Machine Learning Algorithms for Classification of Microcirculation Images from Septic and Non-Septic Patients

Sepsis is a life-threatening disease and one of the major causes of death in hospitals. Imaging of microcirculatory dysfunction is a promising approach for automated diagnosis of sepsis. We report a machine learning classifier capable of distinguishing non-septic and septic images from dark field microcirculation videos of patients. The classifier achieves an accuracy of 89.45%. The area under the receiver operating characteristics of the classifier was 0.92, the precision was 0.92 and the recall was 0.84. Codes representing the learned feature space of trained classifier were visualized using t-SNE embedding and were separable and distinguished between images from critically ill and non-septic patients. Using an unsupervised convolutional autoencoder, independent of the clinical diagnosis, we also report clustering of learned features from a compressed representation associated with healthy images and those with microcirculatory dysfunction. The feature space used by our trained classifier to distinguish between images from septic and non-septic patients has potential diagnostic application.Comment: Accepted for publication at 2018 IEEE International Conference on Machine Learning and Applications (IEEE ICMLA

Vacuum therapy in complex treatment of patients with odontogenic inflammatory process of the maxillofacial area and neck

ВАКУУМНАЯ ТЕРАПИЯРАНЫ, ХИРУРГИЧЕСКАЯ ОБРАБОТКАРАНЫ ЗАЖИВЛЕНИЕРЕГЕНЕРАЦИЯЧЕЛЮСТНО-ЛИЦЕВЫЕ ХИРУРГИЧЕСКИЕ ОПЕРАЦИИОСТЕОМИЕЛИТКОСТЕЙ БОЛЕЗНИ ИНФЕКЦИОННЫЕМИКРОЦИРКУЛЯЦИЯЦель. Оценить эффективность применения вакуумной системы в комплексном лечении пациентов с одонтогенными инфекционно-воспалительными заболеваниями челюстно-лицевой области и шеи. Материал и методы. Проведено обследование 178 пациентов с острым одонтогенным остеомиелитом челюсти, осложненным флегмоной прилежащих клетчаточных пространств. Для оказания помощи пациентам основной группы дополнительно использовалась система для вакуумной терапии ран. Пациентам группы сравнения лечение проводилось с использованием стандартных методов. Группу контроля составили 50 здоровых лиц. Деформируемость эритроцитов (ДЭ) оценивали по времени прохождения их суспензии стандартного расстояния по пористому фильтру. Адгезию лейкоцитарно-тромбоцитарной суспензии (ЛТС) исследовали, регистрируя изменения светопропускания суспензии лейкоцитов до и после инкубации вместе с волокнистым субстратом с помощью агрегометра АР 2110 "СОЛАР". Результаты. При включении в лечебный комплекс вакуумной терапии в более короткие сроки (5 (5; 6) суток) купировалась боль при пальпации очага воспаления, на 7-е (5; 8) сутки восстанавливалась конфигурация лица, гиперемия кожи купировалась на 5-е (5; 6) сутки, гнойная экссудация из раны прекращалась на 6-е (5; 7) сутки, создавались благоприятные условия для начала формирования грануляций на 7-е (6; 8) сутки. Выявлено снижение продолжительности лечения (9 (8; 10) суток) основной группы пациентов относительно пациентов группы сравнения – 10 (8;12) суток. Вакуумная терапия при завершении лечения способствует снижению повышенных в начале лечения показателей микроциркуляции до уровня здоровых лиц. Заключение. Применение вакуумной терапии в лечении пациентов с острым одонтогенным остеомиелитом челюсти, осложненным флегмоной прилежащих клетчаточных пространств, способствует сокращению сроков заживления раны и уменьшению продолжительности лечения с 10 (8; 12) до 9 (8; 10) суток. Выявлена нормализация скорости и степени агрегации ЛТС, ДЭ в плазме крови при завершении лечения пациентов с использованием отрицательного давления.Objective. To evaluate the effectiveness of the vacuum system in the complex treatment of patients with odontogenic inflammatory diseases of the maxillofacial area and neck. Methods. The examination of 178 patients with acute odontogenic osteomyelitis of the jaw, complicated by phlegmon of the adjacent tissue spaces, was conducted. To manage the patients of the main group vacuum system was additionally used. Patients in the comparison group were treated using standard methods. The control group consisted of 50 healthy individuals. The erythrocyte deformability (ED) was evaluated by the time of their suspension passing of a standard distance through a porous filter. The adhesion of leukocyte-platelet suspension (LTS) was investigated by recording the changes in the light transmission of the leukocyte’s suspension before and after incubation with a fibrous substrate using the AR 2110 "SOLARф" aggregometer. Results. When vacuum therapy was included in the medical complex, in shorter terms (5 (5-6) days) the pain during palpation of the inflammatory focus was stopped; the face configuration was restored on the 7 (5-8) day; the skin redness was stopped on the 5 (5-6) day; purulent exudation from the wound was stopped on the 6 (5-7) day; favorable conditions were created for the beginning of the formation of granulations on the 7 (6-8) day. A decrease in treatment terms (9 (8-10) days) of the main group of patients in relation to the duration of treatment in patients with standard complex treatment (10 (8-12) days) was revealed. Vacuum therapy at the end of treatment helps to reduce microcirculation indices elevated at the beginning of treatment to the level of healthy individuals. Conclusions. The use of vacuum therapy in the treatment of patients with acute odontogenic osteomyelitis of the jaw, complicated by phlegmon of the adjacent tissue spaces, reduces the wound healing terms and reduces the duration of treatment from 10 (8; 12) days to 9 (8; 10) days. Normalization of the rate and degree of aggregation of LTS, DE in the blood plasma was revealed at the end of patients’ treatment using negative pressure

Vacuum therapy in complex treatment of patients with odontogenic inflammatory process of the maxillofacial area and neck

ВАКУУМНАЯ ТЕРАПИЯРАНЫ, ХИРУРГИЧЕСКАЯ ОБРАБОТКАРАНЫ ЗАЖИВЛЕНИЕРЕГЕНЕРАЦИЯЧЕЛЮСТНО-ЛИЦЕВЫЕ ХИРУРГИЧЕСКИЕ ОПЕРАЦИИОСТЕОМИЕЛИТКОСТЕЙ БОЛЕЗНИ ИНФЕКЦИОННЫЕМИКРОЦИРКУЛЯЦИЯЦель. Оценить эффективность применения вакуумной системы в комплексном лечении пациентов с одонтогенными инфекционно-воспалительными заболеваниями челюстно-лицевой области и шеи. Материал и методы. Проведено обследование 178 пациентов с острым одонтогенным остеомиелитом челюсти, осложненным флегмоной прилежащих клетчаточных пространств. Для оказания помощи пациентам основной группы дополнительно использовалась система для вакуумной терапии ран. Пациентам группы сравнения лечение проводилось с использованием стандартных методов. Группу контроля составили 50 здоровых лиц. Деформируемость эритроцитов (ДЭ) оценивали по времени прохождения их суспензии стандартного расстояния по пористому фильтру. Адгезию лейкоцитарно-тромбоцитарной суспензии (ЛТС) исследовали, регистрируя изменения светопропускания суспензии лейкоцитов до и после инкубации вместе с волокнистым субстратом с помощью агрегометра АР 2110 "СОЛАР". Результаты. При включении в лечебный комплекс вакуумной терапии в более короткие сроки (5 (5; 6) суток) купировалась боль при пальпации очага воспаления, на 7-е (5; 8) сутки восстанавливалась конфигурация лица, гиперемия кожи купировалась на 5-е (5; 6) сутки, гнойная экссудация из раны прекращалась на 6-е (5; 7) сутки, создавались благоприятные условия для начала формирования грануляций на 7-е (6; 8) сутки. Выявлено снижение продолжительности лечения (9 (8; 10) суток) основной группы пациентов относительно пациентов группы сравнения – 10 (8;12) суток. Вакуумная терапия при завершении лечения способствует снижению повышенных в начале лечения показателей микроциркуляции до уровня здоровых лиц. Заключение. Применение вакуумной терапии в лечении пациентов с острым одонтогенным остеомиелитом челюсти, осложненным флегмоной прилежащих клетчаточных пространств, способствует сокращению сроков заживления раны и уменьшению продолжительности лечения с 10 (8; 12) до 9 (8; 10) суток. Выявлена нормализация скорости и степени агрегации ЛТС, ДЭ в плазме крови при завершении лечения пациентов с использованием отрицательного давления.Objective. To evaluate the effectiveness of the vacuum system in the complex treatment of patients with odontogenic inflammatory diseases of the maxillofacial area and neck. Methods. The examination of 178 patients with acute odontogenic osteomyelitis of the jaw, complicated by phlegmon of the adjacent tissue spaces, was conducted. To manage the patients of the main group vacuum system was additionally used. Patients in the comparison group were treated using standard methods. The control group consisted of 50 healthy individuals. The erythrocyte deformability (ED) was evaluated by the time of their suspension passing of a standard distance through a porous filter. The adhesion of leukocyte-platelet suspension (LTS) was investigated by recording the changes in the light transmission of the leukocyte’s suspension before and after incubation with a fibrous substrate using the AR 2110 "SOLARф" aggregometer. Results. When vacuum therapy was included in the medical complex, in shorter terms (5 (5-6) days) the pain during palpation of the inflammatory focus was stopped; the face configuration was restored on the 7 (5-8) day; the skin redness was stopped on the 5 (5-6) day; purulent exudation from the wound was stopped on the 6 (5-7) day; favorable conditions were created for the beginning of the formation of granulations on the 7 (6-8) day. A decrease in treatment terms (9 (8-10) days) of the main group of patients in relation to the duration of treatment in patients with standard complex treatment (10 (8-12) days) was revealed. Vacuum therapy at the end of treatment helps to reduce microcirculation indices elevated at the beginning of treatment to the level of healthy individuals. Conclusions. The use of vacuum therapy in the treatment of patients with acute odontogenic osteomyelitis of the jaw, complicated by phlegmon of the adjacent tissue spaces, reduces the wound healing terms and reduces the duration of treatment from 10 (8; 12) days to 9 (8; 10) days. Normalization of the rate and degree of aggregation of LTS, DE in the blood plasma was revealed at the end of patients’ treatment using negative pressure

CapillaryX: A Software Design Pattern for Analyzing Medical Images in Real-time using Deep Learning

Abstract Recent advances in digital imaging, e.g., increased number of pixels captured, have meant that the volume of data to be processed and analyzed from these images has also increased. Deep learning algorithms are state-of-the-art for analyzing such images, given their high accuracy when trained with a large data volume of data. Nevertheless, such analysis requires considerable computational power, making such algorithms time- and resource-demanding. Such high demands can be met by using third-party cloud service providers. However, analyzing medical images using such services raises several legal and privacy challenges and do not necessarily provide real-time results. This paper provides a computing architecture that locally and in parallel can analyze medical images in real-time using deep learning thus avoiding the legal and privacy challenges stemming from uploading data to a third-party cloud provider. To make local image processing efficient on modern multi-core processors, we utilize parallel execution to offset the resource- intensive demands of deep neural networks. We focus on a specific medical-industrial case study, namely the quantifying of blood vessels in microcirculation images for which we have developed a working system. It is currently used in an industrial, clinical research setting as part of an e-health application. Our results show that our system is approximately 78% faster than its serial system counterpart and 12% faster than a master-slave parallel system architecture

CapillaryX: A Software Design Pattern for Analyzing Medical Images in Real-time using Deep Learning

Recent advances in digital imaging, e.g., increased number of pixels captured, have meant that the volume of data to be processed and analyzed from these images has also increased. Deep learning algorithms are state-of-the-art for analyzing such images, given their high accuracy when trained with a large data volume of data. Nevertheless, such analysis requires considerable computational power, making such algorithms time- and resource-demanding. Such high demands can be met by using third-party cloud service providers. However, analyzing medical images using such services raises several legal and privacy challenges and does not necessarily provide real-time results. This paper provides a computing architecture that locally and in parallel can analyze medical images in real-time using deep learning thus avoiding the legal and privacy challenges stemming from uploading data to a third-party cloud provider. To make local image processing efficient on modern multi-core processors, we utilize parallel execution to offset the resource-intensive demands of deep neural networks. We focus on a specific medical-industrial case study, namely the quantifying of blood vessels in microcirculation images for which we have developed a working system. It is currently used in an industrial, clinical research setting as part of an e-health application. Our results show that our system is approximately 78% faster than its serial system counterpart and 12% faster than a master-slave parallel system architecture

A Sequence Agnostic Multimodal Preprocessing for Clogged Blood Vessel Detection in Alzheimer's Diagnosis

Successful identification of blood vessel blockage is a crucial step for Alzheimer's disease diagnosis. These blocks can be identified from the spatial and time-depth variable Two-Photon Excitation Microscopy (TPEF) images of the brain blood vessels using machine learning methods. In this study, we propose several preprocessing schemes to improve the performance of these methods. Our method includes 3D-point cloud data extraction from image modality and their feature-space fusion to leverage complementary information inherent in different modalities. We also enforce the learned representation to be sequence-order invariant by utilizing bi-direction dataflow. Experimental results on The Clog Loss dataset show that our proposed method consistently outperforms the state-of-the-art preprocessing methods in stalled and non-stalled vessel classification.Comment: 5 pages, 4 figure

Microcirculatory alterations in critically ill COVID-19 patients analyzed using artificial intelligence

Background: The sublingual microcirculation presumably exhibits disease-specific changes in function and morphology. Algorithm-based quantification of functional microcirculatory hemodynamic variables in handheld vital microscopy (HVM) has recently allowed identification of hemodynamic alterations in the microcirculation associated with COVID-19. In the present study we hypothesized that supervised deep machine learning could be used to identify previously unknown microcirculatory alterations, and combination with algorithmically quantified functional variables increases the model's performance to differentiate critically ill COVID-19 patients from healthy volunteers. Methods: Four international, multi-central cohorts of critically ill COVID-19 patients and healthy volunteers (n = 59/n = 40) were used for neuronal network training and internal validation, alongside quantification of functional microcirculatory hemodynamic variables. Independent verification of the models was performed in a second cohort (n = 25/n = 33). Results: Six thousand ninety-two image sequences in 157 individuals were included. Bootstrapped internal validation yielded AUROC(CI) for detection of COVID-19 status of 0.75 (0.69-0.79), 0.74 (0.69-0.79) and 0.84 (0.80-0.89) for the algorithm-based, deep learning-based and combined models. Individual model performance in external validation was 0.73 (0.71-0.76) and 0.61 (0.58-0.63). Combined neuronal network and algorithm-based identification yielded the highest externally validated AUROC of 0.75 (0.73-0.78) (P < 0.0001 versus internal validation and individual models). Conclusions: We successfully trained a deep learning-based model to differentiate critically ill COVID-19 patients from heathy volunteers in sublingual HVM image sequences. Internally validated, deep learning was superior to the algorithmic approach. However, combining the deep learning method with an algorithm-based approach to quantify the functional state of the microcirculation markedly increased the sensitivity and specificity as compared to either approach alone, and enabled successful external validation of the identification of the presence of microcirculatory alterations associated with COVID-19 status. Keywords: Artificial intelligence; COVID-19; Deep learning; Microcirculation; Neuronal network

The Surviving Sepsis Campaign: research priorities for the administration, epidemiology, scoring and identification of sepsis

Epidemiologia; Disfunció d'òrgans; SèpsiaEpidemiology; Organ dysfunction; SepsisEpidemiología; Disfunción de órganos; SepsisObjective To identify priorities for administrative, epidemiologic and diagnostic research in sepsis. Design As a follow-up to a previous consensus statement about sepsis research, members of the Surviving Sepsis Campaign Research Committee, representing the European Society of Intensive Care Medicine and the Society of Critical Care Medicine addressed six questions regarding care delivery, epidemiology, organ dysfunction, screening, identification of septic shock, and information that can predict outcomes in sepsis. Methods Six questions from the Scoring/Identification and Administration sections of the original Research Priorities publication were explored in greater detail to better examine the knowledge gaps and rationales for questions that were previously identified through a consensus process. Results The document provides a framework for priorities in research to address the following questions: (1) What is the optimal model of delivering sepsis care?; (2) What is the epidemiology of sepsis susceptibility and response to treatment?; (3) What information identifies organ dysfunction?; (4) How can we screen for sepsis in various settings?; (5) How do we identify septic shock?; and (6) What in-hospital clinical information is associated with important outcomes in patients with sepsis? Conclusions There is substantial knowledge of sepsis epidemiology and ways to identify and treat sepsis patients, but many gaps remain. Areas of uncertainty identified in this manuscript can help prioritize initiatives to improve an understanding of individual patient and demographic heterogeneity with sepsis and septic shock, biomarkers and accurate patient identification, organ dysfunction, and ways to improve sepsis care.The authors volunteered their time to producing this manuscript and no funding was used to produce it