Automatic computation of the arteriovenous ratio and assessment of its effectiveness as a prognostic indicator in hypertension

[Resumen] La retina es la única parte del cuerpo humano en donde se pueden observar los vasos sanguíneos directamente de una forma no invasiva mediante un examen de fondo de ojo. De esta manera, la imagen de la retina mediante las técnicas de procesamiento de imágenes se convirtió en un campo de clave para el diagnóstico precoz de varias enfermedades sistémicas que provocan alteraciones visibles en dicha imagen. Así, alteraciones en el ancho de los vasos retinianos se asocian con patologías tales como diabetes o hipertensión. De hecho, el estrechamiento de las arterias constituye un indicio precoz de la hipertensión arterial sistémica, siendo una característica del grado I de la retinopatía hipertensiva de acuerdo con la clasificación de Keith-Wagener-Barker. En este sentido, se han realizado esfuerzos para desarrollar programas asistidos por ordenador para medir con precisión los cambios en el ancho de los vasos a través del índice arteriovenoso (IAV), es decir, la relación entre los calibres de las arterias y las venas. Sin embargo, aunque estos sistemas se han usado en muchos estudios con fines de investigación, su aplicabilidad en la práctica clínica diaria es todavía discutida. En este trabajo, se propone una nueva metodología para el cálculo del IAV con el fin de estratificar el riesgo cardiovascular de los hipertensos. Por un lado, se ha desarrollado un método completamente automático para estimar el IAV en una imagen de fondo de ojo de un paciente. Por otro lado, se propone un sistema para monitorizar el IAV del paciente a lo largo del tiempo. Para este fin, las mediciones del IAV en las diferentes imágenes adquiridas sobre el mismo ojo del paciente en diferentes fechas se estiman usando el mismo conjunto de vasos medidos en las mismas áreas. Por lo tanto, la mediciones obtenidos de esta manera son comparables y precisas, debido a que son independientes en el conjunto de vasos seleccionados para el cálculo. Las dos técnicas se han integrado en SIRIUS, un sistema web destinado a incluir diferentes servicios en el campo del análisis de la imagen retiniana. El sistema incluye también gestión de pacientes y revisiones, lo que facilita el análisis de las lesiones retinianas causadas por diferentes patologías y su evolución después de un determinado tratamiento. Además al ser una aplicación distribuída a través de la web, proporciona un entorno de colaboración entre diferentes médicos, investigadores y centros.[Resumo] A retina é a única parte do corpo humano onde se poden observar os vasos sanguíneos directamente dunha maneira non invasiva mediante un examen do fondo do ollo. Desta maneira, a imaxe da retina mediante as técnicas de procesamento de imáxenes converteuse nun campo chave para o diagnóstico precoz de varias enfermidades sistémicas que provocan alteracións visibles en dita imaxe. Así, cambios no ancho dos vasos retinianos asócianse con patoloxías tales como a diabetes ou a hipertensión. De feito, o estreitamento das arterias constitúe un indicio prematuro da hipertensión arterial sistémica, sendo unha característica do grado I da retinopatía hipertensiva dacordo coa clasificación de Keith- Wagener-Barker. Neste sentido, fixerónse moitos esforzos para desenvolver programas asistidos por ordenador para medir con precisión os cambios no ancho dos vasos a través do índice arteriovenoso (IAV), é dicir, a relación entre os calibres das arterias e das veas. Nembargantes, aínda que estes sistemas foron usados en moitos estudios con fins investigadores, a sua aplicabilidade na práctica clínica diaria aínda é discutida. Neste traballo, proponse unha nova metodoloxía para o cálculo do IAV co fin de estratificar o risco cardiovascular dos hipertensos. Por un lado, desenvolveuse un método completamente automático para estimar o IAV nunha imaxe de fondo de ollo dun doente. Por outra banda, proponse un sistema para monitorizar o IAV dun doente a lo longo do tempo. Para isto, as medicións do IAV nas diferentes imaxes adquiridas sobre o mesmo ollo do doente en diferentes datas fanse usando o mesmo conxunto de vasos medidos nas mesmas áreas. Polo tanto, as medicións obtidas desta maneira son comparables e precisas, debido a que son independentes do conxunto de vasos seleccionados para o cálculo. As dúas técnicas foron integradas no SIRIUS, un sistema web destinado a incluir diferentes servicios no campo da análise da imaxe retiniana. O sistema inclúe tamén xestión de doentes e revisións, facilitando a análise e estudo das lesións retinianas causadas por diferentes patoloxías e a súa evolución despois dun determinado tratamento. Ademais ao ser unha aplicación distribuída a través da web, proporciona un entorno de colaboración entre diferentes médicos, investigadores e centros.[Abstract] Retina is the only part in the human body where blood vessels can be directly observed in a non-invasive way through an eye fundus examination. In this manner, the retinal imaging assisted by image processing techniques became a key field for the early diagnosis of several systemic diseases which cause visible alterations in the fundus image. Thus, changes in the retinal vessel widths are associated with pathologies such as diabetes or hypertension. In fact, arteriolar narrowing constitutes an early sign of systemic hypertension, being a feature for the grade I of hypertension retinopathy according to Keith-Wagener-Barker classification. In this sense, some efforts have been made to develop computer-assisted programs to measure accurately abnormalities in the vessel widths through the arteriovenous ratio (AVR), that is, the relation between arteriolar and venular vessel widths. However, although these systems have been used in many studies for research purposes, their applicability to daily clinical practice is yet discussed. In this work, a new methodology for the AVR computation is proposed in order to stratify the cardiovascular risk of hypertension. On one hand, a fully automatic method to estimate the AVR in a sample patient's image is developed. On the other hand, an AVR monitoring system to compute the patient's AVR over time was implemented. To this end, the AVR measurements computed in the different patient's images acquired from the same eye at different dates, uses the same set of vessels measured at the same areas. Thus, the measurements achieved in this manner are comparable and precise due to they are independent on the set of vessels selected for the calculus. The two approaches have been integrated in SIRIUS, a web-based system aimed to include different services in the field of retinal image analysis. It includes patient and checkup management, making easier to analyze the retinal lesions caused by different pathologies and their evolution after a specific treatment. Moreover, being a application distributed via the web, it provides a collaborative environment among different physicians, researchers and medical centers

Delivering Reliable AI to Clinical Contexts: Addressing the Challenge of Missing Data

Clinical data are essential in the medical domain, ensuring quality of care and improving decision-making. However, their heterogeneous and incomplete nature leads to an ubiquity of data quality problems, particularly missing values. Inevitable challenges arise in delivering reliable Decision Support Systems (DSSs), as missing data yield negative effects on the learning process of Machine Learning models. The interest in developing missing value imputation strategies has been growing, in an endeavour to overcome this issue. This dissertation aimed to study missing data and their relationships with observed values, and to lateremploy that information in a technique that addresses the predicaments posed by incomplete datasets in real-world scenarios. Moreover, the concept of correlation was explored within the context of missing value imputation, a promising but rather overlooked approach in biomedical research. First, a comprehensive correlational study was performed, which considered key aspects from missing data analysis. Afterwards, the gathered knowledge was leveraged to create three novel correlation-based imputation techniques. Thesewere not only validated on datasets with a controlled and synthetic missingness, but also on real-world medical datasets. Their performance was evaluated against competing imputation methods, both traditional and state-of-the-art. The contributions of this dissertation encompass a systematic view of theoretical concepts regarding the analysis and handling of missing values. Additionally, an extensive literature review concerning missing data imputation was conducted, which comprised a comparative study of ten methods under diverse missingness conditions. The proposed techniques exhibited similar results when compared to their competitors, sometimes even superior in terms of imputation precision and classification performance, evaluated through the Mean Absolute Error and the Area Under the Receiver Operating Characteristic curve, respectively. Therefore, this dissertation corroborates the potential of correlation to improve the robustness of DSSs to missing values, and provides answers to current flaws shared by correlation-based imputation strategies in real-world medical problems.Dados clínicos são essenciais para assegurar cuidados médicos de qualidade e melhorar a tomada de decisões. Contudo, a sua natureza heterogénea e incompleta cria uma ubiquidade de problemas de qualidade, nomeadamente pela existência de valores em falta. Esta condição origina desafios inevitáveis para a disponibilização de Sistemas de Apoio à Decisão (SADs) fiáveis, já que dados em falta acarretam efeitos negativos no treino de modelos de Aprendizagem Automática. O interesse no desenvolvimento de estratégias de imputação de valores em falta tem vindo a crescer, num esforço para superar esta adversidade. Esta dissertação visou estudar o problema dos dados em falta através das relações que estes apresentam com os valores observados. Esta informação foi depois utilizada no desenvolvimento de técnicas para colmatar os problemas impostos por dados incompletos em cenários reais. Ademais, o conceito de correlação foi explorado no contexto da imputação de valores em falta, já que, apesar de promissor, tem vindo a ser negligenciado em investigação biomédica. Em primeiro lugar, foi realizado um estudo correlacional abrangente que contemplou aspetos fundamentais da análise de dados em falta. Posteriormente, o conhecimento recolhido foi aplicado na criação de três novas técnicas de imputação baseadas na correlação. Estas foram validadas não só em conjuntos de dados com incompletude controlada e sintética, mas também em conjuntos de dados médicos reais. O seu desempenho foi avaliado e comparado a métodos de imputação tanto tradicionais como de estado-de-arte. As contribuições desta dissertação passam pela sistematização de conceitos teóricos relativos à análise e tratamento de dados em falta. Adicionalmente, realizou-se uma extensa revisão da literatura referente à imputação de dados, que compreendeu um estudo comparativo de dez métodos sob diversas condições de incompletude. As técnicas propostas exibiram resultados semelhantes aos dos restantes métodos, por vezes até superiores em termos de precisão da imputação e de performance da classificação. Assim, esta dissertação corrobora o potencial da utilização da correlação na melhoria da robustez de SADs a dados em falta, e fornece respostas a algumas das atuais falhas partilhadas por estratégias de imputação baseadas em correlação quando aplicadas a casos médicos reais

The Experience of Relapse After Long-Term Sobriety and Subsequent Return to Sobriety

While psychiatric medications have been categorized as the same as substances of abuse in Alcoholics Anonymous (AA), medications for common medical disorders were not affected by this disapproval of medication. It may be time for a new dialogue (Woody, 2015). According to Gjersing and Bretteville (2018), there has been a concerning increase in overdose deaths in the last decade. This includes a threefold increase in overdose deaths from prescription narcotics and six-fold increase in overdose deaths from heroin in the United States. When prescription opioid users find difficulty in obtaining pills, they may move on to heroin, which is much more readily available on the streets, in an effort to avoid painful opioid withdrawal. For this study, individuals who had previously achieved long-term abstinence from alcohol or substance use but relapsed after a significant amount of time sober were interviewed in order to better understand their experience with relapse as well as their experience returning to at least partial remission. Thematic analysis was conducted on interview data. The results from this phenomenological analysis of interviews with eight participants identified several themes regarding the experience of being a long-timer, relapsing after a substantial amount of time abstinent, and challenges to as well as factors in returning to AA. These themes are organized as long-term recovery, relapse, and a new beginning. Long-term recovery is further explored as acute treatment only, treatment did not utilize evidence-based interventions, treatment did not address emotional issues, contact with mental health, long-timer, and complacency and drifting. Relapse is further explored as medical issues, new trauma, and justification of the use of medication or marijuana. A new beginning is further explored as recovery challenges such as feelings of ostracism, age-related issues, and shame as well as recovery factors such as finding acceptance and love within the fellowship, cognitive reframing, and re-engaging the program with enthusiasm. This Dissertation is available on Open Access at AURA: Antioch University Repository and Archive, http://aura.antioch.edu and OhioLink ETD Center, http://www.ohiolink.edu/et

The Experience of Relapse After Long-Term Sobriety and Subsequent Return to Sobriety

While psychiatric medications have been categorized as the same as substances of abuse in Alcoholics Anonymous (AA), medications for common medical disorders were not affected by this disapproval of medication. It may be time for a new dialogue (Woody, 2015). According to Gjersing and Bretteville (2018), there has been a concerning increase in overdose deaths in the last decade. This includes a threefold increase in overdose deaths from prescription narcotics and six-fold increase in overdose deaths from heroin in the United States. When prescription opioid users find difficulty in obtaining pills, they may move on to heroin, which is much more readily available on the streets, in an effort to avoid painful opioid withdrawal. For this study, individuals who had previously achieved long-term abstinence from alcohol or substance use but relapsed after a significant amount of time sober were interviewed in order to better understand their experience with relapse as well as their experience returning to at least partial remission. Thematic analysis was conducted on interview data. The results from this phenomenological analysis of interviews with eight participants identified several themes regarding the experience of being a long-timer, relapsing after a substantial amount of time abstinent, and challenges to as well as factors in returning to AA. These themes are organized as long-term recovery, relapse, and a new beginning. Long-term recovery is further explored as acute treatment only, treatment did not utilize evidence-based interventions, treatment did not address emotional issues, contact with mental health, long-timer, and complacency and drifting. Relapse is further explored as medical issues, new trauma, and justification of the use of medication or marijuana. A new beginning is further explored as recovery challenges such as feelings of ostracism, age-related issues, and shame as well as recovery factors such as finding acceptance and love within the fellowship, cognitive reframing, and re-engaging the program with enthusiasm. This Dissertation is available on Open Access at AURA: Antioch University Repository and Archive, http://aura.antioch.edu and OhioLink ETD Center, http://www.ohiolink.edu/et

Low-Resource Unsupervised NMT:Diagnosing the Problem and Providing a Linguistically Motivated Solution

Unsupervised Machine Translation hasbeen advancing our ability to translatewithout parallel data, but state-of-the-artmethods assume an abundance of mono-lingual data. This paper investigates thescenario where monolingual data is lim-ited as well, finding that current unsuper-vised methods suffer in performance un-der this stricter setting. We find that theperformance loss originates from the poorquality of the pretrained monolingual em-beddings, and we propose using linguis-tic information in the embedding train-ing scheme. To support this, we look attwo linguistic features that may help im-prove alignment quality: dependency in-formation and sub-word information. Us-ing dependency-based embeddings resultsin a complementary word representationwhich offers a boost in performance ofaround 1.5 BLEU points compared to stan-dardWORD2VECwhen monolingual datais limited to 1 million sentences per lan-guage. We also find that the inclusion ofsub-word information is crucial to improv-ing the quality of the embedding

Automatic BIRAD scoring of breast cancer mammograms

A computer aided diagnosis system (CAD) is developed to fully characterize and classify mass to benign and malignancy and to predict BIRAD (Breast Imaging Reporting and Data system) scores using mammographic image data. The CAD includes a preprocessing step to de-noise mammograms. This is followed by an active counter segmentation to deforms an initial curve, annotated by a radiologist, to separate and define the boundary of a mass from background. A feature extraction scheme wasthen used to fully characterize a mass by extraction of the most relevant features that have a large impact on the outcome of a patient biopsy. For this thirty-five medical and mathematical features based on intensity, shape and texture associated to the mass were extracted. Several feature selection schemes were then applied to select the most dominant features for use in next step, classification. Finally, a hierarchical classification schemes were applied on those subset of features to firstly classify mass to benign (mass with BIRAD score 2) and malignant mass (mass with BIRAD score over 4), and secondly to sub classify mass with BIRAD score over 4 to three classes (BIRAD with score 4a,4b,4c). Accuracy of segmentation performance were evaluated by calculating the degree of overlapping between the active counter segmentation and the manual segmentation, and the result was 98.5%. Also reproducibility of active counter 3 using different manual initialization of algorithm by three radiologists were assessed and result was 99.5%. Classification performance was evaluated using one hundred sixty masses (80 masses with BRAD score 2 and 80 mass with BIRAD score over4). The best result for classification of data to benign and malignance was found using a combination of sequential forward floating feature (SFFS) selection and a boosted tree hybrid classifier with Ada boost ensemble method, decision tree learner type and 100 learners’ regression tree classifier, achieving 100% sensitivity and specificity in hold out method, 99.4% in cross validation method and 98.62 % average accuracy in cross validation method. For further sub classification of eighty malignance data with BIRAD score of over 4 (30 mass with BIRAD score 4a,30 masses with BIRAD score 4b and 20 masses with BIRAD score 4c), the best result achieved using the boosted tree with ensemble method bag, decision tree learner type with 200 learners Classification, achieving 100% sensitivity and specificity in hold out method, 98.8% accuracy and 98.41% average accuracy for ten times run in cross validation method. Beside those 160 masses (BIRAD score 2 and over 4) 13 masses with BIRAD score 3 were gathered. Which means patient is recommended to be tested in another medical imaging technique and also is recommended to do follow-up in six months. The CAD system was trained with mass with BIRAD score 2 and over 4 also 4 it was further tested using 13 masses with a BIRAD score of 3 and the CAD results are shown to agree with the radiologist’s classification after confirming in six months follow up. The present results demonstrate high sensitivity and specificity of the proposed CAD system compared to prior research. The present research is therefore intended to make contributions to the field by proposing a novel CAD system, consists of series of well-selected image processing algorithms, to firstly classify mass to benign or malignancy, secondly sub classify BIRAD 4 to three groups and finally to interpret BIRAD 3 to BIRAD 2 without a need of follow up study

Procena regularnosti i sinhronizma u paralelnim biomedicinskim vremenskim nizovima

Objectives: Self-monitoring in health applications has already been recognized as a part of the mobile crowdsensing concept, where subjects, equipped with adequate sensors, share and extract information for personal or common benefit. Limited data transmission resources force a local analysis at wearable devices, but it is incompatible with analytical tools that require stationary and artifact-free data. The key objective of this thesis is to explain a computationally efficient binarized cross-approximate entropy, (X)BinEn, for blind cardiovascular signal processing in environments where energy and processor resources are limited. Methods: The proposed method is a descendant of cross-approximate entropy ((X)ApEn). It operates over binary differentially encoded data series, split into m-sized binary vectors. Hamming distance is used as a distance measure, while a search for similarities is performed over the vector sets, instead of over the individual vectors. The procedure is tested in laboratory rats exposed to shaker and restraint stress and compared to the existing (X)ApEn results. Results: The number of processor operations is reduced. (X)BinEn captures entropy changes similarly to (X)ApEn. The coding coarseness has an adverse effect of reduced sensitivity, but it attenuates parameter inconsistency and binary bias. A special case of (X)BinEn is equivalent to Shannon entropy. A binary conditional m=1 entropy is embedded into the procedure and can serve as a complementary dynamic measure. Conclusion: (X)BinEn can be applied to a single time series as auto-entropy or, more generally, to a pair of time series, as cross-entropy. It is intended for mobile, battery operated self-attached sensing devices with limited power and processor resources.Cilj: Snimanje sopstvenih zdravstveih prametara je postalo deo koncepta mobilnog ‘crowdsensing-a’ prema kojem učesnici sa nakačenim senzorima skupljaju i dele informacije, na ličnu ili opštu dobrobit. Međutim, ograničenja u prenosu podataka dovela su do koncepta lokalne obrade (na licu mesta). To je pak nespojivo sa uobičajenim metodama za koje je potrebno da podaci koji se obrađuju budu stacionarni i bez artefakata. Ključni deo ove teze je opis procesorski nezahtevne binarizovane unakrsne aproksimativne entropije (X)BinEn koja omogućava analizu kardiovaskularnih podataka bez prethodne predobrade, u uslovima ograničenog napajanja i procesorskih resursa. Metoda: (X)BinEn je nastao razradom postojećeg postupka unakrsne entropije ((X)ApEn). Definisan je nad binarnim diferencijalno kodovanim vremenskim nizovima, razdeljenim u binarne vektore dužine m. Za procenu razmaka između vektora koristi se Hemingovo rastojanje, a sličnost vektora se ne procenjuje između svakog vektora pojedinačno, već između skupova vektora. Procedura je testirana nad laboratorijskim pacovima izloženim različitim vrstova stresova i upoređena sa postojećim rezultatima. Rezultati: Broj potrebnih procesorskih operacija je značajno smanjen. (X)BinEn registruje promene entropije slično (X)ApEn. Beskonačno klipovanje je gruba kvantizacija i za posledicu ima smanjenu osetljivost na promene, ali, sa druge strane, prigušuje binarnu asimetriju i nekonzistentnan uticaj parametara. Za određeni skup parametara (X)BinEn je ekvivalentna Šenonovoj entropiji. Uslovna binarna m=1 entropija automatski se dobija kao uzgredni product binarizovane entropije, i može da se iskoristi kao komplementarna dinamička mera. Zaključak: (X)BinEn može da se koristi za jedan vremenski niz, kao auto-entropija, ili, u opštem slučaju, za dva vremenska niza kao unakrsna entropija. Namenjena je mobilnim uređajima sa baterijskim napajanjem za individualne korisnike, to jest za korisnike sa ograničenim napajanjem i procesorskim resursima