ОЦЕНКА ДИАГНОСТИЧЕСКОЙ ТОЧНОСТИ СИСТЕМЫ СКРИНИНГА ТУБЕРКУЛЕЗА ЛЕГКИХ НА ОСНОВЕ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА

The objective of the study: to evaluate the applicability of the automated system for detection of chest diseases during a regular mass screening of the population through assessment of universe parameters of diagnostic accuracy.Subjects and methods. A retrospective diagnostic study was conducted. The index-test (the method being studied) implied distinction and analysis of X-ray films using the software based on convolutional neural networks of U-NET type, which were modified and trained for specific purposes. The reference method used was the double revision of the previously classified X-ray films by two qualified roentgenologists with work experience of 8-10 years. Two depersonalized samplings of digital X-ray films were used: Sample 1 (n = 140), the ratio of the norm and pathology made 50 : 50; Sample 2 (n = 150), the ratio of the norm and pathology made 95 : 5.Results. The following parameters were set up for Samples 1 and 2 respectively: sensitivity ‒ 87.2 and 75.0%, specificity ‒ 60.0 and 53.5%, the prognostic value of the positive result ‒ 68.6 and 8.3%, the prognostic value of the negative result ‒ 82.4 and 97.5%, the area under characteristic curve ‒ 0.74 and 0.64.Conclusions. The index test can be used only for mass regular screening in the population with low pre-test chances of pathology, which is confirmed by the prognostic value of the negative result (97.5%). This technology was recommended for the semiautomatic formation of pulmonary tuberculosis risk groups for consequent verification of the results by a roentgenologist.Цель исследования: оценить применимость системы автоматизированного выявления заболеваний органов грудной клетки для массовых периодических осмотров населения путем вычисления совокупности параметров диагностической точности.Материалы и методы. Проведено ретроспективное диагностическое исследование. Индекс-тест (исследуемый метод) – распознавание и анализ рентгенограмм посредством программного продукта на основе сверточных нейронных сетей типа U-NET, модифицированных и обученных специальным образом. Референсный метод – двойной пересмотр ранее классифицированных рентгенограмм двумя квалифицированными врачами-рентгенологами со стажем работы 8-10 лет. Использованы две деперсонализированные выборки цифровых флюорограмм: 1 (n = 140), соотношение норма : патология ‒ 50 : 50; 2 (n = 150), соотношение норма : патология ‒ 95 : 5.Результаты. Установлены параметры для выборок 1 и 2 соответственно: чувствительность ‒ 87,2 и 75,0%, специфичность ‒ 60,0 и 53,5%, прогностическая ценность положительного результата ‒ 68,6 и 8,3%, отрицательного ‒ 82,4 и 97,5%, площадь под характеристической кривой ‒ 0,74 и 0,64.Выводы. Индекс-тест применим только для массовых периодических осмотров в популяциях с низкой претестовой вероятностью наличия патологии, что подтверждается значением прогностической ценности отрицательного результата (97,5%). Технология может быть рекомендована для полуавтоматизированного формирования групп риска по туберкулезу легких для последующей верификации результатов врачом-рентгенологом

Deep learning classification of chest x-ray images

We propose a deep learning based method for classification of commonly occurring pathologies in chest X-ray images. The vast number of publicly available chest X-ray images provides the data necessary for successfully employing deep learning methodologies to reduce the misdiagnosis of thoracic diseases. We applied our method to the classification of two example pathologies, pulmonary nodules and cardiomegaly, and we compared the performance of our method to three existing methods. The results show an improvement in AUC for detection of nodules and cardiomegaly compared to the existing methods.Comment: 4 pages, 4 figures, 2 tables, conference , SSIAI 202

ОЦЕНКА ДИАГНОСТИЧЕСКОЙ ТОЧНОСТИ СИСТЕМЫ СКРИНИНГА ТУБЕРКУЛЕЗА ЛЕГКИХ НА ОСНОВЕ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА

The objective of the study: to evaluate the applicability of the automated system for detection of chest diseases during a regular mass screening of the population through assessment of universe parameters of diagnostic accuracy.Subjects and methods. A retrospective diagnostic study was conducted. The index-test (the method being studied) implied distinction and analysis of X-ray films using the software based on convolutional neural networks of U-NET type, which were modified and trained for specific purposes. The reference method used was the double revision of the previously classified X-ray films by two qualified roentgenologists with work experience of 8-10 years. Two depersonalized samplings of digital X-ray films were used: Sample 1 (n = 140), the ratio of the norm and pathology made 50 : 50; Sample 2 (n = 150), the ratio of the norm and pathology made 95 : 5.Results. The following parameters were set up for Samples 1 and 2 respectively: sensitivity ‒ 87.2 and 75.0%, specificity ‒ 60.0 and 53.5%, the prognostic value of the positive result ‒ 68.6 and 8.3%, the prognostic value of the negative result ‒ 82.4 and 97.5%, the area under characteristic curve ‒ 0.74 and 0.64.Conclusions. The index test can be used only for mass regular screening in the population with low pre-test chances of pathology, which is confirmed by the prognostic value of the negative result (97.5%). This technology was recommended for the semiautomatic formation of pulmonary tuberculosis risk groups for consequent verification of the results by a roentgenologist.Цель исследования: оценить применимость системы автоматизированного выявления заболеваний органов грудной клетки для массовых периодических осмотров населения путем вычисления совокупности параметров диагностической точности.Материалы и методы. Проведено ретроспективное диагностическое исследование. Индекс-тест (исследуемый метод) – распознавание и анализ рентгенограмм посредством программного продукта на основе сверточных нейронных сетей типа U-NET, модифицированных и обученных специальным образом. Референсный метод – двойной пересмотр ранее классифицированных рентгенограмм двумя квалифицированными врачами-рентгенологами со стажем работы 8-10 лет. Использованы две деперсонализированные выборки цифровых флюорограмм: 1 (n = 140), соотношение норма : патология ‒ 50 : 50; 2 (n = 150), соотношение норма : патология ‒ 95 : 5.Результаты. Установлены параметры для выборок 1 и 2 соответственно: чувствительность ‒ 87,2 и 75,0%, специфичность ‒ 60,0 и 53,5%, прогностическая ценность положительного результата ‒ 68,6 и 8,3%, отрицательного ‒ 82,4 и 97,5%, площадь под характеристической кривой ‒ 0,74 и 0,64.Выводы. Индекс-тест применим только для массовых периодических осмотров в популяциях с низкой претестовой вероятностью наличия патологии, что подтверждается значением прогностической ценности отрицательного результата (97,5%). Технология может быть рекомендована для полуавтоматизированного формирования групп риска по туберкулезу легких для последующей верификации результатов врачом-рентгенологом

Клинические аспекты применения искусственного интеллекта для интерпретации рентгенограмм органов грудной клетки

The review considers the possible use of artificial intelligence for the interpretation of chest X-rays by analyzing 45 publications. Experimental and commercial diagnostic systems for pulmonary tuberculosis, pneumonia, neoplasms and other diseases have been analyzed.В обзоре рассмотрены возможности применения искусственного интеллекта для интерпретации рентгенограмм органов грудной клетки путем анализа 45 литературных источников. Проанализированы экспериментальные и коммерческие системы диагностики туберкулеза легких, пневмоний, новообразований и других заболеваний

A statistical approach on pulmonary tuberculosis detection system based on X-ray image

This paper presented the research result on the design of pulmonary TB (Tuberculosis) detection systems using a statistical approach. The study aimed to address two problems in detecting pulmonary TB by doctors, especially in remote areas of Indonesia, namely the long waiting time for patients to get the doctor's diagnosis and the doctor's subjectivity. We used hundreds of X-ray images from radiology department of Sardjito Hospital, Yogyakarta, as primary data and thirty data from various sources on the internet as secondary data. Using statistical approach, we exploited statistical image feature from image histogram, examined two statistical methods of PCA and LDA transformation for feature extraction, and two minimum distance classifier in image classification. We also used histogram equalization in the image enhancement process and bicubic interpolation in image segmentation and template making. Test results on primary and secondary data images show the identification accuracy of 94% and 83.3%, respectively

A Deep Learning Based Suggested Model to Detect Necrotising Enterocolitis in Abdominal Radiography Images

Despite decades of exploration into necrotising enterocolitis (NEC), we still lack the capacity to accurately diagnose the disease to improve outcomes in its management. Existing diagnostics struggle to delineate NEC from other neonatal intestinal diseases; it is also unable to highlight those likely to deteriorate to needing emergency life-saving surgery before it is too late. The diagnosis of NEC is heavily dependent on interpretation of radiological findings, especially abdominal radiography (AR) and abdominal ultrasound (AUS). Interexpert variability in interpreting AR imaging, and in the case of AUS, performing and interpreting the test, remains an unresolved challenge. With the compounding impact of the shrinking radiology workforce, a novel approach is imperative. Computer assisted detection (CAD) and classification of abnormal pathology in medical imaging is a rapidly evolving field of clinical and biomedical research. This technology is widely used as a preliminary screening tool. This research paper proposes a deep learning-based model to classify AR images in an automated manner, generating class activation maps (CAM) from various imaging features consistent with NEC pathology, as agreed by expert consensus papers (in neonatology and paediatric radiology). It also compares it with conventional machine learning methods. The suggested model aims to produce heatmaps for various imaging features to highlight NEC pathology in AR (or in future AUS). Once the model is trained, validation is done through quantitative measures and visually by the attending radiologist (clinician) reviewing the validity of the colour maps highlighting the pathology of the AR image (future extension to AUS). As the volume of imaging data is increasing year by year, CAD can be a key strategy to assist radiology departments meet service needs. This technology can greatly assist in screening for NEC, improving the detection of NEC and potentially aid in the earlier identification of disease. Furthermore, it can fast track research cost effectively by creating big data through the automatic labeling of imaging data to create big-data for NEC databases