Μελέτη της χρήσης της τεχνητής νοημοσύνης (artificial intelligence) στην έγκαιρη διάγνωση του καρκίνου δέρματος

Η Τεχνητή Νοημοσύνη έχει ενσωματωθεί σε πολλές πτυχές της καθημερινότητας μας και η Ιατρική δεν θα μπορούσε να αποτελέσει εξαίρεση. Πλειάδα επιστημονικών άρθρων έχουν εξερευνήσει την εφαρμογή της Τεχνητής Νοημοσύνης σε τομείς όπως η Οφθαλμολογία και η Ακτινολογία, ενώ υπάρχουν ήδη εγκεκριμένες εφαρμογές Τεχνητής Νοημοσύνης οι οποίες χρησιμοποιούνται στην καθημερινή κλινική πράξη. Κατά τη διάρκεια της διδακτορικής μας διατριβής επιχειρήσαμε να διερευνήσουμε (1) Το κατά πόσον μπορεί η Τεχνητή Νοημοσύνη να χρησιμοποιηθεί στην έγκαιρη διάγνωση του καρκίνου δέρματος; (2) Ποια είναι τα μειονεκτήματα των εφαρμογών της Τεχνητής Νοημοσύνης στη δερματολογία μέχρι στιγμής και πως μπορούν να βελτιωθούν; (3) Που θα μπορούσε δυνητικά να βρει εφαρμογή η Τεχνητή Νοημοσύνη στη δερματολογία και με ποιο τρόπο θα ήταν χρήσιμη; (4) Ποια είναι η βέλτιστη προσέγγιση και ερμηνεία των αποτελεσμάτων που παράγονται τόσο από την έρευνα, όσο και από την εφαρμογή της Τεχνητής Νοημοσύνης στη δερματολογία; Και (5) Πως μπορούμε να βελτιώσουμε τη διαγνωστική ακρίβεια τόσο των κλινικών ιατρών, όσο και των αλγορίθμων Τεχνητής Νοημοσύνης για την έγκαιρη διάγνωση του καρκίνου δέρματος και ειδικότερα του μελανώματος; Τέλος, επιχειρήσαμε να περιγράψουμε το πλαίσιο, εντός του οποίου, οι αλγόριθμοι Τεχνητής Νοημοσύνης θα μπορούσαν να φανούν χρήσιμοι στην κλινική πράξη, προς όφελος των ασθενών.Artificial Intelligence (AI) has been incorporated in a wide spectrum of our daily lives, and Medicine could be no exemption to it. A plurality of scientific articles has explored the application of AI in fields such as Ophthalmology and Radiology, while there are already FDA approved, AI applications, which are used in clinical practice. Through our research we explored: (1) Can AI be used in early skin cancer diagnosis? (2) Which are the pitfalls of AI algorithms in Dermatology and in which possible ways could they be improved upon? (3) How could AI be of use in Dermatology, and in which ways could it be used? (4) Which is the best approach to the research conducted with regards to AI algorithms in early skin cancer diagnosis, and how should these results be interpreted? And (5), how can we improve the diagnostic accuracy of clinicians and AI algorithms for early skin cancer diagnosis and more specifically, melanoma? Finally, we attempted to describe the overall framework, within which, AI algorithms could be proven useful in clinical practice, and more importantly, beneficial to the patients

The rotterdam study: 2014 objectives and design update

The Rotterdam Study is a prospective cohort study ongoing since 1990 in the city of Rotterdam in The Netherlands. The study targets cardiovascular, endocrine, hepatic, neurological, ophthalmic, psychiatric, dermatological, oncological, and respiratory diseases. As of 2008, 14,926 subjects aged 45 years or over comprise the Rotterdam Study cohort. The findings of the Rotterdam Study have been presented in over a 1,000 research articles and reports (see www.erasmus-epidemiology.nl/rotterdamstudy). This article gives the rationale of the study and its design. It also presents a summary of the major findings and an update of the objectives and methods

Computer aided classification of histopathological damage in images of haematoxylin and eosin stained human skin

EngD ThesisExcised human skin can be used as a model to assess the potency, immunogenicity and contact sensitivity of potential therapeutics or cosmetics via the assessment of histological damage. The current method of assessing the damage uses traditional manual histological assessment, which is inherently subjective, time consuming and prone to intra-observer variability. Computer aided analysis has the potential to address issues surrounding traditional histological techniques through the application of quantitative analysis. This thesis describes the development of a computer aided process to assess the immune-mediated structural breakdown of human skin tissue. Research presented includes assessment and optimisation of image acquisition methodologies, development of an image processing and segmentation algorithm, identification and extraction of a novel set of descriptive image features and the evaluation of a selected subset of these features in a classification model. A new segmentation method is presented to identify epidermis tissue from skin with varying degrees of histopathological damage. Combining enhanced colour information with general image intensity information, the fully automated methodology segments the epidermis with a mean specificity of 97.7%, a mean sensitivity of 89.4% and a mean accuracy of 96.5% and segments effectively for different severities of tissue damage. A set of 140 feature measurements containing information about the tissue changes associated with different grades of histopathological skin damage were identified and a wrapper algorithm employed to select a subset of the extracted features, evaluating feature subsets based their prediction error for an independent test set in a Naïve Bayes Classifier. The final classification algorithm classified a 169 image set with an accuracy of 94.1%, of these images 20 were an unseen validation set for which the accuracy was 85.0%. The final classification method has a comparable accuracy to the existing manual method, improved repeatability and reproducibility and does not require an experienced histopathologist

Adenokartsinoomi mikrokeskkonna muutuste kvantifitseerimine ja diagnostilise tähenduse hindamine eesnäärmes, rakendades uuemaid digipatoloogilisi programmiarendusi

Väitekirja elektrooniline versioon ei sisalda publikatsiooneEesnäärme adenokartsinoom on meestel diagnoositud pahaloomulistest kasvajatest maailmas sageduselt teisel kohal ning viiendal kohal vähkidest põhjustatud meeste surmadest. Tervise Arengu Instituudi andmetel diagnoositi 2017. aastal Eestis prostata adenokartsinoomi 1113 juhul, mis moodustas 25,2% kõikidest pahaloomulistest kasvajatest. Haiguse diagnoos püstitatakse enne radikaalset prostatektoomiat kasutades biopsiate uuringut. Kuigi eesnäärme bioptaatide käsitluses toimub pidev areng, on jätkuvalt suur tõenäosus, et radikaalse prostatektoomia järgselt muutub kasvaja histoloogiline aste – Gleason’i skoor hinnatakse prostatektioomia materjali alusel raskemaks 23,3% kuni 42,7% juhtudest. Kõikidel bioptaatidel on epiteliaalset komponenti ümbritsev mikrokeskkond, mis potentsiaalselt võib anda täiendavat diagnostilist ja prognostilist informatsiooni. Selle hüpoteesi testimiseks kasutati kahte stromaalset markerit: Masson’i trikroomi ja antud paikme vaates uut markerit anti-FANCM antikeha. Kvantitatiivne stromaalsete muutuste hindamine mikroskoobis on sageli aeganõudev, väljakutseid pakkuv ning hindajast lähtuvalt subjektiivne. Tänapäevaste digitaalse patoloogia lahendustega saaks hinnangu anda kiirelt ja usaldusväärselt. Uurimistöö käigus töötati välja avatud lähtekoodiga programm Pathadin, mis imiteerib patoloogi üldiseid töövõtteid. Arsti poolt treenitud mudel õppis eristama eesnäärme erinevaid struktuure – näärmeid, närve, stroomat, rasva ja optilisi tühimikke, analüüsima neid, kasutama erinevaid filtreid, näiteks värvifiltrit strooma analüüsiks (FANCM, Masson’i trikroom) ning loendama näärmelises komponendis DAB positiivsete rakkude hulka. Mudel sai hakkama oluliste diagnostiliste parameetrite skriinimisega, näiteks tuvastas eesnäärmes edukalt perineuraalse invasiooni ja ekstraprostaatilise leviku. Uurimistöö tulemuste alusel saab välja tuua mitmed olulised järeldused. Esiteks kirjeldab töö süsteemselt ära uue immunohistokeemilise markeri FACNM kasutamise eesnäärmes. Teiseks tõestab, et stromaalset päritolu muutused on Gleason’i skoorist sõltuvad, kuid on piiratud väärtusega madala tundlikkuse tõttu. Kolmandaks näitab, et masinõpe kui mitte veel täielikult usaldusväärne inimesest sõltumatute lõplike diagnooside püstitamisel, võib juba praegu olla abiks histoloogilistes ja teaduslikes uuringutes. Töö kirjelduses esitatud juhendid on universaalsed: neid saab kasutada erinevatel kudedel, nad võiksid julgustada patolooge kasutama arvuti poolt abistatavat diagnostikat ning looma ja arendama oma mudeleid.Prostate adenocarcinoma is the second most frequently diagnosed cancer in males worldwide. According to 2017 data of the Estonian National Institute for Health Development, it was diagnosed in 1113 cases, making 25.2% of all diagnosed malignancies. The standard for the definitive diagnosis prior to the radical prostatectomy is a systematic biopsy sampling. Nevertheless, despite the progress in biopsy sampling, the final Gleason score is upgraded in 23.3% to 42.7% of all radical prostatectomy samples. To increase the concordance between biopsy and prostatectomy, an idea of evaluating the diagnostic significance of microenvironmental changes surrounding the epithelial component has emerged. Two markers were tested to study stromogenic changes — Masson’s trichrome and, a novel in the prostate, anti-FANCM antibody. The precise quantification of histological stains using a microscope is frequently time-consuming, challenging, and depends on human reliability. However, modern digital pathology solutions could perform the analysis quickly and accurately. During the studies, an open-source set of tools under the name of Pathadin and a model for prostate segmentation were developed. The program imitates pathologists' work in its basics: trained by a doctor, it learned to distinguish glands, nerves, stroma, fat, and empty compartments in the prostate to analyze these independently and apply specific filters such as color analysis for FANCM and Masson’s trichrome in the stroma, or DAB positive cell counting in the glandular component. The model was also able to assist in the screening of significant diagnostic features, as the perineural invasion or extraprostatic extension. The work had several essential outcomes. Firstly, a novel in the prostate immunohistochemical marker, FACNM, was systematically described. Secondly, it was shown that stromogenic changes are indeed Gleason dependant yet are of a limited clinical value due to low sensitivity. Thirdly, if not yet fully reliable for independent definitive diagnoses, machine learning can already be beneficial in histological routines and scientific research. The workflow and manuals provided in the manuscript are somewhat universal and can be used for different tissues, encouraging pathologists to test computer-assisted diagnostics and train their own, more advanced, models.https://www.ester.ee/record=b546203

Sustainable Fruit Growing

Fruit production has faced many challenges in recent years as society seeks to increase fruit consumption while increasing safety and reducing the harmful effects of intensive farming practices (e.g., pesticides and fertilizers). In the last 50 years, the population has more than doubled and is expected to grow to 9 billion people by 2050. Per capita consumption of fruit is also increasing during this time and the global fruit industry is facing a major challenge to produce enough fruit in quantity and quality. The need for sustainable production of nutritious food is critical for human and environmental health.This book provides some answers to people who are increasingly concerned about the sustainability of fruit production and the fruit industry as a whole

Cellular Aspects of Cutaneous Inflammation: Clinical and in vitro studies of allergie contact dermatitis and allergie drug eruptions

This thesis is about the application of immunological insights and techniques to improve diagnosis, treatment and follow-up of inflammatory skin diseases, like allergic contact dermatitis (ACD) and allergic drug eruptions (ADE). The cells and mediators involved in cutaneous inflammation, allergic skin reactions in particular, will be discussed below. In chapter 1.2 and 1.3, clinical, epidemiological and immunological aspects of ACD and ADE, respectively, will be discussed. Finally, the various immunological techniques used in this thesis will be summarized in chap· ter 1.4. Healthy, uninflamed skin, with the horny layer as the first barrier, protects the body from loss of cells, fluids and electrolytes and from penetration of harmful substances like chemicals and infectious agents. In inflammatory skin conditions, such as psoriasis, contact allergy and wound healing, this barrier is disrupted or indi· rectly disturbed by an inflammatory infiltrate in the epidermis. Usually, inflammati on is an effective response. It is the basic reaction of living tissue to several types of injuries leading to its complete or incomplete healing. The classical signs of inflammation, irrespective of the tissue(s) involved, are rubor, calor, dolor, tumor and functio laesa, Le. redness, heat, pain, swelling and disturbed function, respectively. However, inflammatory skin disease may become 50 severe and widespread that it intervenes with physiological functions of the skin, like protection against external agents, prevention of water and heat loss, and, in a broader sense, psychosocial functions of the affected individu al. In inflammatory skin diseases, allergic skin reactions included, various resident, recruited and/or recirculating ce lis and their mediators participate. Together they constitute the Skin Immune System (SIS) [1, 2J. The SIS includes cells of the epidermis, the dermis, the blood vessels, the Iymphatics, and their mediators. The cutaneous nervous system interacts with several of these components of the SIS. The various constituents mentioned will now be looked at more closely in the following sections