Artificial intelligence in andrology: From Semen Analysis to Image Diagnostics

Artificial intelligence (AI) in medicine has gained a lot of momentum in the last decades and has been applied to various fields of medicine. Advances in computer science, medical informatics, robotics, and the need for personalized medicine have facilitated the role of AI in modern healthcare. Similarly, as in other fields, AI applications, such as machine learning, artificial neural networks, and deep learning, have shown great potential in andrology and reproductive medicine. AI-based tools are poised to become valuable assets with abilities to support and aid in diagnosing and treating male infertility, and in improving the accuracy of patient care. These automated, AI-based predictions may offer consistency and efficiency in terms of time and cost in infertility research and clinical management. In andrology and reproductive medicine, AI has been used for objective sperm, oocyte, and embryo selection, prediction of surgical outcomes, cost-effective assessment, development of robotic surgery, and clinical decision-making systems. In the future, better integration and implementation of AI into medicine will undoubtedly lead to pioneering evidence-based breakthroughs and the reshaping of andrology and reproductive medicine

Metabolomics-Based Discovery of Diagnostic Biomarkers for Onchocerciasis

Onchocerciasis, caused by the filarial parasite Onchocerca volvulus, afflicts millions of people, causing such debilitating symptoms as blindness and acute dermatitis. There are no accurate, sensitive means of diagnosing O. volvulus infection. Clinical diagnostics are desperately needed in order to achieve the goals of controlling and eliminating onchocerciasis and neglected tropical diseases in general. In this study, a metabolomics approach is introduced for the discovery of small molecule biomarkers that can be used to diagnose O. volvulus infection. Blood samples from O. volvulus infected and uninfected individuals from different geographic regions were compared using liquid chromatography separation and mass spectrometry identification. Thousands of chromatographic mass features were statistically compared to discover 14 mass features that were significantly different between infected and uninfected individuals. Multivariate statistical analysis and machine learning algorithms demonstrated how these biomarkers could be used to differentiate between infected and uninfected individuals and indicate that the diagnostic may even be sensitive enough to assess the viability of worms. This study suggests a future potential of these biomarkers for use in a field-based onchocerciasis diagnostic and how such an approach could be expanded for the development of diagnostics for other neglected tropical diseases

Penentuan Abnormalitas Lintasan Pergerakan Spermatozoa Pada Video Mikroskopis Menggunakan Modifikasi Frame Difference Dan Regresi Linear

Penelitian ini mengusulkan beberapa metode dalam deteksi, menghitung jumlah dan pelacakan lintasan pergerakan spermatozoa berbasis video secara cerdas. Tiga bagian besar yang diteliti, yaitu: penentuan abnormalitas morfologi spermatozoa, abnormalitas motility spermatozoa yang terdiri dari modifikasi background subtraction untuk penjejakan spermatozoa dan penentuan abnormalitas pergerakan spermatozoa berdasarkan lintasan. Pada bagian penentuan abnormalitas morfologi digunakan metode SVM (Support Vector Machine) yang dibandingkan dengan metode K-NN (K-Nearest Neighbour) untuk identifikasi abnormalitas pada bentuk kepala spermtozoa. Pada bagian pelacakan kepala spermatozoa digunakan metode M-Frame Difference. Pada bagian ekstraksi fitur untuk penentuan abnormalitas bentuk kepala spermatozoa antara lain area, eccentricity dan ECD sesudah dilakukan BLOB Analysis. Pada bagian kedua dengan memodifikasi beberapa algoritma background subtraction untuk memisahkan objek sperma dari cairan semen. Penelitian ini melalukan deteksi dan perhitungan spermatozoa yang bergerak pada data video. Untuk melakukan deteksi pada sperma yang bergerak, metode Mixture of Gaussian V2 background subtraction digunakan. Metode ini sesuai dalam kasus deteksi sperma karena data sperma yang digunakan cenderung uni-modal. Penelitian ini juga membandingkan metode background subtraction lainnya dalam melakukan deteksi sperma. Bagian ketiga dilakukan penentuan abnormalitas pergerakan berbasis algoritma regresi linaer pada spermatozoa dalam semen, dari lintasan yang terbentuk dianalisa normal tidaknya pergerakan sperma dalam semen. Dari hasil percobaan yang dilakukan video data spermatozoa manusia, ternyata metode di atas didapat posisi pergerakan spermatozoa hasil penjejakan dikenali bentuk lintasannya berdasarkan rata-rata jarak posisinya terhadap garis regresi linier, dengan threshold RMS sebesar 10 terdapat 10 spermatozoa progresif dan 4 spermatozoa non progresif =============================================================================================== sections were examined, namely: determination of morphological abnormalities of spermatozoa, abnormalities of spermatozoa motility which consisted of the modification of background subtraction for tracking spermatozoa, and determination of abnormalities of spermatozoa movement based on the trajectory. In the determination of morphological abnormalities, SVM (Support Vector Machine) method is used which is then compared to the K-NN (K-Nearest Neighbor) method to identify abnormalities in the spermatozoa's head shape. In the tracking section of the spermatozoa head, the M-Frame Difference method was used. Some extraction features performed to determine spermatozoa head shape abnormalities include area, eccentricity, and ECD after BLOB Analysis. The second part modified some background subtraction algorithms to separate sperm objects from semen. This study detected and calculated moving spermatozoa in video data. To detect the moving sperm, the Mixture of Gaussian V2 background subtraction method is used. This method is suitable in the case of sperm detection because sperm data tends to be uni-modal. This study also compared other background subtraction methods for sperm detection. The third part determined the movement abnormalities based on the linear regression algorithm on spermatozoa in cement; the trajectory formed is analyzed whether the movement of sperm in cement is normal or not. From the results of experiments conducted on human spermatozoa video data, it turns out that the above method obtained the position of spermatozoa tracking results identified by the shape of the track based on the average distance of the linear regression line. With an RMS threshold of 10, there are 10 progressive spermatozoa and 4 nonprogressive spermatozoa

Genetic Analysis of Dog Congenital Deafness and Herding Behavior

Strong artificial selections of canine morphological and behavioral traits lead to the formation of more than 400 modern dog (Canis familiaris, CFA) breeds within the past 300 years. Most dog breeds are derived from small numbers of founders, and this closed genetic pool within each breed results in the high frequency of occurrence of canine congenital disorders. The majority of these heredopathies share common clinical signs with corresponding human diseases. Therefore, dogs are appropriate spontaneous models for studying human diseases. Congenital deafness can cause both health and welfare problems in dogs, and it is quite prevalent among several dog breeds such as Dalmatian, Australian Cattle Dog, English Setter and Australian Stumpy Tail Cattle Dog (ASCD). However, hearing loss causative or associated genes in these dog breeds are not yet identified. The purpose of the study in Chapter 2 was to identify congenital deafness related genes in ASCD. Three bilateral deaf and one normal hearing ASCDs were whole genome sequenced. The publicly available 722 canine whole genome sequences were also used to investigate potential causative mutations in this study. A case-control genome-wide association study (GWAS) was conducted by setting three deafness affected ASCDs as cases, and one unaffected ASCD and 43 additional herding group dogs were used as controls. The GWAS identified several loci on six chromosomes with potential canine deafness association (CFA3, 8, 17, 23, 28 and 37), and most (7 out of 13) of the significantly associated loci were located within CFA37. The private variants unique to three deaf ASCD were filtered by comparison to 722 canine controls of over 144 modern breeds. Subsequent annotation of these variants was performed, only potentially functional variants were filtered resulting in four remaining missense mutations. A missense mutation in the Kruppel-like factor 7 (KLF7) gene (NC_006619.3: g.15562684G>A; XP_022270984.1: p.Leu173Phe) on CFA37 could be emphasized to be associated considering the variant effect prediction and gene function. KLF7 inner ear expression and a corresponding functional impact in development of inner ear and sensory neurons is known. Further genotyping of the KLF7 variant in 28 affected and 27 normal hearing ASCDs still supported its association with ASCD congenital deafness. Dogs have been selectively bred to intensify the performance abilities in regard to diverse tasks such as herding, hunting or companionship. Finally, modern dog breeds vary diversely in not only morphological but also behavioral traits. GWAS analysis of dog morphological traits using breed standard values have been well studied, and many auspicious genes were identified. However, due to the complexity of dog behavior traits, research progress on this topic is still limited. The study of Chapter 3 was intended to elucidate the candidate genes underlying dog behavior traits including herding, predation, temperament and trainability. The phenotype information of these behavioral traits was obtained from American Kennel Club, which classified dog breeds into seven groups (Herding, Hound, Working, Terrier, Toy, Sporting and Non-sporting) based on the behavior, heritage and historical roles. 268 publicly available dog whole genome sequences of 130 modern breeds were used in this study. Four GWASs were performed to investigate potential candidate genes. Dogs with herding behavior were compared with the other dog categories by GWAS. Candidate neurological genes such as THOC1, ASIC2, MSRB3, LLPH, RFX8 and CHL1 were detected within or nearest to the significant loci of herding GWAS. Regarding dog predation behavior, herding behavior is the modified predatory behavior like repression of killing instinct, while hound dogs were selectively bred to enhance predation behaviors. We then use hound and herding group dogs in GWAS to analyze the dog predation behavior. Three neural genes JAK2, MEIS1 and LRRTM4 that were nearest to the significant loci of predation GWAS were revealed as candidates. In temperament GWAS, candidate neurological gene ACSS3 was significantly associated with dog temperament trait. Dog behaviors were reported to be associated with body mass, so we repeated the four GWASs with incorporating dog breed standard body size as covariates. Similar results except for the significant associations of ASIC2, JAK2 and MEIS1 were observed, while these three candidate genes could contribute to dog behaviors through their effects on dog brain architecture. Linkage disequilibrium (LD) analysis of the herding GWAS significant associated signals were also conducted. Promising neurological processes or cellular components were disclosed in GO analysis of potentially functional private genes of herding dogs. In the study described in Chapter 4, one loss of function mutation in ABHD16B was identified to be associated with bull infertility. However, the exact gene function of ABHD16B remains unknown. Western blot was applied to locate ABHD16B protein expression, uncovering its occurrence in bull testis tissue but not in sperm cells. ABHD16B protein owns a function domain of α/β-hydrolase (ABHD) and several ABHD members are involved in lipid metabolism. It is assumed that ABHD16B could play roles in biosynthesis of sperm membrane lipids. Lipidomes of heterozygous and homozygous wild-type bull sperms were analyzed to explore potential aberrations. Several lipid components including PC, DAG, Cer, SM and PC were found significantly altered which verified our hypothesis. Therefore, the imbalanced lipid homeostasis of sperm membrane could be responsible for the bull infertility problem subjected in this study.2021-10-1

Big data analytics for preventive medicine

© 2019, Springer-Verlag London Ltd., part of Springer Nature. Medical data is one of the most rewarding and yet most complicated data to analyze. How can healthcare providers use modern data analytics tools and technologies to analyze and create value from complex data? Data analytics, with its promise to efficiently discover valuable pattern by analyzing large amount of unstructured, heterogeneous, non-standard and incomplete healthcare data. It does not only forecast but also helps in decision making and is increasingly noticed as breakthrough in ongoing advancement with the goal is to improve the quality of patient care and reduces the healthcare cost. The aim of this study is to provide a comprehensive and structured overview of extensive research on the advancement of data analytics methods for disease prevention. This review first introduces disease prevention and its challenges followed by traditional prevention methodologies. We summarize state-of-the-art data analytics algorithms used for classification of disease, clustering (unusually high incidence of a particular disease), anomalies detection (detection of disease) and association as well as their respective advantages, drawbacks and guidelines for selection of specific model followed by discussion on recent development and successful application of disease prevention methods. The article concludes with open research challenges and recommendations

Classification of Breast Cancer Patients Using Somatic Mutation Profiles and Machine Learning Approaches

The high degree of heterogeneity observed in breast cancers makes it very difficult to classify cancer patients into distinct clinical subgroups and consequently limits the ability to devise effective therapeutic strategies. In this study, we explore the use of gene mutation profiles to classify, characterize and predict the subgroups of breast cancers. We analyzed the whole exome sequencing data from 358 ethnically similar breast cancer patients in The Cancer Genome Atlas (TCGA) project. Identified somatic and non-synonymous single nucleotide variants were assigned a quantitative score (C-score) that represents the extent of negative impact on the function of the gene. Using these scores with a non-negative matrix factorization method, we clustered the patients into three subgroups. By comparing the clinical stage of patients among the three subgroups, we identified an early-stage-enriched and a late-stage-enriched subgroup. Comparison of the C-scores (mutation scores) of these subgroups identified 358 genes that carry significantly higher rates of mutations in the late-stage-enriched subgroup. Functional characterization of these genes revealed important functional gene families that carry a heavy mutational load in the late-state-enriched subgroup. Finally, using the identified subgroups, we also developed a supervised classification model to predict the likely stage of patients, given their mutation profiles, hence provide clinical insights to help devise an effective treatment plan. This study demonstrates that gene mutation profiles can be effectively used with machine-learning methods to identify clinically distinguishable subgroups of cancer patients. Genes and gene families that carry a heavy mutational load in late-stage-enriched cancer patients compared to early-stage-enriched subgroup were also identified from functional analysis of genes. The classification model developed in this method could provide a reasonable prediction of the stage of cancer patients solely based on their mutation profiles. This study represents the first use of only somatic mutation profile data to identify and predict breast cancer subgroups and this generic methodology could also be applied to other cancer datasets

Novel cancer biomarkers derived from quantitative phase imaging of biopsy cells

Hlavným cieľom tejto práce je vývoj nových nádorových biomarkerov využiteľných v perzonalizovaných liečbach. Pre pochopenie, prečo je táto problematika dôležitá, slúži stručný popis rakoviny obsahujúcej aj štatistické výsledky za uplynulé roky. Práca taktiež popisuje jednotlivé metódy sveteľnej mikroskopie využiteľné pri analýze buniek a aj následné spracovanie obrazov pozostávajúce zo segmentácie, trackingu, extrakcie príznakov a klasifikácie. V tejto práci sú prezentované príznaky hlavných vlastností buniek, ako je rýchlosť a tvar bunky. Práve tieto príznaky môžu byť potenciálne biomarkery pri liečbe rakoviny.The main objective of this work is the development of novel cancer biomarkers usable in personalized treatments. To understand why this issue is important, a brief description of cancer, including statistical results over the past years, is provided. The work also describes individual methods of light microscopy that can be used in cell analysis and subsequent image processing consisting of segmentation, tracking, feature extraction and classification. In this work, the main cell features, such as cell motility and shape, are presented. These features can be potential biomarkers in the treatment of cancer.