Advances and challenges in automated malaria diagnosis using digital microscopy imaging with artificial intelligence tools: A review

Malaria is an infectious disease caused by parasites of the genus Plasmodium spp. It is transmitted to humans by the bite of an infected female Anopheles mosquito. It is the most common disease in resource-poor settings, with 241 illion malaria cases reported in 2020 according to the World Health Organization. Optical microscopy examination of blood smears is the gold standard technique for malaria diagnosis; however, it is a time-consuming method and a well-trained microscopist is needed to perform the microbiological diagnosis. New techniques based on digital imaging analysis by deep learning and artificial intelligence methods are a challenging alternative tool for the diagnosis of infectious diseases. In particular, systems based on Convolutional Neural Networks for image detection of the malaria parasites emulate the microscopy visualization of an expert. Microscope automation provides a fast and low-cost diagnosis, requiring less supervision. Smartphones are a suitable option for microscopic diagnosis, allowing image capture and software identification of parasites. In addition, image analysis techniques could be a fast and optimal solution for the diagnosis of malaria, tuberculosis, or Neglected Tropical Diseases in endemic areas with low resources. The implementation of automated diagnosis by using smartphone applications and new digital imaging technologies in low-income areas is a challenge to achieve. Moreover, automating the movement of the microscope slide and image autofocusing of the samples by hardware implementation would systemize the procedure. These new diagnostic tools would join the global effort to fight against pandemic malaria and other infectious and poverty-related diseases.The project is funded by the Microbiology Department of Vall d’Hebron Universitary Hospital, the Cooperation Centre of the Universitat Politècnica de Catalunya (CCD-UPC) and the Probitas FoundationPostprint (published version

Advances and challenges in automated malaria diagnosis using digital microscopy imaging with artificial intelligence tools : A review

Malaria is an infectious disease caused by parasites of the genus Plasmodium spp. It is transmitted to humans by the bite of an infected female Anopheles mosquito. It is the most common disease in resource-poor settings, with 241 million malaria cases reported in 2020 according to the World Health Organization. Optical microscopy examination of blood smears is the gold standard technique for malaria diagnosis; however, it is a time-consuming method and a well-trained microscopist is needed to perform the microbiological diagnosis. New techniques based on digital imaging analysis by deep learning and artificial intelligence methods are a challenging alternative tool for the diagnosis of infectious diseases. In particular, systems based on Convolutional Neural Networks for image detection of the malaria parasites emulate the microscopy visualization of an expert. Microscope automation provides a fast and low-cost diagnosis, requiring less supervision. Smartphones are a suitable option for microscopic diagnosis, allowing image capture and software identification of parasites. In addition, image analysis techniques could be a fast and optimal solution for the diagnosis of malaria, tuberculosis, or Neglected Tropical Diseases in endemic areas with low resources. The implementation of automated diagnosis by using smartphone applications and new digital imaging technologies in low-income areas is a challenge to achieve. Moreover, automating the movement of the microscope slide and image autofocusing of the samples by hardware implementation would systemize the procedure. These new diagnostic tools would join the global effort to fight against pandemic malaria and other infectious and poverty-related diseases

Assessing neurodegeneration of the retina and brain with ultra-widefield retinal imaging

The eye is embryologically, physiologically and anatomically linked to the brain. Emerging evidence suggests that neurodegenerative diseases, such as Alzheimer’s disease (AD), manifest in the retina. Retinal imaging is a quick, non-invasive method to view the retina and its microvasculature. Features such as blood vessel calibre, tortuosity and complexity of the vascular structure (measured through fractal analysis) are thought to reflect microvascular health and have been found to associate with clinical signs of hypertension, diabetes, cardiovascular disease and cognitive decline. Small deposits of acellular debris called drusen in the peripheral retina have also been linked with AD where histological studies show they can contain amyloid beta, a hallmark of AD. Age-related macular degeneration (AMD) is a neurodegenerative disorder of the retina and a leading cause of irreversible vision loss in the ageing population. Increasing number and size of drusen is a characteristic of AMD disease progression. Ultra-widefield (UWF) retinal imaging with a scanning laser ophthalmoscope captures up to 80% of the retina in a single acquisition allowing a larger area of the retina to be assessed for signs of neurodegeneration than is possible with a conventional fundus camera, particularly the periphery. Quantification of changes to the microvasculature and drusen load could be used to derive early biomarkers of diseases that have vascular and neurodegenerative components such as AD and other forms of dementia.Manually grading drusen in UWF images is a difficult, subjective and a time-consuming process because the area imaged is large (around 700mm2) and drusen appear as small spots ( 0.8 and < 0.9), achieving AUC 0.55-0.59, 0.78-0.82 and 0.82-0.85 in the central, perimacular and peripheral zones, respectively. Measurements of the retinal vasculature appearing in UWF images of cognitively healthy (CH) individuals and patients diagnosed with mild cognitive impairment (MCI) and AD were obtained using a previously established pipeline. Following data cleaning, vascular measures were compared using multivariate generalised estimation equations (GEE), which accounts for the correlation between eyes of individuals with correction for confounders (e.g. age). The vascular measures were repeated for a subset of images and analysed using GEE to assess the repeatability of the results. When comparing AD with CH, the analysis showed a statistically significant difference between measurements of arterioles in the inferonasal quadrant, but fractal analysis produced inconsistent results due to differences in the area sampled in which the fractal dimension was calculated.When looking at drusen load, there was a higher abundance of drusen in the inferonasal region of the peripheral retina in the CH and AD compared to the MCI group. Using GEE analysis, there was evidence of a significant difference in drusen count when comparing MCI to CH (p = 0.02) and MCI to AD (p = 0.03), but no evidence of a difference when comparing AD to CH. However, given the low sensitivity of the system (partly the result of only moderate agreement between human observers), there will be a large proportion of drusen that are not detected giving an under estimation of the true amount of drusen present in an image. Overcoming this limitation will involve training the system using larger datasets and annotations from additional observers to create a more consistent reference standard. Further validation could then be performed in the future to determine if these promising pilot results persist, leading to candidate retinal biomarkers of AD

Computational Image Analysis For Axonal Transport, Phenotypic Profiling, And Digital Pathology

Recent advances in fluorescent probes, microscopy, and imaging platforms have revolutionized biology and medicine, generating multi-dimensional image datasets at unprecedented scales. Traditional, low-throughput methods of image analysis are inadequate to handle the increased “volume, velocity, and variety” that characterize the realm of big data. Thus, biomedical imaging requires a new set of tools, which include advanced computer vision and machine learning algorithms. In this work, we develop computational image analysis solutions to biological questions at the level of single-molecules, cells, and tissues. At the molecular level, we dissect the regulation of dynein-dynactin transport initiation using in vitro reconstitution, single-particle tracking, super-resolution microscopy, live-cell imaging in neurons, and computational modeling. We show that at least two mechanisms regulate dynein transport initiation neurons: (1) cytoplasmic linker proteins, which are regulated by phosphorylation, increase the capture radius around the microtubule, thus reducing the time cargo spends in a diffusive search; and (2) a spatial gradient of tyrosinated alpha-tubulin enriched in the distal axon increases the affinity of dynein-dynactin for microtubules. Together, these mechanisms support a multi-modal recruitment model where interacting layers of regulation provide efficient, robust, and spatiotemporal control of transport initiation. At the cellular level, we develop and train deep residual convolutional neural networks on a large and diverse set of cellular microscopy images. Then, we apply networks trained for one task as deep feature extractors for unsupervised phenotypic profiling in a different task. We show that neural networks trained on one dataset encode robust image phenotypes that are sufficient to cluster subcellular structures by type and separate drug compounds by the mechanism of action, without additional training, supporting the strength and flexibility of this approach. Future applications include phenotypic profiling in image-based screens, where clustering genetic or drug treatments by image phenotypes may reveal novel relationships among genetic or pharmacologic pathways. Finally, at the tissue level, we apply deep learning pipelines in digital pathology to segment cardiac tissue and classify clinical heart failure using whole-slide images of cardiac histopathology. Together, these results demonstrate the power and promise of computational image analysis, computer vision, and deep learning in biological image analysis

Analysis of Signal Decomposition and Stain Separation methods for biomedical applications

Nowadays, the biomedical signal processing and classification and medical image interpretation play an essential role in the detection and diagnosis of several human diseases. The problem of high variability and heterogeneity of information, which is extracted from digital data, can be addressed with signal decomposition and stain separation techniques which can be useful approaches to highlight hidden patterns or rhythms in biological signals and specific cellular structures in histological color images, respectively. This thesis work can be divided into two macro-sections. In the first part (Part I), a novel cascaded RNN model based on long short-term memory (LSTM) blocks is presented with the aim to classify sleep stages automatically. A general workflow based on single-channel EEG signals is developed to enhance the low performance in staging N1 sleep without reducing the performances in the other sleep stages (i.e. Wake, N2, N3 and REM). In the same context, several signal decomposition techniques and time-frequency representations are deployed for the analysis of EEG signals. All extracted features are analyzed by using a novel correlation-based timestep feature selection and finally the selected features are fed to a bidirectional RNN model. In the second part (Part II), a fully automated method named SCAN (Stain Color Adaptive Normalization) is proposed for the separation and normalization of staining in digital pathology. This normalization system allows to standardize digitally, automatically and in a few seconds, the color intensity of a tissue slide with respect to that of a target image, in order to improve the pathologist’s diagnosis and increase the accuracy of computer-assisted diagnosis (CAD) systems. Multiscale evaluation and multi-tissue comparison are performed for assessing the robustness of the proposed method. In addition, a stain normalization based on a novel mathematical technique, named ICD (Inverse Color Deconvolution) is developed for immunohistochemical (IHC) staining in histopathological images. In conclusion, the proposed techniques achieve satisfactory results compared to state-of-the-art methods in the same research field. The workflow proposed in this thesis work and the developed algorithms can be employed for the analysis and interpretation of other biomedical signals and for digital medical image analysis