More slices, less truth: effects of different test-set design strategies for magnetic resonance image classification

Aim To assess the effects of different test-set design strategies for magnetic resonance (MR) image classification using deep learning. Methods Error rates in 10 experimental settings were assessed. The performance of pretrained models and data augmentation were examined as possible contributing factors. Results Error rates in experimental settings using MR images of different patients for training and test sets were ten times higher than those in experimental settings using MR images of the same patients (four disease groups with whole-chest images, 46.80% vs 2.06%; four disease groups without whole-chest images, 49.09% vs 1.29%; sex classification with whole-chest images, 16.02% vs 0.96%; and sex classification without whole-chest images, 23.56% vs 0.30%). Error rates were higher when data augmentation was applied to settings that used MR images of different patients for training and test sets. Conclusion When deep learning is applied to MR image classification, training and test sets should consist of MR images of different patients. Models built on training and test sets consisting of images of the same patients yield optimistic error rates and lead to wrong conclusions. MR images of neighboring slices are so similar that they cause data leakage effect

Akutno bubrežno oštećenje nakon operacija na otvorenom srcu

Cardiac surgery-associated acute kidney injury (CS-AKI) is a major complication associated with increased morbidity and mortality. There are multiple diagnostic criteria for CS-AKI. Despite many new investigations available for improved AKI diagnostics, creatinine and urea remain the cornerstone of diagnostics in everyday clinical practice. There are three major pathophysiological mechanisms that contribute to kidney injury, i.e. renal hypoperfusion, inflammation with oxidative stress, and use of nephrotoxic agents. Some risk factors have been identified that can be modified during the course of treatment (use of nephrotoxic agents, duration of cardiopulmonary bypass, type of extracorporeal circulation, postoperative low cardiac output or hypotension). The aim of AKI prevention should always be to prevent aggravation of renal failure and, if possible, to avoid progression to renal replacement therapy, which in turn brings worse long-term outcomes.Akutno bubrežno oštećenje povezano s kardiokirurgijom (cardiac surgery-associated acute kidney injury, CS-AKI) je značajna komplikacija s visokim pobolom i smrtnošću. Postoji više dijagnostičkih kriterija za dijagnozu CS-AKI. Usprkos mnogim novim istraživanjima, kreatinin i ureja ostaju temelj dijagnostike. Glavni patofiziološki procesi koji doprinose bubrežnom oštećenju su bubrežna hipoperfuzija, upala uzrokovana oksidativnim stresom i bubrežno oštećenje uzrokovano upotrebom nefrotoksičnih sredstava. Tijekom terapije može se utjecati na nekoliko čimbenika rizika (upotreba nefrotoksičnih sredstava, trajanje kardiopulmonarne premosnice, tip izvantjelesne cirkulacije, smanjeni minutni volumen ili poslijeoperacijska hipotenzija). Cilj prevencije AKI je spriječiti progresiju bubrežnog oštećenja koje zahtijeva kompleksniju terapiju i donosi lošije dugoročne ishode

Subtypes and Mechanisms of Hypertrophic Cardiomyopathy Proposed by Machine Learning Algorithms

Hypertrophic cardiomyopathy (HCM) is a relatively common inherited cardiac disease that results in left ventricular hypertrophy. Machine learning uses algorithms to study patterns in data and develop models able to make predictions. The aim of this study is to identify HCM subtypes and examine the mechanisms of HCM using machine learning algorithms. Clinical and laboratory findings of 143 adult patients with a confirmed diagnosis of nonobstructive HCM are analyzed; HCM subtypes are determined by clustering, while the presence of different HCM features is predicted in classification machine learning tasks. Four clusters are determined as the optimal number of clusters for this dataset. Models that can predict the presence of particular HCM features from other genotypic and phenotypic information are generated, and subsets of features sufficient to predict the presence of other features of HCM are determined. This research proposes four subtypes of HCM assessed by machine learning algorithms and based on the overall phenotypic expression of the participants of the study. The identified subsets of features sufficient to determine the presence of particular HCM aspects could provide deeper insights into the mechanisms of HCM

Semiautomatic epicardial fat segmentation based on fuzzy c-means clustering and geometric ellipse fitting

Automatic segmentation of particular heart parts plays an important role in recognition tasks, which is utilized for diagnosis and treatment. One particularly important application is segmentation of epicardial fat (surrounds the heart), which is shown by various studies to indicate risk level for developing various cardiovascular diseases as well as to predict progression of certain diseases. Quantification of epicardial fat from CT images requires advance image segmentation methods. The problem of the state-of-the-art methods for epicardial fat segmentation is their high dependency on user interaction, resulting in low reproducibility of studies and time-consuming analysis. We propose in this paper a novel semiautomatic approach for segmentation and quantification of epicardial fat from 3D CT images. Our method is a semisupervised slice-by-slice segmentation approach based on local adaptive morphology and fuzzy c-means clustering. Additionally, we use a geometric ellipse prior to filter out undesired parts of the target cluster. The validation of the proposed methodology shows good correspondence between the segmentation results and the manual segmentation performed by physicians

Učinak dipping profila gestacijske hipertenzije na majčine simptome i fizikalne nalaze, porođajnu težinu i prijevremeni porođaj

The study aimed to determine if the non-dipping pattern of blood pressure (BP) influences preterm delivery in gestational hypertension (GH), but also maternal clinical findings and birth weight. Sixty women with GH, i.e. 30 women with a dipping BP profile (control group) and 30 non-dippers (study group), were included in the study. Echocardiography was performed in all subjects, as well as ambulatory blood pressure monitoring (ABPM) during third trimester. ABPM was repeated 6-8 weeks after delivery. Thirteen women with preterm delivery were classified as non-dippers and only four as dippers (p=0.01). The average and peak systolic and diastolic night-time BP had negative linear correlation with birth weight (p<0.0005). Total vascular resistance (p<0.0005) and mass index (p=0.014) were significantly higher as compared with women with term delivery, while ejection fraction (EF) (p=0.007) and circumferential systolic velocity (p=0.042) were significantly reduced in the preterm delivery group. Multivariate binary logistic regression identified the average night-time systolic BP, left ventricular mass index and EF as independent predictors of preterm delivery. Study results suggested a relationship of the non-dipping BP pattern in GH with preterm delivery, birth weight, and maternal clinical findings.Cilj ovoga istraživanja bio je utvrditi povezanost non-dipping profila krvnog tlaka (KT) s prijevremenim porođajem, porođajnom težinom novorođenčeta te kliničkim i ehokardiografskim parametrima kod žena s gestacijskom hipertenzijom (GH). Istraživanje je obuhvatilo 60 žena s GH, 30 s dipping profilom KT (kontrolna skupina) i 30 non-dippera (ispitna skupina). Sve žene podvrgnute su kompletnoj ehokardiografiji i 24-satnom ambulantnom praćenju krvnog tlaka (ambulatory blood pressure monitoring, ABPM) tijekom trećeg trimestra, a ABPM je ponovljen 6-8 tjedana nakon porođaja. Ukupno 17 žena imalo je prijevremeni porođaj. Trinaest žena s prijevremenim porođajem imalo je non-dipping profil KT, dok su samo četiri žene imale dipping profil KT (0,01). Prosječni i maksimalni sistolički i dijastolički noćni KT imali su negativnu linearnu korelaciju s porođajnom težinom (p<0,0005). Ukupna vaskularna rezistencija (p<0,0005) i indeks mase miokarda lijeve klijetke (p=0,014) bili su znatno viši u skupini žena s prijevremenim porođajem, dok su parametri sistoličke funkcije, tj. ejekcijska frakcija (EF) (p=0,007) i brzina cirkumferentnog skraćenja miokarda lijevog ventrikla (p=0,042) bili statistički značajno sniženi u skupini žena s prijevremenim porođajem. Multivarijatna regresijska analiza pokazala je da su prosječni noćni sistolički KT, indeks mase lijevog ventrikla i EF identificirani kao nezavisni prediktori prijevremenog porođaja. Rezultati istraživanja pokazali su da postoji povezanost između non-dipping profila KT s prijevremenim porođajem, porođajnom težinom novorođenčeta i poremećajem hemodinamskog statusa majke u GH