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

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

Glaucomatous vertical vessel density asymmetry of the temporal raphe detected with optical coherence tomography angiography.

Changes in retinal vasculature and ocular circulation may play an important role in the glaucoma development and progression. We evaluated the vertical asymmetry across the temporal raphe of the deep retinal layer vessel density, using swept-source optical coherence tomography angiography (SS-OCTA), and its relationship with the central visual field (VF) loss. Thirty-four eyes of 27 patients with open-angle glaucoma were included. SS-OCTA macular scanning was performed within a 3 × 3 mm (300 × 300 pixels) volume, centred on the fovea. The relationships between the vertical asymmetrical deep retinal vessel density reduction (ADRVD) across the temporal raphe and various ocular parameters were analysed. Twenty-two glaucomatous eyes with ADRVDs had central VF loss. Contrarily, ADRVDs were not found in any of the 12 eyes without central VF loss. Thirteen eyes (59.1%) with central VF loss had ADRVDs topographically corresponding to the central VF loss and macular ganglion cell complex thinning. The glaucomatous eyes with ADRVDs exhibited inferior rather than superior central VF loss (P = 0.032). Thus, ADRVD specifically indicates the glaucomatous central visual loss. Further analysis of ADRVD may improve our understanding on glaucoma pathogenesis, offering new treatment insights

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

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

Click on the link to view

Sex-related differences in chromatic sensitivity

Generally women are believed to be more discriminating than men in the use of colour names and this is often taken to imply superior colour vision. However, if both X-chromosome linked colour deficient males (~8%) and females (<1%) as well as heterozygote female carriers (~15%) are excluded from comparisons, then differences between men and women in red-green colour discrimination have been reported as not being significant (e.g., Pickford, 1944; Hood et al., 2006). We re-examined this question by assessing the performance of 150 males and 150 females on the Colour Assessment and Diagnosis (CAD) test (Rodriguez-Carmona, 2005). This is a sensitive test that yields small colour detection thresholds. The test employs direction-specific, moving, chromatic stimuli embedded in a background of random, dynamic, luminance contrast noise. A four-alternative, forced-choice procedure is employed to measure the subject’s thresholds for detection of colour signals in 16 directions in colour space, while ensuring that the subject cannot make use of any residual luminance contrast signals. In addition, we measured the Rayleigh anomaloscope matches in a subgroup of 111 males and 114 females. All the age-matched males (30.8 ± 9.7) and females (26.7 ± 8.8) had normal colour vision as diagnosed by a battery of conventional colour vision tests. Females with known colour deficient relatives were excluded from the study. Comparisons between the male and female groups revealed no significant differences in anomaloscope midpoints (p=0.709), but a significant difference in matching ranges (p=0.040); females on average tended to have a larger mean range (4.11) than males (3.75). Females also had significantly higher CAD thresholds than males along the red-green (p=0.0004), but not along the yellow-blue discrimination axis. The differences between males and females in red-green discrimination may be related to the heterozygosity in X-linked cone photopigment expression common among females

Longitudinal EEG power in the first postnatal year differentiates autism outcomes

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

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 (7–26%). Ultrasound shows higher sensitivity (63–100%). 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

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

딥러닝을 이용한 녹내장 진단 보조 시스템

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 협동과정 바이오엔지니어링전공, 2021. 2. 김희찬.본 논문에서는 딥 러닝 기반의 진단 보조 시스템을 제안하였다. 새로운 방법이 녹내장 데이터에 적용되었고 결과를 평가하였다. 첫번째 연구에서는 스펙트럼영역 빛간섭단층촬영기(SD-OCT)를 딥 러닝 분류 기를 이용해 분석하였다. 스펙트럼영역 빛간섭단층촬영기는 녹내장으로 인한 구조적 손상을 평가하기 위해 사용하는 장비이다. 분류 알고리즘은 합성 곱 신경망을 이용해 개발 되었으며, 스펙트럼영역 빛간섭단층촬영기의 망막신경섬유층(RNFL)과 황반부 신경절세포내망상층 (GCIPL) 사진을 이용해 학습했다. 제안한 방법은 두개의 이미지를 입력으로 받는 이중입력합성곱신경망(DICNN)이며, 딥 러닝 분류에서 효과적인 것으로 알려져 있다. 이중입력합성곱신경망은 망막신경섬유층 과 신경절세포층 의 두께 지도를 이용하여 학습 됐으며, 학습된 네트워크는 녹내장과 정상 군을 구분한다. 이중입력합성곱신경망은 정확도와 수신기동작특성곡선하면적 (AUC)으로 평가 되었다. 망막신경섬유층과 신경절세포층 두께 지도로 학습된 설계한 딥 러닝 모델을 조기 녹내장과 정상 군을 분류하는 성능을 평가하고 비교하였다. 성능평가 결과 이중입력합성곱신경망은 조기 녹내장을 분류하는데 0.869의 수신기동작특성곡선의넓이와 0.921의 민감도, 0.756의 특이도를 보였다. 두번째 연구에서는 딥 러닝을 이용해 시신경유두사진의 해상도와 대비, 색감, 밝기를 보정하는 방법을 제안하였다. 시신경유두사진은 녹내장을 진단하는데 있어 효과적인 것으로 알려져 있다. 하지만, 녹내장의 진단에서 환자의 나, 작은 동공, 매체 불투명성 등으로 인해 평가가 어려운 경우가 있다. 초 해상도와 보정 알고리즘은 초 해상도 적대적생성신경망을 통해 개발되었다. 원본 고해상도의 시신경 유두 사진은 저해상도 사진으로 축소되고, 보정된 고해상도 시신경유두사진으로 보정 되며, 보정된 사진은 시신경여백의 가시성과 근처 혈관을 잘 보이도록 후처리 알고리즘을 이용한다. 저해상도이미지를 보정된 고해상도이미지로 복원하는 과정을 초해상도적대적신경망을 통해 학습한다. 설계한 네트워크는 신호 대 잡음 비(PSNR)과 구조적유사성(SSIM), 평균평가점(MOS)를 이용해 평가 되었다. 현재의 연구는 딥 러닝이 안과 이미지를 4배 해상도와 구조적인 세부 항목이 잘 보이도록 개선할 수 있다는 것을 보여주었다. 향상된 시신경유두 사진은 시신경의 병리학적인 특성의 진단 정확도를 명확히 향상시킨다. 성능평가결과 평균 PSNR은 25.01 SSIM은 0.75 MOS는 4.33으로 나타났다. 세번째 연구에서는 환자 정보와 안과 영상(시신경유두 사진과 붉은색이 없는 망막신경섬유층 사진)을 이용해 녹내장 의심 환자를 분별하고 녹내장 의심 환자의 발병 연수를 예측하는 딥 러닝 모델을 개발하였다. 임상 데이터들은 녹내장을 진단하거나 예측하는데 유용한 정보들을 가지고 있다. 하지만, 어떻게 다양한 유형의 임상정보들을 조합하는 것이 각각의 환자들에 대해 잠재적인 녹내장을 예측하는데 어떤 영향을 주는지에 대한 연구가 진행 된 적이 없다. 녹내장 의 심자 분류와 발병 년 수 예측은 합성 곱 자동 인코더(CAE)를 비 지도적 특성 추출 기로 사용하고, 기계학습 분류 기와 회귀기를 통해 진행하였다. 설계한 모델은 정확도와 평균제곱오차(MSE)를 통해 평가 되었으며, 이미지 특징과 환자 특징은 조합했을 때 녹내장 의심 환자 분류와 발병 년 수 예측의 성능이 이미지 특징과 환자 특징을 각각 썼을 때보다 성능이 좋았다. 정답과의 MSE는 2.613으로 나타났다. 본 연구에서는 딥 러닝을 이용해 녹내장 관련 임상 데이터 중 망막신경섬유층, 신경절세포층 사진을 녹내장 진단에 이용되었고, 시신경유두 사진은 시신경의 병리학적인 진단 정확도를 높였고, 환자 정보는 보다 정확한 녹내장 의심 환자 분류와 발병 년 수 예측에 이용되었다. 향상된 녹내장 진단 성능은 기술적이고 임상적인 지표들을 통해 검증되었다.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.

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