Targeted computational analysis of the C3HEB/FEJ mouse model for drug efficacy testing

2020 Spring.Includes bibliographical references.Efforts to develop effective and safe drugs for the treatment of tuberculosis (TB) require preclinical evaluation in animal models. Alongside efficacy testing of novel therapies, effects on pulmonary pathology and disease progression are monitored by using histopathology images from these infected animals. To compare the severity of disease across treatment cohorts, pathologists have historically assigned a semi-quantitative histopathology score that may be subjective in terms of their training, experience, and personal bias. Manual histopathology, therefore, has limitations regarding reproducibility between studies and pathologists, potentially masking successful treatments. This report describes a pathologist-assistive software tool that reduces these user limitations while providing a rapid, quantitative scoring system for digital histopathology image analysis. The software, called 'Lesion Image Recognition and Analysis' (LIRA), employs convolutional neural networks to classify seven different pathology features, including three different lesion types from pulmonary tissues of the C3HeB/FeJ tuberculosis mouse model. LIRA was developed to improve the efficiency of histopathology analysis for mouse tuberculosis infection models. The model approach also has broader applications to other diseases and tissues. This also includes animals that are undergoing anti-mycobacterial treatment and host immune system modulation. A complimentary software package called 'Mycobacterial Image Analysis' (MIA) had also been developed that characterizes the varying bacilli characteristics such as density, aggregate/planktonic bacilli size, fluorescent intensity, and total counts. This further groups the bacilli characteristic data depending on the seven different classifications that are selected by the user. Using this approach allows for an even more targeted analysis approach that can determine how therapy and microenvironments influence the Mtb response

Role of Mycobacterium Tuberculosis-Induced Necrotic Cell Death of Macrophages in the Pathogenesis of Pulmonary Tuberculosis: A Dissertation

Mycobacterium tuberculosis, the causative agent of tuberculosis, can manipulate host cell death pathways as virulent strains inhibit apoptosis to protect its replication niche and induce necrosis as a mechanism of escape. In vitro studies revealed that similar to lytic viruses, M. tuberculosis has the ability to induce cytolysis in macrophages when it reaches an intracellular burden of ~25 bacilli. Base on this finding, we proposed the burst size hypothesis that states when M. tuberculosis invades a macrophage at a low multiplicity of infection it replicates to a burst size triggering necrosis to escape the cell and infect naïve nearby phagocytes, propagating the spread of infection. The first part of this study investigated if the in vitro observations of M. tuberculosis cytolysis were relevant to cell death of infected phagocytes during pulmonary tuberculosis in vivo. Mice infected with a low dose of M. tuberculosis revealed during TB disease, the major host cell shifted from one type of phagocyte to another. Enumeration of intracellular bacilli from infected lung cells revealed the predictions of the hypothesis were confirmed by the distribution of bacillary loads across the population of infected phagocytes. Heavily burdened cells appeared nonviable sharing distinctive features similar to infected macrophages from in vitro studies. Collectively, the data indicates that M. tuberculosis triggers necrosis in mononuclear cells when its number reaches the threshold burst size. The previous study showed during the period of logarithmic bacterial expansion, neutrophils were the primary host cell for M. tuberculosis coinciding with the timeframe of the highest rate of burst size necrosis. The second part of this study examined this link by infecting mice with one of four different M. tuberculosis strains ranging in virulence. Mice infected with the most virulent strain had the highest bacterial burden and elicited the greatest number of infected neutrophils with the most extensive lung inflammation and greater accounts of cell death. Treating these mice with a bacteriostatic agent decreased the bacterial load and infected neutrophils in a dose-dependent manner indicating necrosis induced by virulent M. tuberculosis recruited neutrophils to the lungs. Infected neutrophils can serve as a biomarker in tuberculosis as evidenced by poorly controlled infection and increased severity of lung immune pathology

A Colour Wheel to Rule them All: Analysing Colour & Geometry in Medical Microscopy

Personalized medicine is a rapidly growing field in healthcare that aims to customize medical treatments and preventive measures based on each patient’s unique characteristics, such as their genes, environment, and lifestyle factors. This approach acknowledges that people with the same medical condition may respond differently to therapies and seeks to optimize patient outcomes while minimizing the risk of adverse effects. To achieve these goals, personalized medicine relies on advanced technologies, such as genomics, proteomics, metabolomics, and medical imaging. Digital histopathology, a crucial aspect of medical imaging, provides clinicians with valuable insights into tissue structure and function at the cellular and molecular levels. By analyzing small tissue samples obtained through minimally invasive techniques, such as biopsy or aspirate, doctors can gather extensive data to evaluate potential diagnoses and clinical decisions. However, digital analysis of histology images presents unique challenges, including the loss of 3D information and stain variability, which is further complicated by sample variability. Limited access to data exacerbates these challenges, making it difficult to develop accurate computational models for research and clinical use in digital histology. Deep learning (DL) algorithms have shown significant potential for improving the accuracy of Computer-Aided Diagnosis (CAD) and personalized treatment models, particularly in medical microscopy. However, factors such as limited generability, lack of interpretability, and bias sometimes hinder their clinical impact. Furthermore, the inherent variability of histology images complicates the development of robust DL methods. Thus, this thesis focuses on developing new tools to address these issues. Our essential objective is to create transparent, accessible, and efficient methods based on classical principles from various disciplines, including histology, medical imaging, mathematics, and art, to tackle microscopy image registration and colour analysis successfully. These methods can contribute significantly to the advancement of personalized medicine, particularly in studying the tumour microenvironment for diagnosis and therapy research. First, we introduce a novel automatic method for colour analysis and non-rigid histology registration, enabling the study of heterogeneity morphology in tumour biopsies. This method achieves accurate tissue cut registration, drastically reducing landmark distance and excellent border overlap. Second, we introduce ABANICCO, a novel colour analysis method that combines geometric analysis, colour theory, fuzzy colour spaces, and multi-label systems for automatically classifying pixels into a set of conventional colour categories. ABANICCO outperforms benchmark methods in accuracy and simplicity. It is computationally straightforward, making it useful in scenarios involving changing objects, limited data, unclear boundaries, or when users lack prior knowledge of the image or colour theory. Moreover, results can be modified to match each particular task. Third, we apply the acquired knowledge to create a novel pipeline of rigid histology registration and ABANICCO colour analysis for the in-depth study of triple-negative breast cancer biopsies. The resulting heterogeneity map and tumour score provide valuable insights into the composition and behaviour of the tumour, informing clinical decision-making and guiding treatment strategies. Finally, we consolidate the developed ideas into an efficient pipeline for tissue reconstruction and multi-modality data integration on Tuberculosis infection data. This enables accurate element distribution analysis to understand better interactions between bacteria, host cells, and the immune system during the course of infection. The methods proposed in this thesis represent a transparent approach to computational pathology, addressing the needs of medical microscopy registration and colour analysis while bridging the gap between clinical practice and computational research. Moreover, our contributions can help develop and train better, more robust DL methods.En una época en la que la medicina personalizada está revolucionando la asistencia sanitaria, cada vez es más importante adaptar los tratamientos y las medidas preventivas a la composición genética, el entorno y el estilo de vida de cada paciente. Mediante el empleo de tecnologías avanzadas, como la genómica, la proteómica, la metabolómica y la imagen médica, la medicina personalizada se esfuerza por racionalizar el tratamiento para mejorar los resultados y reducir los efectos secundarios. La microscopía médica, un aspecto crucial de la medicina personalizada, permite a los médicos recopilar y analizar grandes cantidades de datos a partir de pequeñas muestras de tejido. Esto es especialmente relevante en oncología, donde las terapias contra el cáncer se pueden optimizar en función de la apariencia tisular específica de cada tumor. La patología computacional, un subcampo de la visión por ordenador, trata de crear algoritmos para el análisis digital de biopsias. Sin embargo, antes de que un ordenador pueda analizar imágenes de microscopía médica, hay que seguir varios pasos para conseguir las imágenes de las muestras. La primera etapa consiste en recoger y preparar una muestra de tejido del paciente. Para que esta pueda observarse fácilmente al microscopio, se corta en secciones ultrafinas. Sin embargo, este delicado procedimiento no está exento de dificultades. Los frágiles tejidos pueden distorsionarse, desgarrarse o agujerearse, poniendo en peligro la integridad general de la muestra. Una vez que el tejido está debidamente preparado, suele tratarse con tintes de colores característicos. Estos tintes acentúan diferentes tipos de células y tejidos con colores específicos, lo que facilita a los profesionales médicos la identificación de características particulares. Sin embargo, esta mejora en visualización tiene un alto coste. En ocasiones, los tintes pueden dificultar el análisis informático de las imágenes al mezclarse de forma inadecuada, traspasarse al fondo o alterar el contraste entre los distintos elementos. El último paso del proceso consiste en digitalizar la muestra. Se toman imágenes de alta resolución del tejido con distintos aumentos, lo que permite su análisis por ordenador. Esta etapa también tiene sus obstáculos. Factores como una calibración incorrecta de la cámara o unas condiciones de iluminación inadecuadas pueden distorsionar o hacer borrosas las imágenes. Además, las imágenes de porta completo obtenidas so de tamaño considerable, complicando aún más el análisis. En general, si bien la preparación, la tinción y la digitalización de las muestras de microscopía médica son fundamentales para el análisis digital, cada uno de estos pasos puede introducir retos adicionales que deben abordarse para garantizar un análisis preciso. Además, convertir un volumen de tejido completo en unas pocas secciones teñidas reduce drásticamente la información 3D disponible e introduce una gran incertidumbre. Las soluciones de aprendizaje profundo (deep learning, DL) son muy prometedoras en el ámbito de la medicina personalizada, pero su impacto clínico a veces se ve obstaculizado por factores como la limitada generalizabilidad, el sobreajuste, la opacidad y la falta de interpretabilidad, además de las preocupaciones éticas y en algunos casos, los incentivos privados. Por otro lado, la variabilidad de las imágenes histológicas complica el desarrollo de métodos robustos de DL. Para superar estos retos, esta tesis presenta una serie de métodos altamente robustos e interpretables basados en principios clásicos de histología, imagen médica, matemáticas y arte, para alinear secciones de microscopía y analizar sus colores. Nuestra primera contribución es ABANICCO, un innovador método de análisis de color que ofrece una segmentación de colores objectiva y no supervisada y permite su posterior refinamiento mediante herramientas fáciles de usar. Se ha demostrado que la precisión y la eficacia de ABANICCO son superiores a las de los métodos existentes de clasificación y segmentación del color, e incluso destaca en la detección y segmentación de objetos completos. ABANICCO puede aplicarse a imágenes de microscopía para detectar áreas teñidas para la cuantificación de biopsias, un aspecto crucial de la investigación de cáncer. La segunda contribución es un método automático y no supervisado de segmentación de tejidos que identifica y elimina el fondo y los artefactos de las imágenes de microscopía, mejorando así el rendimiento de técnicas más sofisticadas de análisis de imagen. Este método es robusto frente a diversas imágenes, tinciones y protocolos de adquisición, y no requiere entrenamiento. La tercera contribución consiste en el desarrollo de métodos novedosos para registrar imágenes histopatológicas de forma eficaz, logrando el equilibrio adecuado entre un registro preciso y la preservación de la morfología local, en función de la aplicación prevista. Como cuarta contribución, los tres métodos mencionados se combinan para crear procedimientos eficientes para la integración completa de datos volumétricos, creando visualizaciones altamente interpretables de toda la información presente en secciones consecutivas de biopsia de tejidos. Esta integración de datos puede tener una gran repercusión en el diagnóstico y el tratamiento de diversas enfermedades, en particular el cáncer de mama, al permitir la detección precoz, la realización de pruebas clínicas precisas, la selección eficaz de tratamientos y la mejora en la comunicación el compromiso con los pacientes. Por último, aplicamos nuestros hallazgos a la integración multimodal de datos y la reconstrucción de tejidos para el análisis preciso de la distribución de elementos químicos en tuberculosis, lo que arroja luz sobre las complejas interacciones entre las bacterias, las células huésped y el sistema inmunitario durante la infección tuberculosa. Este método también aborda problemas como el daño por adquisición, típico de muchas modalidades de imagen. En resumen, esta tesis muestra la aplicación de métodos clásicos de visión por ordenador en el registro de microscopía médica y el análisis de color para abordar los retos únicos de este campo, haciendo hincapié en la visualización eficaz y fácil de datos complejos. Aspiramos a seguir perfeccionando nuestro trabajo con una amplia validación técnica y un mejor análisis de los datos. Los métodos presentados en esta tesis se caracterizan por su claridad, accesibilidad, visualización eficaz de los datos, objetividad y transparencia. Estas características los hacen perfectos para tender puentes robustos entre los investigadores de inteligencia artificial y los clínicos e impulsar así la patología computacional en la práctica y la investigación médicas.Programa de Doctorado en Ciencia y Tecnología Biomédica por la Universidad Carlos III de MadridPresidenta: María Jesús Ledesma Carbayo.- Secretario: Gonzalo Ricardo Ríos Muñoz.- Vocal: Estíbaliz Gómez de Marisca

Deep Learning in Medical Image Analysis

The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and understanding the underlying biological process. This book presents and highlights novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis

Single Cell Analysis

Cells are the most fundamental building block of all living organisms. The investigation of any type of disease mechanism and its progression still remains challenging due to cellular heterogeneity characteristics and physiological state of cells in a given population. The bulk measurement of millions of cells together can provide some general information on cells, but it cannot evolve the cellular heterogeneity and molecular dynamics in a certain cell population. Compared to this bulk or the average measurement of a large number of cells together, single-cell analysis can provide detailed information on each cell, which could assist in developing an understanding of the specific biological context of cells, such as tumor progression or issues around stem cells. Single-cell omics can provide valuable information about functional mutation and a copy number of variations of cells. Information from single-cell investigations can help to produce a better understanding of intracellular interactions and environmental responses of cellular organelles, which can be beneficial for therapeutics development and diagnostics purposes. This Special Issue is inviting articles related to single-cell analysis and its advantages, limitations, and future prospects regarding health benefits

Medical Image Segmentation by Deep Convolutional Neural Networks

Medical image segmentation is a fundamental and critical step for medical image analysis. Due to the complexity and diversity of medical images, the segmentation of medical images continues to be a challenging problem. Recently, deep learning techniques, especially Convolution Neural Networks (CNNs) have received extensive research and achieve great success in many vision tasks. Specifically, with the advent of Fully Convolutional Networks (FCNs), automatic medical image segmentation based on FCNs is a promising research field. This thesis focuses on two medical image segmentation tasks: lung segmentation in chest X-ray images and nuclei segmentation in histopathological images. For the lung segmentation task, we investigate several FCNs that have been successful in semantic and medical image segmentation. We evaluate the performance of these different FCNs on three publicly available chest X-ray image datasets. For the nuclei segmentation task, since the challenges of this task are difficulty in segmenting the small, overlapping and touching nuclei, and limited ability of generalization to nuclei in different organs and tissue types, we propose a novel nuclei segmentation approach based on a two-stage learning framework and Deep Layer Aggregation (DLA). We convert the original binary segmentation task into a two-step task by adding nuclei-boundary prediction (3-classes) as an intermediate step. To solve our two-step task, we design a two-stage learning framework by stacking two U-Nets. The first stage estimates nuclei and their coarse boundaries while the second stage outputs the final fine-grained segmentation map. Furthermore, we also extend the U-Nets with DLA by iteratively merging features across different levels. We evaluate our proposed method on two public diverse nuclei datasets. The experimental results show that our proposed approach outperforms many standard segmentation architectures and recently proposed nuclei segmentation methods, and can be easily generalized across different cell types in various organs

Phenotyping the virulence of SARS-CoV-2 variants in hamsters by digital pathology and machine learning

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has continued to evolve throughout the coronavirus disease-19 (COVID-19) pandemic, giving rise to multiple variants of concern (VOCs) with different biological properties. As the pandemic progresses, it will be essential to test in near real time the potential of any new emerging variant to cause severe disease. BA.1 (Omicron) was shown to be attenuated compared to the previous VOCs like Delta, but it is possible that newly emerging variants may regain a virulent phenotype. Hamsters have been proven to be an exceedingly good model for SARS-CoV-2 pathogenesis. Here, we aimed to develop robust quantitative pipelines to assess the virulence of SARS-CoV-2 variants in hamsters. We used various approaches including RNAseq, RNA in situ hybridization, immunohistochemistry, and digital pathology, including software assisted whole section imaging and downstream automatic analyses enhanced by machine learning, to develop methods to assess and quantify virus-induced pulmonary lesions in an unbiased manner. Initially, we used Delta and Omicron to develop our experimental pipelines. We then assessed the virulence of recent Omicron sub-lineages including BA.5, XBB, BQ.1.18, BA.2, BA.2.75 and EG.5.1. We show that in experimentally infected hamsters, accurate quantification of alveolar epithelial hyperplasia and macrophage infiltrates represent robust markers for assessing the extent of virus-induced pulmonary pathology, and hence virus virulence. In addition, using these pipelines, we could reveal how some Omicron sub-lineages (e.g., BA.2.75 and EG.5.1) have regained virulence compared to the original BA.1. Finally, to maximise the utility of the digital pathology pipelines reported in our study, we developed an online repository containing representative whole organ histopathology sections that can be visualised at variable magnifications (https://covid-atlas.cvr.gla.ac.uk). Overall, this pipeline can provide unbiased and invaluable data for rapidly assessing newly emerging variants and their potential to cause severe disease

Phenotypic discrimination of Mycobacterium tuberculosis by Raman spectroscopy

TB remains a major health issue worldwide causing around 1.5 deaths each year. The recent phase III clinical trials of shortened TB treatment failed to show superiority compared to the current regimen and this mainly because of relapse. Relapse is thought to be caused by dormant bacteria. Dormancy in Mycobacterium species has been shown to be associated with the accumulation of intracellular lipids, defining two phenotypes: the lipid rich (LR) cells (associated with dormancy) and the lipid poor (LP) cells (non-dormant). LR cells were shown to have a higher phenotypic antibiotic resistance compared to LP cells. Studying these two phenotypes is therefore central in tuberculosis research to understand better the disease and also potentially start to reveal the bacteriology of relapse. We investigated the power of Raman spectroscopy, a label-free and non-destructive technique, to discriminate LR and LP bacteria both in-vitro and ex-vivo. This represents the first Raman spectroscopy study that tries to discriminate the phenotypes of M. tuberculosis and investigate them directly at the site of the disease. Using total lipid extract of M. tuberculosis, we showed the location of the main lipid bands in the Raman spectrum. The two major lipid peaks were located around 1300 cm⁻¹ and 1450 cm⁻¹. Raman spectroscopy can discriminate LR and LP cells with high sensitivity and specificity. The main differences between the two groups are located in the two major Raman lipid peaks, the lipid band A (1300 cm⁻¹) and lipid band B (1440 to 1450 cm⁻¹). The two phenotypes were successfully discriminated in TB infected guinea pig lung tissue sections also from in-vitro culture using wavelength modulated Raman (WMR) spectroscopy combined with fluorescence imaging. We developed a protocol to perform both Raman spectroscopy and immunohistochemistry on the same tissue sample. We studied the evolution of LR and LP proportion in mycobacterial population as the growth conditions changed and showed that LR cells could rapidly convert to LP cells as they face favourable growth conditions. The results presented in this thesis showed that LR M. tuberculosis cells could be predominant at the site of infection. This would suggest that drug sensitivity testing should be performed on culture presenting both LR and LP cells in high proportion."This work was supported by the PreDiCT-TB consortium [IMI Joint undertaking grant agreement number 115337, resources of which are composed of financial contribution from the European Union’s Seventh Framework Programme (FP7/2007– 2013) and EFPIA companies’ in kind contribution (www.imi.europa.eu)]." -- Acknowledgement

Point-of-care diagnostics with digital microscopy and artificial intelligence

The lack of access to diagnostics is a global problem which causes underdiagnosis of various common and treatable diseases. In certain areas, the access to laboratory services and medical experts is extremely limited, such as in sub-Saharan Africa, with often less than one practising pathologist per one million inhabitants. Annually, hundreds of millions of microscopy samples are analysed to diagnose e.g. infectious diseases and cancers, but the need for more is significant. During the last decade, technological advancements and reduced prices of optical components have enabled the construction of inexpensive, portable devices for digitization of microscopy samples; a procedure traditionally limited to well-equipped laboratories with expensive high-end equipment. By allowing digitization of samples directly at the point of care (POC), advanced digital diagnostic techniques, such as the analysis of samples with medical ‘artificial intelligence’ (AI) algorithms, can be utilized also outside high-end laboratories – which is precisely where the need for improved diagnostics is often most significant. The aim of this thesis is to study how low-cost, POC digital microscopy, supported by automatized digital image analysis and AI can be applied for routine microscopy diagnostics with an emphasis on potential areas of application in low-resource settings. We describe, implement and evaluate various techniques for POC digitization and analysis of samples using both visual methods and digital algorithms. Specifically, we evaluate the technologies for the analysis of breast cancer tissue samples (assessment of hormone receptor expression), intraoperative samples from cancer surgeries (detection of metastases in lymph node frozen sections), cytological samples (digital Pap smear screening) and parasitological samples (diagnostics of neglected tropical diseases). Our results show how the digitization of a variety of routine microscopy samples is feasible using systems suitable POC usage with sufficient image quality for diagnostic applications. Furthermore, the findings demonstrate how digital methods, based on computer vision and AI, can be utilized to facilitate the sample analysis process to e.g. quantify tissue stains and detect atypical cells and infectious pathogens in the samples with levels of accuracy comparable to conventional methods. In conclusion, our findings show how technological advancements can be leveraged to create general-purpose digital microscopy diagnostic platforms, which are implementable and feasible to use for diagnostic purposes at the POC. This allows the utilization of modern digital algorithms and AI to aid in analysis of samples and facilitate the diagnostic process by automatically extracting information from the digital samples. These findings are important steps in the effort to develop novel diagnostic technologies which are usable also in areas without access to high-end laboratories, and the technologies described here are also likely to be applicable for diagnostics of other diseases which are currently diagnosed with light microscopy.Bristande åtkomst till medicinsk diagnostik är ett signifikant globalt problem som resulterar i att många vanliga, behandlingsbara sjukdomar förblir underdiagnostiserade. I vissa områden är tillgängligheten av laboratorietjänster och medicinisk personal kraftigt begränsad, liksom exempelvis i subsahariska Afrika där det totala antalet patologer ofta är lägre än en per miljoner invånare. Under det senaste decenniet har den tekniska utvecklingen möjliggjort utvecklingen av portabla, förmånliga instrument för att digitalisera biologiska prov även i fältmiljö; något som traditionellt begränsats till högklassiga laboratorier på grund av kravet på avancerad och dyr teknik. Genom att möjliggöra patientnära (’point of care’) digitalisering av prov kan avancerade digitala metoder, som provanalys med ’artificiell intelligens’, tillämpas även i fältmiljö – dvs. där behovet av förbättrad diagnostik är störst. Målet med avhandlingen är att undersöka hur förmånlig, patientnära digitalmikroskopi, kombinerat med digital bildanalys och artificiell intelligens, kan tillämpas för att effektivera rutinmässig mikroskopidiagnostik med tyngdpunkt på eventuella användningsområden i lågresursmiljöer. I arbetet beskriver, implementerar och utvärderar vi olika metoder för patientnära digitalisering av mikroskopiprov och analys av proven både visuellt och med automatiserade, digitala metoder. Arbetet är uppdelat i olika områden som undersöker teknologin för olika användningsområden. Dessa är: 1) onkologisk vävnadspatologi (bestämning av hormonreceptorstatus), 2) analys av intraoperativa vävnadsprov (fryssnitt för detektion av metastaser), 3) analys av cytologiska cellprov (Papa-prov) samt 4) diagnostik av de vanligaste tropiska parasitsjukdomarna (’neglected tropical diseases’). Resultaten visar hur digitaliseringen av mikroskopiprov är möjlig med miniatyriserade digitalmikroskop som lämpar sig för användning i fältmiljö, med en tillräcklig bildkvalitet för medicinsk diagnostik. Utöver detta kan provanalysen effektiveras med hjälp av automatiserad digital bildanalys, för att ex. mäta nivåer av vävnadsfärger samt identifiera premaligna cellförändringar samt parasiter i proven. Sammanfattningsvis tyder resultaten på att metoderna här är lämpliga för patientnära mikroskopidiagnostik av ett flertal olika sjukdomar. Genom att tillämpa moderna digitala bildanalysmetoder kan provanalysen automatiseras och effektiveras. Resultaten är betydande steg i utvecklingen av digitaldiagnostiska metoder som är användbara även i områden utan tillgång till högklassig laboratorieinfrastruktur. Utöver detta är metoderna beskrivna här sannolikt även möjliga att tillämpa för diagnostik av ett flertal övriga sjukdomar vars diagnostik för tillfället baserar sig på ljusmikroskopi