs ASA 1–2, 1·58, [1·30–1·92], p<0·0001]), surgical safety checklist not used (1·39 [1·02–1·90], p=0·035), and ventilation or parenteral nutrition unavailable when needed (ventilation 1·96, [1·41–2·71], p=0·0001; parenteral nutrition 1·35, [1·05–1·74], p=0·018). Administration of parenteral nutrition (0·61, [0·47–0·79], p=0·0002) and use of a peripherally inserted central catheter (0·65 [0·50–0·86], p=0·0024) or percutaneous central line (0·69 [0·48–1·00], p=0·049) were associated with lower mortality. Interpretation Unacceptable differences in mortality exist for gastrointestinal congenital anomalies between lowincome, middle-income, and high-income countries. Improving access to quality neonatal surgical care in LMICs will be vital to achieve Sustainable Development Goal 3.2 of ending preventable deaths in neonates and children younger than 5 years by 2030

Assessment and comparison of target registration accuracy in surgical instrument tracking technologies

Image guided surgery systems aim to support surgeons by providing reliable pre-operative and intra-operative imaging of the patient combined with the corresponding tracked instrument location. The image guidance is based on a combination of medical images, such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT) and Ultrasonography (US), and surgical instrument tracking. For this reason, tracking systems are of great importance as they determine location and orientation of the equipment used by surgeons. Assessment of the accuracy of these tracking systems is hence of the utmost importance to determine how much error is introduced in image guided surgery only due to tracking inaccuracy. Thus, this study aimed to compare in a surgical Operating Room (OR) accuracy of the two most used tracking systems, Optical Tracking (OT) and Electromagnetic Tracking (EMT), in terms of Target Registration Error (TRE) assessment at multiple distances from the target position. Results of the experiments show that optical tracking performs more accurately in tracking the instrument tip than electromagnetic tracking in the experimental conditions. This was tested using a phantom designed for accuracy measurement in a wide variety of positions and orientations. Nevertheless, EMT remains a viable option for flexible instruments, due to its reliability in tracking without the need for line of sight.acceptedVersio

Assessment and comparison of target registration accuracy in surgical instrument tracking technologies

Image guided surgery systems aim to support surgeons by providing reliable pre-operative and intra-operative imaging of the patient combined with the corresponding tracked instrument location. The image guidance is based on a combination of medical images, such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT) and Ultrasonography (US), and surgical instrument tracking. For this reason, tracking systems are of great importance as they determine location and orientation of the equipment used by surgeons. Assessment of the accuracy of these tracking systems is hence of the utmost importance to determine how much error is introduced in image guided surgery only due to tracking inaccuracy. Thus, this study aimed to compare in a surgical Operating Room (OR) accuracy of the two most used tracking systems, Optical Tracking (OT) and Electromagnetic Tracking (EMT), in terms of Target Registration Error (TRE) assessment at multiple distances from the target position. Results of the experiments show that optical tracking performs more accurately in tracking the instrument tip than electromagnetic tracking in the experimental conditions. This was tested using a phantom designed for accuracy measurement in a wide variety of positions and orientations. Nevertheless, EMT remains a viable option for flexible instruments, due to its reliability in tracking without the need for line of sight

FastPathology: An open-source platform for deep learning-based research and decision support in digital pathology

Deep convolutional neural networks (CNNs) are the current state-of-the-art for digital analysis of histopathological images. The large size of whole-slide microscopy images (WSIs) requires advanced memory handling to read, display and process these images. There are several open-source platforms for working with WSIs, but few support deployment of CNN models. These applications use third-party solutions for inference, making them less user-friendly and unsuitable for high-performance image analysis. To make deployment of CNNs user-friendly and feasible on low-end machines, we have developed a new platform, FastPathology, using the FAST framework and C++. It minimizes memory usage for reading and processing WSIs, deployment of CNN models, and real-time interactive visualization of results. Runtime experiments were conducted on four different use cases, using different architectures, inference engines, hardware configurations and operating systems. Memory usage for reading, visualizing, zooming and panning a WSI were measured, using FastPathology and three existing platforms. FastPathology performed similarly in terms of memory to the other C++-based application, while using considerably less than the two Java-based platforms. The choice of neural network model, inference engine, hardware and processors influenced runtime considerably. Thus, FastPathology includes all steps needed for efficient visualization and processing of WSIs in a single application, including inference of CNNs with real-time display of the results. Source code, binary releases, video demonstrations and test data can be found online on GitHub at https://github.com/SINTEFMedtek/FAST-Pathology/.publishedVersio

FastPathology: An open-source platform for deep learning-based research and decision support in digital pathology

Deep convolutional neural networks (CNNs) are the current state-of-the-art for digital analysis of histopathological images. The large size of whole-slide microscopy images (WSIs) requires advanced memory handling to read, display and process these images. There are several open-source platforms for working with WSIs, but few support deployment of CNN models. These applications use third-party solutions for inference, making them less user-friendly and unsuitable for high-performance image analysis. To make deployment of CNNs user-friendly and feasible on low-end machines, we have developed a new platform, FastPathology, using the FAST framework and C++. It minimizes memory usage for reading and processing WSIs, deployment of CNN models, and real-time interactive visualization of results. Runtime experiments were conducted on four different use cases, using different architectures, inference engines, hardware configurations and operating systems. Memory usage for reading, visualizing, zooming and panning a WSI were measured, using FastPathology and three existing platforms. FastPathology performed similarly in terms of memory to the other C++-based application, while using considerably less than the two Java-based platforms. The choice of neural network model, inference engine, hardware and processors influenced runtime considerably. Thus, FastPathology includes all steps needed for efficient visualization and processing of WSIs in a single application, including inference of CNNs with real-time display of the results. Source code, binary releases, video demonstrations and test data can be found online on GitHub at https://github.com/SINTEFMedtek/FAST-Pathology/