Ulcerative Colitis Mayo Endoscopic Scoring Classification with Active Learning and Generative Data Augmentation

Endoscopic imaging is commonly used to diagnose Ulcerative Colitis (UC) and classify its severity. It has been shown that deep learning based methods are effective in automated analysis of these images and can potentially be used to aid medical doctors. Unleashing the full potential of these methods depends on the availability of large amount of labeled images; however, obtaining and labeling these images are quite challenging. In this paper, we propose a active learning based generative augmentation method. The method involves generating a large number of synthetic samples by training using a small dataset consisting of real endoscopic images. The resulting data pool is narrowed down by using active learning methods to select the most informative samples, which are then used to train a classifier. We demonstrate the effectiveness of our method through experiments on a publicly available endoscopic image dataset. The results show that using synthesized samples in conjunction with active learning leads to improved classification performance compared to using only the original labeled examples and the baseline classification performance of 68.1% increases to 74.5% in terms of Quadratic Weighted Kappa (QWK) Score. Another observation is that, attaining equivalent performance using only real data necessitated three times higher number of images.Comment: 6 pages, 3 figures, to be published in IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 202

Diagnosis of comorbid migraine without aura in patients with idiopathic/genetic epilepsy based on the gray zone approach to the International Classification of Headache Disorders 3 criteria

BackgroundMigraine without aura (MwoA) is a very frequent and remarkable comorbidity in patients with idiopathic/genetic epilepsy (I/GE). Frequently in clinical practice, diagnosis of MwoA may be challenging despite the guidance of current diagnostic criteria of the International Classification of Headache Disorders 3 (ICHD-3). In this study, we aimed to disclose the diagnostic gaps in the diagnosis of comorbid MwoA, using a zone concept, in patients with I/GEs with headaches who were diagnosed by an experienced headache expert.MethodsIn this multicenter study including 809 consecutive patients with a diagnosis of I/GE with or without headache, 163 patients who were diagnosed by an experienced headache expert as having a comorbid MwoA were reevaluated. Eligible patients were divided into three subgroups, namely, full diagnosis, zone I, and zone II according to their status of fulfilling the ICHD-3 criteria. A Classification and Regression Tree (CART) analysis was performed to bring out the meaningful predictors when evaluating patients with I/GEs for MwoA comorbidity, using the variables that were significant in the univariate analysis.ResultsLonger headache duration (<4 h) followed by throbbing pain, higher visual analog scale (VAS) scores, increase of pain by physical activity, nausea/vomiting, and photophobia and/or phonophobia are the main distinguishing clinical characteristics of comorbid MwoA in patients with I/GE, for being classified in the full diagnosis group. Despite being not a part of the main ICHD-3 criteria, the presence of associated symptoms mainly osmophobia and also vertigo/dizziness had the distinguishing capability of being classified into zone subgroups. The most common epilepsy syndromes fulfilling full diagnosis criteria (n = 62) in the CART analysis were 48.39% Juvenile myoclonic epilepsy followed by 25.81% epilepsy with generalized tonic-clonic seizures alone.ConclusionLonger headache duration, throbbing pain, increase of pain by physical activity, photophobia and/or phonophobia, presence of vertigo/dizziness, osmophobia, and higher VAS scores are the main supportive associated factors when applying the ICHD-3 criteria for the comorbid MwoA diagnosis in patients with I/GEs. Evaluating these characteristics could be helpful to close the diagnostic gaps in everyday clinical practice and fasten the diagnostic process of comorbid MwoA in patients with I/GEs

Comparison of Antibiotic Resistance Patterns of Microorganisms Causing Acute Pyelonephritis in Children at 5-year Interval

Objective: Urinary tract infections (UTIs) are among the most common bacterial infections in children. Selection of empirical antibiotic therapy is based on patient characteristics and regional antibiotic resistance patterns. Antibiotic resistance driven by inappropriate antibiotic use remains currently one of the major public health concerns. The aim of this study was to compare the microbiological spectrum of pediatric acute pyelonephritis and antimicrobial resistance patterns in two time periods 5 years apart.Method: Clinical characteristics, treatment modalities, causative uropathogens in urine cultures, antibiotic susceptibility and resistance patterns of the patients with acute acute pyelonephritis were compared between the two time periods.Results: Group 1 consisted of 86 children (mean age 3.52 +/- 0.4 years, 32 boys) hospitalized, and treated for acute pyelonephritis between 2012-2013; Group 2 included 72 children (mean age 3.78 +/- 0.7 years, 25 boys) between 2017-2018. Escherichia coli was the most common microorganism in both groups. The most frequently used antibiotics for pyelonephritis treatment in both groups were amikacin (55% vs 51%) and ceftriaxone (33% vs 37%), gentamicin (5% vs 22%) While 77% of the children in Group 1 used prophylactic antibiotics, this rate was significantly lower with 23% in Group 2. Resistance to ampicillin, cefepime and ceftriaxone were significantly lower in Group 2. Ceftriaxone resistance which created concerns in recent years regressed from 60% to 37%.Conclusion: Our study revealed significant reductions in rates of resistance to several antibiotics, particularly ceftriaxone within 5 year-period. Possible explanations for these results may be that aminoglycosides are preferred more frequently than ceftriaxone therapy, prophylactic treatment is limited in selected cases, and cephalosporins are not used for prophylaxis. We believe that rational empirical antibiotic selection will prevent the development of resistance in urinary tract infections

An algorithm for automatic detection of repeater F-waves and MUNE studies

Artuğ, Necdet Tuğrul (Arel Author)The present study aims to develop an algorithm and software that automatically detects repeater F-waves which are very difficult to analyze when elicited as high number of recordings in motor unit number estimation studies. The main strategy of the study was to take the repeater F waves discriminated by the neurologist, from limited number of recordings, as the gold standard and to test the conformity of the results of the new automated method. Ten patients with ALS and ten healthy controls were evaluated. 90 F-waves with supramaximal stimuli and 300 F-waves with submaximal stimuli were recorded. Supramaximal recordings were evaluated both manually by an expert neurologist and automatically by the developed software to test the performance of the algorithm. The results both acquired from the neurologist and from the software were found compatible. Therefore, the main expected impact of the present study is to make the analysis of repeater F waves easier primarily in motor unit number estimation studies, since there is currently a continuing need for such automated programs in clinical neurophysiology. Submaximal recordings were examined only by the developed software. The extracted features were: maximum M response amplitude, mean power of M response, mean of sMUP values, MUNE value, number of baskets, persistence of F-waves, persistence of repeater F-waves, mean of F-waves' powers, median of F-waves' powers. Feature selection methods were also applied to determine the most valuable features. Various classifiers such as multi-layer perceptron (MLP), radial basis function network (RBF), support vector machines (SVM) and k nearest neighbors (k-NN) were tested to differentiate two classes. Initially all features, then decreased numbers of features after feature selection process were applied to the aforementioned classifiers. The classification performance usually increased when decreased features were applied to intelligent systems. Ulnar recordings under submaximal stimulation showed better performance when compared with supramaximal equivalents or median nerve equivalents. The highest performance was obtained as 90% with k-NN algorithm which was a committee decision based classifier. This result was achieved with only two features, namely mean of sMUP amplitude and MUNE value

Automatic Analysis of CMAP Scan Data on Healthy Controls and Motor Neuron Patients

#nofulltext# --- Artuğ, Tuğrul (Arel Author)In this study, motor response recordings were acquired from thenar and hypothenar muscles of poliomyelitis survivors, ALS patients and healthy participants by using CMAP Scan method. CMAP Scan curve was plotted by using 500 stimuli between minimum and maximum stimulus intensity. Automatic analysis software was developed with MATLAB for calculating CMAP Scan parameters. Statistical results revealed that step%, D50 and returner% values can differentiate healthy individuals from the patients. The developed software helps clinicians for following up the progression rate of the diseases which cause anterior horn cell degeneration