FROM HEART MURMUR TO ECHOCARDIOGRAPHY CONGENITAL HEART DEFECTS DIAGNOSTICS USING MACHINELEARNING ALGORITHMS

The most common clinical sign in pediatric cardiology is heart murmur, which can often be uncharacteristic. The aim of this research was to present the results of development of a classifier based on machine learning algorithms whose purpose is to classify organic murmur that occur in congenital heart defect (CHD). The study is based on the data collected at Pediatric Clinic, Clinical Center University of Sarajevo during three-year period. Totally, 116 children aged from 1 to 15 years were enrolled in the study. Input parameters for classification are parameters obtained during basic physical examination and assessment of patient. First, analysis of relevance of the feature for classification was done using InfoGain, GainRatio, Relief and Correlation method. In the second step, classifiers based on Naive Bayes, Logistic Regression, Decision Tree, Random Forest and Support Vector Machine were developed and compared by performance. The results of this research suggest that high accuracy (>90%) classifier for detection of CHD based on 16 parameters can be developed. Such classifier with appropriate user interface would be valuable diagnostic aid to doctors and pediatricians at primary healthcare level for diagnostic of heart murmurs

FROM HEART MURMUR TO ECHOCARDIOGRAPHY CONGENITAL HEART DEFECTS DIAGNOSTICS USING MACHINELEARNING ALGORITHMS

The most common clinical sign in pediatric cardiology is heart murmur, which can often be uncharacteristic. The aim of this research was to present the results of development of a classifier based on machine learning algorithms whose purpose is to classify organic murmur that occur in congenital heart defect (CHD). The study is based on the data collected at Pediatric Clinic, Clinical Center University of Sarajevo during three-year period. Totally, 116 children aged from 1 to 15 years were enrolled in the study. Input parameters for classification are parameters obtained during basic physical examination and assessment of patient. First, analysis of relevance of the feature for classification was done using InfoGain, GainRatio, Relief and Correlation method. In the second step, classifiers based on Naive Bayes, Logistic Regression, Decision Tree, Random Forest and Support Vector Machine were developed and compared by performance. The results of this research suggest that high accuracy (>90%) classifier for detection of CHD based on 16 parameters can be developed. Such classifier with appropriate user interface would be valuable diagnostic aid to doctors and pediatricians at primary healthcare level for diagnostic of heart murmurs

Strengthening of prism beam by using NSM technique with roots planted in concrete

This paper presents experimental results of four prismatic concrete reinforced beam and strengthened by NSM (Near surface mounted) FRP (Fiber Reinforced Polymer) reinforced technique, with additional roots planted in the concrete. The strengthening technique causes load capacity of beams to increase from (6%-8%).A decrease in mid-span deflection was also observed from (4%-5%).Using this technique gave increasing in flexural beam resistant under the same conditions and this increasing was also noted in shear beam resistant

Pulmonary Auscultation using Mobile Devices - Feasibility Study in Respiratory Diseases

A auscultação pulmonar convencional é essencial no controlo das doenças respiratórias. Contudo, a deteção de sons adventícios fora do ambiente hospitalar continua a ser um desafio. Nós estudámos a exequibilidade de realizar auscultação com o microfone incorporado de um smartphone em contexto clínico. Noventa e cinco pacientes (mediana[intervalo interquartil] 16[11-24] anos; 52% mulheres; 42 fibrose quística, 24 asma, 17 outras doenças respiratórias e 12 sem doença respiratória) foram recrutados nos serviços de Pediatria e Pneumologia de um hospital terciário. Os clínicos realizaram auscultação convencional em 4 locais (traqueia, peito anterior direito e bases pulmonares direita e esquerda), documentando quaisquer sons adventícios. A auscultação com o smartphone foi gravada nos mesmos locais. As gravações (n=738) foram classificadas por dois investigadores e o acordo calculado (%; kappa de Cohen(IC95%)). Foram obtidas gravações com qualidade em 88% dos participantes e 69% das gravações (91%; k=0.80(IC95% 0.75-0.85)), com uma proporção de qualidade superior na traqueia (79%) e inferior no grupo da asma (52%). Foram encontrados sons adventícios em apenas 27% dos participantes e 12% das gravações (91%; k=0.57(IC95% 0.46-0.68)), o que poderá ter contribuído para o acordo razoável entre a auscultação convencional e a auscultação com o smartphone (86%; k=0.25(IC95% 0.13-0.37)). Os nossos resultados demonstram que a auscultação com o smartphone foi exequível, mas que é necessária mais investigação para melhorar o seu acordo com a auscultação convencional.Conventional lung auscultation is essential in the management of respiratory diseases. However, detecting adventitious sounds outside medical facilities remains challenging. We assessed the feasibility of lung auscultation using the smartphone's embedded microphone in real-world clinical practice. Ninety-five patients (median[interquartile range] 16[11-24]y; 52% female; 42 cystic fibrosis, 24 asthma, 17 other respiratory diseases and 12 no respiratory diseases) were re-cruited at Pediatrics and Pulmonology departments of a tertiary hospital. Clinicians performed conventional auscultation at 4 locations (trachea, right anterior chest, right and left lung bases), documenting any adventitious sounds. Smartphone auscultation was recorded in the same loca-tions. The recordings (n=738) were classified by two annotators and agreement calculated (%; Cohen's k(95%CI)). Recordings with quality were obtained in 88% of the participants and 69% of the recordings (91%; k=0.80(95%CI 0.75-0.85)), with the quality proportion being higher at the trachea (79%) and lower in the asthma group (52%). Adventitious sounds were present in only 27% of the participants and 12% of the recordings (91%; k=0.57(95%CI 0.46-0.68)), which may have contributed to the fair agreement between conventional and smartphone auscultation (86%; k=0.25(95%CI 0.13-0.37)). Our results show that smartphone auscultation was feasible, but further investigation is required to improve its agreement with conventional auscultation