14,681 research outputs found

    Early diagnosis of cardiovascular diseases in workers: role of standard and advanced echocardiography

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    Cardiovascular disease (CVD) still remains the main cause of morbidity and mortality and consequently early diagnosis is of paramount importance. Working conditions can be regarded as an additional risk factor for CVD. Since different aspects of the job may affect vascular health differently, it is important to consider occupation from multiple perspectives to better assess occupational impacts on health. Standard echocardiography has several targets in the cardiac population, as the assessment of myocardial performance, valvular and/or congenital heart disease, and hemodynamics. Three-dimensional echocardiography gained attention recently as a viable clinical tool in assessing left ventricular (LV) and right ventricular (RV), volume, and shape. Two-dimensional (2DSTE) and, more recently, three-dimensional speckle tracking echocardiography (3DSTE) have also emerged as methods for detection of global and regional myocardial dysfunction in various cardiovascular diseases, and applied to the diagnosis of subtle LV and RV dysfunction. Although these novel echocardiographic imaging modalities have advanced our understanding of LV and RV mechanics, overlapping patterns often show challenges that limit their clinical utility. This review will describe the current state of standard and advanced echocardiography in early detection (secondary prevention) of CVD and address future directions for this potentially important diagnostic strategy

    Pupillometric analysis for assessment of gene therapy in Leber Congenital Amaurosis patients

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    Background: Objective techniques to assess the amelioration of vision in patients with impaired visual function are needed to standardize efficacy assessment in gene therapy trials for ocular diseases. Pupillometry has been investigated in several diseases in order to provide objective information about the visual reflex pathway and has been adopted to quantify visual impairment in patients with Leber Congenital Amaurosis (LCA). In this paper, we describe detailed methods of pupillometric analysis and a case study on three Italian patients affected by Leber Congenital Amaurosis (LCA) involved in a gene therapy clinical trial at two follow-up time-points: 1 year and 3 years after therapy administration. Methods: Pupillary light reflexes (PLR) were measured in patients who had received a unilateral subretinal injection in a clinical gene therapy trial. Pupil images were recorded simultaneously in both eyes with a commercial pupillometer and related software. A program was generated with MATLAB software in order to enable enhanced pupil detection with revision of the acquired images (correcting aberrations due to the inability of these severely visually impaired patients to fixate), and computation of the pupillometric parameters for each stimulus. Pupil detection was performed through Hough Transform and a non-parametric paired statistical test was adopted for comparison. Results: The developed program provided correct pupil detection also for frames in which the pupil is not totally visible. Moreover, it provided an automatic computation of the pupillometric parameters for each stimulus and enabled semi-automatic revision of computerized detection, eliminating the need for the user to manually check frame by frame. With reference to the case study, the amplitude of pupillary constriction and the constriction velocity were increased in the right (treated eye) compared to the left (untreated) eye at both follow-up time-points, showing stability of the improved PLR in the treated eye. Conclusions: Our method streamlined the pupillometric analyses and allowed rapid statistical analysis of a range of parameters associated with PLR. The results confirm that pupillometry is a useful objective measure for the assessment of therapeutic effect of gene therapy in patients with LCA

    Application of the radio-iodine rose bengal test in liver disease in infancy and childhood

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    Longitudinal EEG power in the first postnatal year differentiates autism outcomes

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    An aim of autism spectrum disorder (ASD) research is to identify early biomarkers that inform ASD pathophysiology and expedite detection. Brain oscillations captured in electroencephalography (EEG) are thought to be disrupted as core ASD pathophysiology. We leverage longitudinal EEG power measurements from 3 to 36 months of age in infants at low- and high-risk for ASD to test how and when power distinguishes ASD risk and diagnosis by age 3-years. Power trajectories across the first year, second year, or first three years postnatally were submitted to data-driven modeling to differentiate ASD outcomes. Power dynamics during the first postnatal year best differentiate ASD diagnoses. Delta and gamma frequency power trajectories consistently distinguish infants with ASD diagnoses from others. There is also a developmental shift across timescales towards including higher-frequency power to differentiate outcomes. These findings reveal the importance of developmental timing and trajectory in understanding pathophysiology and classifying ASD outcomes.R01 DC010290 - NIDCD NIH HHS; T32 MH112510 - NIMH NIH HHS; U54 HD090255 - NICHD NIH HHSPublished versio

    Nipple discharge: the state of the art

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    Over 80% of females experience nipple discharge during their life. Differently from lactational (milk production) and physiological (white, green, or yellow), which are usually bilateral and involving multiple ducts, pathologic nipple discharge (PND) is a spontaneous commonly single-duct and unilateral, clear, serous, or bloody secretion. Mostly caused by intraductal papilloma(s) or ductal ectasia, in 5-33% of cases is due to an underlying malignancy. After clinical history and physical examination, mammography is the first step after 39, but its sensitivity is low (7โ€“26%). Ultrasound shows higher sensitivity (63โ€“100%). Nipple discharge cytology is limited by a false negative rate over 50%. Galactography is an invasive technique that may cause discomfort and pain; it can be performed only when the duct discharge is demonstrated at the time of the study, with incomplete/failed examination rate up to 15% and a difficult differentiation between malignant and benign lesions. Ductoscopy, performed under local anesthesia in outpatients, provides a direct visualization of intraductal lesions, allowing for directed excision and facilitating a targeted surgery. Its sensitivity reaches 94%; however, it is available in only few centers and most clinicians are unfamiliar with its use. PND has recently emerged as a new indication for contrast-enhanced breast MRI, showing sensitivity superior to galactography, with an overall sensitivity up to 96%, also allowing tailored surgery. Surgery no longer can be considered the standard approach to PND. We propose a state-of-the art flowchart for the management of nipple discharge, including ductoscopy and breast MRI as best options

    Urinary proteomics using capillary electrophoresis coupled to mass spectrometry for diagnosis and prognosis in kidney diseases

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    Purpose of review: Urine is the most useful of body fluids for biomarker research. Therefore, we have focused on urinary proteomics, using capillary electrophoresis coupled to mass spectrometry, to investigate kidney diseases in recent years. Recent findings: Several urinary proteomics studies for the detection of various kidney diseases have indicated the potential of this approach aimed at diagnostic and prognostic assessment. Urinary protein biomarkers such as collagen fragments, serum albumin, [alpha]-1-antitrypsin, and uromodulin can help to explain the processes involved during disease progression. Summary: Urinary proteomics has been used in several studies in order to identify and validate biomarkers associated with different kidney diseases. These biomarkers, with improved sensitivity and specificity when compared with the current gold standards, provide a significant alternative for diagnosis and prognosis, as well as improving clinical decision-making

    ๋”ฅ๋Ÿฌ๋‹์„ ์ด์šฉํ•œ ๋…น๋‚ด์žฅ ์ง„๋‹จ ๋ณด์กฐ ์‹œ์Šคํ…œ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ํ˜‘๋™๊ณผ์ • ๋ฐ”์ด์˜ค์—”์ง€๋‹ˆ์–ด๋ง์ „๊ณต, 2021. 2. ๊น€ํฌ์ฐฌ.๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๋”ฅ ๋Ÿฌ๋‹ ๊ธฐ๋ฐ˜์˜ ์ง„๋‹จ ๋ณด์กฐ ์‹œ์Šคํ…œ์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ์ƒˆ๋กœ์šด ๋ฐฉ๋ฒ•์ด ๋…น๋‚ด์žฅ ๋ฐ์ดํ„ฐ์— ์ ์šฉ๋˜์—ˆ๊ณ  ๊ฒฐ๊ณผ๋ฅผ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ์ฒซ๋ฒˆ์งธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ŠคํŽ™ํŠธ๋Ÿผ์˜์—ญ ๋น›๊ฐ„์„ญ๋‹จ์ธต์ดฌ์˜๊ธฐ(SD-OCT)๋ฅผ ๋”ฅ ๋Ÿฌ๋‹ ๋ถ„๋ฅ˜ ๊ธฐ๋ฅผ ์ด์šฉํ•ด ๋ถ„์„ํ•˜์˜€๋‹ค. ์ŠคํŽ™ํŠธ๋Ÿผ์˜์—ญ ๋น›๊ฐ„์„ญ๋‹จ์ธต์ดฌ์˜๊ธฐ๋Š” ๋…น๋‚ด์žฅ์œผ๋กœ ์ธํ•œ ๊ตฌ์กฐ์  ์†์ƒ์„ ํ‰๊ฐ€ํ•˜๊ธฐ ์œ„ํ•ด ์‚ฌ์šฉํ•˜๋Š” ์žฅ๋น„์ด๋‹ค. ๋ถ„๋ฅ˜ ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ํ•ฉ์„ฑ ๊ณฑ ์‹ ๊ฒฝ๋ง์„ ์ด์šฉํ•ด ๊ฐœ๋ฐœ ๋˜์—ˆ์œผ๋ฉฐ, ์ŠคํŽ™ํŠธ๋Ÿผ์˜์—ญ ๋น›๊ฐ„์„ญ๋‹จ์ธต์ดฌ์˜๊ธฐ์˜ ๋ง๋ง‰์‹ ๊ฒฝ์„ฌ์œ ์ธต(RNFL)๊ณผ ํ™ฉ๋ฐ˜๋ถ€ ์‹ ๊ฒฝ์ ˆ์„ธํฌ๋‚ด๋ง์ƒ์ธต (GCIPL) ์‚ฌ์ง„์„ ์ด์šฉํ•ด ํ•™์Šตํ–ˆ๋‹ค. ์ œ์•ˆํ•œ ๋ฐฉ๋ฒ•์€ ๋‘๊ฐœ์˜ ์ด๋ฏธ์ง€๋ฅผ ์ž…๋ ฅ์œผ๋กœ ๋ฐ›๋Š” ์ด์ค‘์ž…๋ ฅํ•ฉ์„ฑ๊ณฑ์‹ ๊ฒฝ๋ง(DICNN)์ด๋ฉฐ, ๋”ฅ ๋Ÿฌ๋‹ ๋ถ„๋ฅ˜์—์„œ ํšจ๊ณผ์ ์ธ ๊ฒƒ์œผ๋กœ ์•Œ๋ ค์ ธ ์žˆ๋‹ค. ์ด์ค‘์ž…๋ ฅํ•ฉ์„ฑ๊ณฑ์‹ ๊ฒฝ๋ง์€ ๋ง๋ง‰์‹ ๊ฒฝ์„ฌ์œ ์ธต ๊ณผ ์‹ ๊ฒฝ์ ˆ์„ธํฌ์ธต ์˜ ๋‘๊ป˜ ์ง€๋„๋ฅผ ์ด์šฉํ•˜์—ฌ ํ•™์Šต ๋์œผ๋ฉฐ, ํ•™์Šต๋œ ๋„คํŠธ์›Œํฌ๋Š” ๋…น๋‚ด์žฅ๊ณผ ์ •์ƒ ๊ตฐ์„ ๊ตฌ๋ถ„ํ•œ๋‹ค. ์ด์ค‘์ž…๋ ฅํ•ฉ์„ฑ๊ณฑ์‹ ๊ฒฝ๋ง์€ ์ •ํ™•๋„์™€ ์ˆ˜์‹ ๊ธฐ๋™์ž‘ํŠน์„ฑ๊ณก์„ ํ•˜๋ฉด์  (AUC)์œผ๋กœ ํ‰๊ฐ€ ๋˜์—ˆ๋‹ค. ๋ง๋ง‰์‹ ๊ฒฝ์„ฌ์œ ์ธต๊ณผ ์‹ ๊ฒฝ์ ˆ์„ธํฌ์ธต ๋‘๊ป˜ ์ง€๋„๋กœ ํ•™์Šต๋œ ์„ค๊ณ„ํ•œ ๋”ฅ ๋Ÿฌ๋‹ ๋ชจ๋ธ์„ ์กฐ๊ธฐ ๋…น๋‚ด์žฅ๊ณผ ์ •์ƒ ๊ตฐ์„ ๋ถ„๋ฅ˜ํ•˜๋Š” ์„ฑ๋Šฅ์„ ํ‰๊ฐ€ํ•˜๊ณ  ๋น„๊ตํ•˜์˜€๋‹ค. ์„ฑ๋Šฅํ‰๊ฐ€ ๊ฒฐ๊ณผ ์ด์ค‘์ž…๋ ฅํ•ฉ์„ฑ๊ณฑ์‹ ๊ฒฝ๋ง์€ ์กฐ๊ธฐ ๋…น๋‚ด์žฅ์„ ๋ถ„๋ฅ˜ํ•˜๋Š”๋ฐ 0.869์˜ ์ˆ˜์‹ ๊ธฐ๋™์ž‘ํŠน์„ฑ๊ณก์„ ์˜๋„“์ด์™€ 0.921์˜ ๋ฏผ๊ฐ๋„, 0.756์˜ ํŠน์ด๋„๋ฅผ ๋ณด์˜€๋‹ค. ๋‘๋ฒˆ์งธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋”ฅ ๋Ÿฌ๋‹์„ ์ด์šฉํ•ด ์‹œ์‹ ๊ฒฝ์œ ๋‘์‚ฌ์ง„์˜ ํ•ด์ƒ๋„์™€ ๋Œ€๋น„, ์ƒ‰๊ฐ, ๋ฐ๊ธฐ๋ฅผ ๋ณด์ •ํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ์‹œ์‹ ๊ฒฝ์œ ๋‘์‚ฌ์ง„์€ ๋…น๋‚ด์žฅ์„ ์ง„๋‹จํ•˜๋Š”๋ฐ ์žˆ์–ด ํšจ๊ณผ์ ์ธ ๊ฒƒ์œผ๋กœ ์•Œ๋ ค์ ธ ์žˆ๋‹ค. ํ•˜์ง€๋งŒ, ๋…น๋‚ด์žฅ์˜ ์ง„๋‹จ์—์„œ ํ™˜์ž์˜ ๋‚˜, ์ž‘์€ ๋™๊ณต, ๋งค์ฒด ๋ถˆํˆฌ๋ช…์„ฑ ๋“ฑ์œผ๋กœ ์ธํ•ด ํ‰๊ฐ€๊ฐ€ ์–ด๋ ค์šด ๊ฒฝ์šฐ๊ฐ€ ์žˆ๋‹ค. ์ดˆ ํ•ด์ƒ๋„์™€ ๋ณด์ • ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ์ดˆ ํ•ด์ƒ๋„ ์ ๋Œ€์ ์ƒ์„ฑ์‹ ๊ฒฝ๋ง์„ ํ†ตํ•ด ๊ฐœ๋ฐœ๋˜์—ˆ๋‹ค. ์›๋ณธ ๊ณ ํ•ด์ƒ๋„์˜ ์‹œ์‹ ๊ฒฝ ์œ ๋‘ ์‚ฌ์ง„์€ ์ €ํ•ด์ƒ๋„ ์‚ฌ์ง„์œผ๋กœ ์ถ•์†Œ๋˜๊ณ , ๋ณด์ •๋œ ๊ณ ํ•ด์ƒ๋„ ์‹œ์‹ ๊ฒฝ์œ ๋‘์‚ฌ์ง„์œผ๋กœ ๋ณด์ • ๋˜๋ฉฐ, ๋ณด์ •๋œ ์‚ฌ์ง„์€ ์‹œ์‹ ๊ฒฝ์—ฌ๋ฐฑ์˜ ๊ฐ€์‹œ์„ฑ๊ณผ ๊ทผ์ฒ˜ ํ˜ˆ๊ด€์„ ์ž˜ ๋ณด์ด๋„๋ก ํ›„์ฒ˜๋ฆฌ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ด์šฉํ•œ๋‹ค. ์ €ํ•ด์ƒ๋„์ด๋ฏธ์ง€๋ฅผ ๋ณด์ •๋œ ๊ณ ํ•ด์ƒ๋„์ด๋ฏธ์ง€๋กœ ๋ณต์›ํ•˜๋Š” ๊ณผ์ •์„ ์ดˆํ•ด์ƒ๋„์ ๋Œ€์ ์‹ ๊ฒฝ๋ง์„ ํ†ตํ•ด ํ•™์Šตํ•œ๋‹ค. ์„ค๊ณ„ํ•œ ๋„คํŠธ์›Œํฌ๋Š” ์‹ ํ˜ธ ๋Œ€ ์žก์Œ ๋น„(PSNR)๊ณผ ๊ตฌ์กฐ์ ์œ ์‚ฌ์„ฑ(SSIM), ํ‰๊ท ํ‰๊ฐ€์ (MOS)๋ฅผ ์ด์šฉํ•ด ํ‰๊ฐ€ ๋˜์—ˆ๋‹ค. ํ˜„์žฌ์˜ ์—ฐ๊ตฌ๋Š” ๋”ฅ ๋Ÿฌ๋‹์ด ์•ˆ๊ณผ ์ด๋ฏธ์ง€๋ฅผ 4๋ฐฐ ํ•ด์ƒ๋„์™€ ๊ตฌ์กฐ์ ์ธ ์„ธ๋ถ€ ํ•ญ๋ชฉ์ด ์ž˜ ๋ณด์ด๋„๋ก ๊ฐœ์„ ํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ๊ฒƒ์„ ๋ณด์—ฌ์ฃผ์—ˆ๋‹ค. ํ–ฅ์ƒ๋œ ์‹œ์‹ ๊ฒฝ์œ ๋‘ ์‚ฌ์ง„์€ ์‹œ์‹ ๊ฒฝ์˜ ๋ณ‘๋ฆฌํ•™์ ์ธ ํŠน์„ฑ์˜ ์ง„๋‹จ ์ •ํ™•๋„๋ฅผ ๋ช…ํ™•ํžˆ ํ–ฅ์ƒ์‹œํ‚จ๋‹ค. ์„ฑ๋Šฅํ‰๊ฐ€๊ฒฐ๊ณผ ํ‰๊ท  PSNR์€ 25.01 SSIM์€ 0.75 MOS๋Š” 4.33์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์„ธ๋ฒˆ์งธ ์—ฐ๊ตฌ์—์„œ๋Š” ํ™˜์ž ์ •๋ณด์™€ ์•ˆ๊ณผ ์˜์ƒ(์‹œ์‹ ๊ฒฝ์œ ๋‘ ์‚ฌ์ง„๊ณผ ๋ถ‰์€์ƒ‰์ด ์—†๋Š” ๋ง๋ง‰์‹ ๊ฒฝ์„ฌ์œ ์ธต ์‚ฌ์ง„)์„ ์ด์šฉํ•ด ๋…น๋‚ด์žฅ ์˜์‹ฌ ํ™˜์ž๋ฅผ ๋ถ„๋ณ„ํ•˜๊ณ  ๋…น๋‚ด์žฅ ์˜์‹ฌ ํ™˜์ž์˜ ๋ฐœ๋ณ‘ ์—ฐ์ˆ˜๋ฅผ ์˜ˆ์ธกํ•˜๋Š” ๋”ฅ ๋Ÿฌ๋‹ ๋ชจ๋ธ์„ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. ์ž„์ƒ ๋ฐ์ดํ„ฐ๋“ค์€ ๋…น๋‚ด์žฅ์„ ์ง„๋‹จํ•˜๊ฑฐ๋‚˜ ์˜ˆ์ธกํ•˜๋Š”๋ฐ ์œ ์šฉํ•œ ์ •๋ณด๋“ค์„ ๊ฐ€์ง€๊ณ  ์žˆ๋‹ค. ํ•˜์ง€๋งŒ, ์–ด๋–ป๊ฒŒ ๋‹ค์–‘ํ•œ ์œ ํ˜•์˜ ์ž„์ƒ์ •๋ณด๋“ค์„ ์กฐํ•ฉํ•˜๋Š” ๊ฒƒ์ด ๊ฐ๊ฐ์˜ ํ™˜์ž๋“ค์— ๋Œ€ํ•ด ์ž ์žฌ์ ์ธ ๋…น๋‚ด์žฅ์„ ์˜ˆ์ธกํ•˜๋Š”๋ฐ ์–ด๋–ค ์˜ํ–ฅ์„ ์ฃผ๋Š”์ง€์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๊ฐ€ ์ง„ํ–‰ ๋œ ์ ์ด ์—†๋‹ค. ๋…น๋‚ด์žฅ ์˜ ์‹ฌ์ž ๋ถ„๋ฅ˜์™€ ๋ฐœ๋ณ‘ ๋…„ ์ˆ˜ ์˜ˆ์ธก์€ ํ•ฉ์„ฑ ๊ณฑ ์ž๋™ ์ธ์ฝ”๋”(CAE)๋ฅผ ๋น„ ์ง€๋„์  ํŠน์„ฑ ์ถ”์ถœ ๊ธฐ๋กœ ์‚ฌ์šฉํ•˜๊ณ , ๊ธฐ๊ณ„ํ•™์Šต ๋ถ„๋ฅ˜ ๊ธฐ์™€ ํšŒ๊ท€๊ธฐ๋ฅผ ํ†ตํ•ด ์ง„ํ–‰ํ•˜์˜€๋‹ค. ์„ค๊ณ„ํ•œ ๋ชจ๋ธ์€ ์ •ํ™•๋„์™€ ํ‰๊ท ์ œ๊ณฑ์˜ค์ฐจ(MSE)๋ฅผ ํ†ตํ•ด ํ‰๊ฐ€ ๋˜์—ˆ์œผ๋ฉฐ, ์ด๋ฏธ์ง€ ํŠน์ง•๊ณผ ํ™˜์ž ํŠน์ง•์€ ์กฐํ•ฉํ–ˆ์„ ๋•Œ ๋…น๋‚ด์žฅ ์˜์‹ฌ ํ™˜์ž ๋ถ„๋ฅ˜์™€ ๋ฐœ๋ณ‘ ๋…„ ์ˆ˜ ์˜ˆ์ธก์˜ ์„ฑ๋Šฅ์ด ์ด๋ฏธ์ง€ ํŠน์ง•๊ณผ ํ™˜์ž ํŠน์ง•์„ ๊ฐ๊ฐ ์ผ์„ ๋•Œ๋ณด๋‹ค ์„ฑ๋Šฅ์ด ์ข‹์•˜๋‹ค. ์ •๋‹ต๊ณผ์˜ MSE๋Š” 2.613์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋”ฅ ๋Ÿฌ๋‹์„ ์ด์šฉํ•ด ๋…น๋‚ด์žฅ ๊ด€๋ จ ์ž„์ƒ ๋ฐ์ดํ„ฐ ์ค‘ ๋ง๋ง‰์‹ ๊ฒฝ์„ฌ์œ ์ธต, ์‹ ๊ฒฝ์ ˆ์„ธํฌ์ธต ์‚ฌ์ง„์„ ๋…น๋‚ด์žฅ ์ง„๋‹จ์— ์ด์šฉ๋˜์—ˆ๊ณ , ์‹œ์‹ ๊ฒฝ์œ ๋‘ ์‚ฌ์ง„์€ ์‹œ์‹ ๊ฒฝ์˜ ๋ณ‘๋ฆฌํ•™์ ์ธ ์ง„๋‹จ ์ •ํ™•๋„๋ฅผ ๋†’์˜€๊ณ , ํ™˜์ž ์ •๋ณด๋Š” ๋ณด๋‹ค ์ •ํ™•ํ•œ ๋…น๋‚ด์žฅ ์˜์‹ฌ ํ™˜์ž ๋ถ„๋ฅ˜์™€ ๋ฐœ๋ณ‘ ๋…„ ์ˆ˜ ์˜ˆ์ธก์— ์ด์šฉ๋˜์—ˆ๋‹ค. ํ–ฅ์ƒ๋œ ๋…น๋‚ด์žฅ ์ง„๋‹จ ์„ฑ๋Šฅ์€ ๊ธฐ์ˆ ์ ์ด๊ณ  ์ž„์ƒ์ ์ธ ์ง€ํ‘œ๋“ค์„ ํ†ตํ•ด ๊ฒ€์ฆ๋˜์—ˆ๋‹ค.This paper presents deep learning-based methods for improving glaucoma diagnosis support systems. Novel methods were applied to glaucoma clinical cases and the results were evaluated. In the first study, a deep learning classifier for glaucoma diagnosis based on spectral-domain optical coherence tomography (SD-OCT) images was proposed and evaluated. Spectral-domain optical coherence tomography (SD-OCT) is commonly employed as an imaging modality for the evaluation of glaucomatous structural damage. The classification model was developed using convolutional neural network (CNN) as a base, and was trained with SD-OCT retinal nerve fiber layer (RNFL) and macular ganglion cell-inner plexiform layer (GCIPL) images. The proposed network architecture, termed Dual-Input Convolutional Neural Network (DICNN), showed great potential as an effective classification algorithm based on two input images. DICNN was trained with both RNFL and GCIPL thickness maps that enabled it to discriminate between normal and glaucomatous eyes. The performance of the proposed DICNN was evaluated with accuracy and area under the receiver operating characteristic curve (AUC), and was compared to other methods using these metrics. Compared to other methods, the proposed DICNN model demonstrated high diagnostic ability for the discrimination of early-stage glaucoma patients in normal subjects. AUC, sensitivity and specificity was 0.869, 0.921, 0.756 respectively. In the second study, a deep-learning method for increasing the resolution and improving the legibility of Optic-disc Photography(ODP) was proposed. ODP has been proven to be useful for optic nerve evaluation in glaucoma. But in clinical practice, limited patient cooperation, small pupil or media opacities can limit the performance of ODP. A model to enhance the resolution of ODP images, termed super-resolution, was developed using Super Resolution Generative Adversarial Network(SR-GAN). To train this model, high-resolution original ODP images were transformed into two counterparts: (1) down-scaled low-resolution ODPs, and (2) compensated high-resolution ODPs with enhanced visibility of the optic disc margin and surrounding retinal vessels which were produced using a customized image post-processing algorithm. The SR-GAN was trained to learn and recognize the differences between these two counterparts. The performance of the network was evaluated using Peak Signal to Noise Ratio (PSNR), Structural Similarity (SSIM), and Mean Opinion Score (MOS). The proposed study demonstrated that deep learning can be applied to create a generative model that is capable of producing enhanced ophthalmic images with 4x resolution and with improved structural details. The proposed method can be used to enhance ODPs and thereby significantly increase the detection accuracy of optic disc pathology. The average PSNR, SSIM and MOS was 25.01, 0.75, 4.33 respectively In the third study, a deep-learning model was used to classify suspected glaucoma and to predict subsequent glaucoma onset-year in glaucoma suspects using clinical data and retinal images (ODP & Red-free Fundus RNFL Photo). Clinical data contains useful information about glaucoma diagnosis and prediction. However, no study has been undertaken to investigate how combining different types of clinical information would be helpful for predicting the subsequent course of glaucoma in an individual patient. For this study, image features extracted using Convolutional Auto Encoder (CAE) along with clinical features were used for glaucoma suspect classification and onset-year prediction. The performance of the proposed model was evaluated using accuracy and Mean Squared Error (MSE). Combing the CAE extracted image features and clinical features improved glaucoma suspect classification and on-set year prediction performance as compared to using the image features and patient features separately. The average MSE between onset-year and predicted onset year was 2.613 In this study, deep learning methodology was applied to clinical images related to glaucoma. DICNN with RNFL and GCIPL images were used for classification of glaucoma, SR-GAN with ODP images were used to increase detection accuracy of optic disc pathology, and CAE & machine learning algorithm with clinical data and retinal images was used for glaucoma suspect classification and onset-year predication. The improved glaucoma diagnosis performance was validated using both technical and clinical parameters. The proposed methods as a whole can significantly improve outcomes of glaucoma patients by early detection, prediction and enhancing detection accuracy.Contents Abstract i Contents iv List of Tables vii List of Figures viii Chapter 1 General Introduction 1 1.1 Glaucoma 1 1.2 Deep Learning for Glaucoma Diagnosis 3 1.4 Thesis Objectives 3 Chapter 2 Dual-Input Convolutional Neural Network for Glaucoma Diagnosis using Spectral-Domain Optical Coherence Tomography 6 2.1 Introduction 6 2.1.1 Background 6 2.1.2 Related Work 7 2.2 Methods 8 2.2.1 Study Design 8 2.2.2 Dataset 9 2.2.3 Dual-Input Convolutional Neural Network (DICNN) 15 2.2.4 Training Environment 18 2.2.5 Statistical Analysis 19 2.3 Results 20 2.3.1 DICNN Performance 20 2.3.1 Grad-CAM for DICNN 34 2.4 Discussion 37 2.4.1 Research Significance 37 2.4.2 Limitations 40 2.5 Conclusion 42 Chapter 3 Deep-learning-based enhanced optic-disc photography 43 3.1 Introduction 43 3.1.1 Background 43 3.1.2 Needs 44 3.1.3 Related Work 45 3.2 Methods 46 3.2.1 Study Design 46 3.2.2 Dataset 46 3.2.2.1 Details on Customized Image Post-Processing Algorithm 47 3.2.3 SR-GAN Network 50 3.2.3.1 Design of Generative Adversarial Network 50 3.2.3.2 Loss Functions 55 3.2.4 Assessment of Clinical Implications of Enhanced ODPs 58 3.2.5 Statistical Analysis 60 3.2.6 Hardware Specifications & Software Specifications 60 3.3 Results 62 3.3.1 Training Loss of Modified SR-GAN 62 3.3.2 Performance of Final Network 66 3.3.3 Clinical Validation of Enhanced ODP by MOS comparison 77 3.3.4 Comparison of DH-Detection Accuracy 79 3.4 Discussion 80 3.4.1 Research Significance 80 3.4.2 Limitations 85 3.5 Conclusion 88 Chapter 4 Deep Learning Based Prediction of Glaucoma Onset Using Retinal Image and Patient Data 89 4.1 Introduction 89 4.1.1 Background 89 4.1.2 Related Work 90 4.2 Methods 90 4.2.1 Study Design 90 4.2.2 Dataset 91 4.2.3 Design of Overall System 94 4.2.4 Design of Convolutional Auto Encoder 95 4.2.5 Glaucoma Suspect Classification 97 4.2.6 Glaucoma Onset-Year Prediction 97 4.3 Result 99 4.3.1 Performance of Designed CAE 99 4.3.2 Performance of Designed Glaucoma Suspect Classification 101 4.3.3 Performance of Designed Glaucoma Onset-Year Prediction 105 4.4 Discussion 110 4.4.1 Research Significance 110 4.4.2 Limitations 110 4.5 Conclusion 111 Chapter 5 Summary and Future Works 112 5.1 Thesis Summary 112 5.2 Limitations and Future Works 113 Bibliography 115 Abstract in Korean 127 Acknowledgement 130Docto

    Unimpaired Neuropsychological Performance and Enhanced Memory Recall in Patients with Sbma: A Large Sample Comparative Study.

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    Peculiar cognitive profile of patients with SBMA has been described by fragmented literature. Our retrospective study reports the neuropsychological evaluations of a large cohort of patients in order to contribute towards the understanding of this field. We consider 64 neuropsychological evaluations assessing mnesic, linguistic and executive functions collected from 2013 to 2015 in patients attending at Motor Neuron Disease Centre of University of Padova. The battery consisted in: Digit Span forwards and backwards, Prose Memory test, Phonemic Verbal fluency and Trail making tests. ANCOVA statistics were employed to compare tests scores results with those obtained from a sample of healthy control subjects. Multiple linear regressions were used to study the effect on cognitive performance of CAG-repeat expansion, the degree of androgen insensitivity and their interaction to cognitive performance. Statistical analyses did not reveal altered scores in any neuropsychological tests among those adopted. Interestingly, patients performed significantly better in the Prose Memory test's score. No relevant associations were found with genetic, hormonal or clinical patients' profile. Results inconsistent with previous studies have been interpreted according to the phenomenon of somatic mosaicism. We suggest a testosterone-related and the mood state-dependant perspectives as two possible interpretations of the enhanced performances in the Prose Memory test. Further studies employing more datailed tests batteries are encouraged
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