A Deep Learning Model for Screening Multiple Abnormal Findings in Ophthalmic Ultrasonography (With Video).
Ontology highlight
ABSTRACT: Purpose
The purpose of this study was to construct a deep learning system for rapidly and accurately screening retinal detachment (RD), vitreous detachment (VD), and vitreous hemorrhage (VH) in ophthalmic ultrasound in real time.Methods
We used a deep convolutional neural network to develop a deep learning system to screen multiple abnormal findings in ophthalmic ultrasonography with 3580 images for classification and 941 images for segmentation. Sixty-two videos were used as the test dataset in real time. External data containing 598 images were also used for validation. Another 155 images were collected to compare the performance of the model to experts. In addition, a study was conducted to assess the effect of the model in improving lesions recognition of the trainees.Results
The model achieved 0.94, 0.90, 0.92, 0.94, and 0.91 accuracy in recognizing normal, VD, VH, RD, and other lesions. Compared with the ophthalmologists, the modal achieved a 0.73 accuracy in classifying RD, VD, and VH, which has a better performance than most experts (P < 0.05). In the videos, the model had a 0.81 accuracy. With the model assistant, the accuracy of the trainees improved from 0.84 to 0.94.Conclusions
The model could serve as a screening tool to rapidly identify patients with RD, VD, and VH. In addition, it also has potential to be a good tool to assist training.Translational relevance
We developed a deep learning model to make the ultrasound work more accurately and efficiently.
SUBMITTER: Chen D
PROVIDER: S-EPMC8083108 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Detection, diagnosis, and treatment of ophthalmic diseases depend on extraction of information (features and/or their dimensions) from the images. Deep learning (DL) model are crucial for the automation of it. Here, we report on the development of a lightweight DL model, which can precisely segment/detect the required features automatically. The model utilizes dimensionality reduction of image to extract important features, and channel contraction to allow only the required high-level features necessary for reconstruction of segmented feature image. Performance of present model in detection of glaucoma from optical coherence tomography angiography (OCTA) images of retina is high (area under the receiver-operator characteristic curve AUC ~ 0.81). Bland-Altman analysis gave exceptionally low bias (~ 0.00185), and high Pearson's correlation coefficient (p = 0.9969) between the parameters determined from manual and DL based segmentation. On the same dataset, bias is an order of magnitude higher (~ 0.0694, p = 0.8534) for commercial software. Present model is 10 times lighter than Unet (popular for biomedical image segmentation) and have a better segmentation accuracy and model training reproducibility (based on the analysis of 3670 OCTA images). High dice similarity coefficient (D) for variety of ophthalmic images suggested it's wider scope in precise segmentation of images even from other fields. Our concept of channel narrowing is not only important for the segmentation problems, but it can also reduce number of parameters significantly in object classification models. Enhanced disease diagnostic accuracy can be achieved for the resource limited devices (such as mobile phone, Nvidia's Jetson, Raspberry pi) used in self-monitoring, and tele-screening (memory size of trained model ~ 35 MB).
| S-EPMC9122907 | biostudies-literature
Project description:Background/aimsTo characterise the ocular manifestations of Williams-Beuren syndrome (WBS) and compare these to patients with isolated elastin mediated supravalvular aortic stenosis (SVAS).MethodsFifty-seven patients with a diagnosis of WBS and five with SVAS underwent comprehensive ophthalmic evaluation at the National Institutes of Health from 2017 to 2020, including best-corrected visual acuity, slit-lamp biomicroscopy, optical biometry, dilated fundus examination, optical coherence tomography and colour fundus imaging.ResultsMean age of the 57 WBS patients was 20.3 years (range 3-60 years). Best-corrected visual acuity ranged from 20/20 to 20/400 with mean spherical equivalent near plano OU. Twenty-four eyes (21.8%) had an axial length (AL) less than 20.5 mm and 38 eyes (34.5%) had an AL measuring 20.5-22.0 mm. Stellate iris and retinal arteriolar tortuosity were noted in 30 (52.6%) and 51 (89.5%) WBS patients, respectively. Novel retinal findings in WBS included small hypopigmented retinal deposits (OD 29/57, OS 27/57) and broad foveal pit contour (OD 44/55, OS 42/51). Of the five patients with SVAS, none had stellate iris or broad foveal pit contour while 2/5 had retinal arteriolar tortuosity.ConclusionWBS is a complex multisystem genetic disorder with diverse ophthalmic findings that differ from those seen in isolated elastin mediated SVAS. These results suggest other genes within the WBS critical region, aside from ELN, may be involved in observed ocular phenotypes and perhaps broader ocular development. Furthermore, retinal arteriolar tortuosity may provide future insight into systemic vascular findings in WBS.
| S-EPMC10074447 | biostudies-literature
Project description:Deep learning (DL) is the new high-profile technology in medical artificial intelligence (AI) for building screening and diagnosing algorithms for various diseases. The eye provides a window for observing neurovascular pathophysiological changes. Previous studies have proposed that ocular manifestations indicate systemic conditions, revealing a new route in disease screening and management. There have been multiple DL models developed for identifying systemic diseases based on ocular data. However, the methods and results varied immensely across studies. This systematic review aims to summarize the existing studies and provide an overview of the present and future aspects of DL-based algorithms for screening systemic diseases based on ophthalmic examinations. We performed a thorough search in PubMed®, Embase, and Web of Science for English-language articles published until August 2022. Among the 2873 articles collected, 62 were included for analysis and quality assessment. The selected studies mainly utilized eye appearance, retinal data, and eye movements as model input and covered a wide range of systemic diseases such as cardiovascular diseases, neurodegenerative diseases, and systemic health features. Despite the decent performance reported, most models lack disease specificity and public generalizability for real-world application. This review concludes the pros and cons and discusses the prospect of implementing AI based on ocular data in real-world clinical scenarios.
| S-EPMC10001234 | biostudies-literature
Project description:ObjectivesBiosignal data captured by patient monitoring systems could provide key evidence for detecting or predicting critical clinical events; however, noise in these data hinders their use. Because deep learning algorithms can extract features without human annotation, this study hypothesized that they could be used to screen unacceptable electrocardiograms (ECGs) that include noise. To test that, a deep learning-based model for unacceptable ECG screening was developed, and its screening results were compared with the interpretations of a medical expert.MethodsTo develop and apply the screening model, we used a biosignal database comprising 165,142,920 ECG II (10-second lead II electrocardiogram) data gathered between August 31, 2016 and September 30, 2018 from a trauma intensive-care unit. Then, 2,700 and 300 ECGs (ratio of 9:1) were reviewed by a medical expert and used for 9-fold cross-validation (training and validation) and test datasets. A convolutional neural network-based model for unacceptable ECG screening was developed based on the training and validation datasets. The model exhibiting the lowest cross-validation loss was subsequently selected as the final model. Its performance was evaluated through comparison with a test dataset.ResultsWhen the screening results of the proposed model were compared to the test dataset, the area under the receiver operating characteristic curve and the F1-score of the model were 0.93 and 0.80 (sensitivity = 0.88, specificity = 0.89, positive predictive value = 0.74, and negative predictive value = 0.96).ConclusionsThe deep learning-based model developed in this study is capable of detecting and screening unacceptable ECGs efficiently.
| S-EPMC6689506 | biostudies-literature
Project description:Single-pixel cameras capture images without the requirement for a multi-pixel sensor, enabling the use of state-of-the-art detector technologies and providing a potentially low-cost solution for sensing beyond the visible spectrum. One limitation of single-pixel cameras is the inherent trade-off between image resolution and frame rate, with current compressive (compressed) sensing techniques being unable to support real-time video. In this work we demonstrate the application of deep learning with convolutional auto-encoder networks to recover real-time 128?×?128 pixel video at 30 frames-per-second from a single-pixel camera sampling at a compression ratio of 2%. In addition, by training the network on a large database of images we are able to optimise the first layer of the convolutional network, equivalent to optimising the basis used for scanning the image intensities. This work develops and implements a novel approach to solving the inverse problem for single-pixel cameras efficiently and represents a significant step towards real-time operation of computational imagers. By learning from examples in a particular context, our approach opens up the possibility of high resolution for task-specific adaptation, with importance for applications in gas sensing, 3D imaging and metrology.
| S-EPMC5799195 | biostudies-literature
Project description:Owing to their limited accuracy and narrow applicability, current antimicrobial peptide (AMP) prediction models face obstacles in industrial application. To address these limitations, we developed and improved an AMP prediction model using Comparing and Optimizing Multiple DEep Learning (COMDEL) algorithms, coupled with high-throughput AMP screening method, finally reaching an accuracy of 94.8% in test and 88% in experiment verification, surpassing other state-of-the-art models. In conjunction with COMDEL, we employed the phage-assisted evolution method to screen Sortase in vivo and developed a cell-free AMP synthesis system in vitro, ultimately increasing AMPs yields to a range of 0.5-2.1 g/L within hours. Moreover, by multi-omics analysis using COMDEL, we identified Lactobacillus plantarum as the most promising candidate for AMP generation among 35 edible probiotics. Following this, we developed a microdroplet sorting approach and successfully screened three L. plantarum mutants, each showing a twofold increase in antimicrobial ability, underscoring their substantial industrial application values.
| S-EPMC11372459 | biostudies-literature
Project description:ImportanceInterstitial fibrosis and tubular atrophy (IFTA) is a strong indicator of decline in kidney function and is measured using histopathological assessment of kidney biopsy core. At present, a noninvasive test to assess IFTA is not available.ObjectiveTo develop and validate a deep learning (DL) algorithm to quantify IFTA from kidney ultrasonography images.Design, setting, and participantsThis was a single-center diagnostic study of consecutive patients who underwent native kidney biopsy at John H. Stroger Jr. Hospital of Cook County, Chicago, Illinois, between January 1, 2014, and December 31, 2018. A DL algorithm was trained, validated, and tested to classify IFTA from kidney ultrasonography images. Of 6135 Crimmins-filtered ultrasonography images, 5523 were used for training (5122 images) and validation (401 images), and 612 were used to test the accuracy of the DL system. Kidney segmentation was performed using the UNet architecture, and classification was performed using a convolution neural network-based feature extractor and extreme gradient boosting. IFTA scored by a nephropathologist on trichrome stained kidney biopsy slide was used as the reference standard. IFTA was divided into 4 grades (grade 1, 0%-24%; grade 2, 25%-49%; grade 3, 50%-74%; and grade 4, 75%-100%). Data analysis was performed from December 2019 to May 2020.Main outcomes and measuresPrediction of IFTA grade was measured using the metrics precision, recall, accuracy, and F1 score.ResultsThis study included 352 patients (mean [SD] age 47.43 [14.37] years), of whom 193 (54.82%) were women. There were 159 patients with IFTA grade 1 (2701 ultrasonography images), 74 patients with IFTA grade 2 (1239 ultrasonography images), 41 patients with IFTA grade 3 (701 ultrasonography images), and 78 patients with IFTA grade 4 (1494 ultrasonography images). Kidney ultrasonography images were segmented with 91% accuracy. In the independent test set, the point estimates for performance matrices showed precision of 0.8927 (95% CI, 0.8682-0.9172), recall of 0.8037 (95% CI, 0.7722-0.8352), accuracy of 0.8675 (95% CI, 0.8406-0.8944), and an F1 score of 0.8389 (95% CI, 0.8098-0.8680) at the image level. Corresponding estimates at the patient level were precision of 0.9003 (95% CI, 0.8644-0.9362), recall of 0.8421 (95% CI, 0.7984-0.8858), accuracy of 0.8955 (95% CI, 0.8589-0.9321), and an F1 score of 0.8639 (95% CI, 0.8228-0.9049). Accuracy at the patient level was highest for IFTA grade 1 and IFTA grade 4. The accuracy (approximately 90%) remained high irrespective of the timing of ultrasonography studies and the biopsy diagnosis. The predictive performance of the DL system did not show significant improvement when combined with baseline clinical characteristics.Conclusions and relevanceThese findings suggest that a DL algorithm can accurately and independently predict IFTA from kidney ultrasonography images.
| S-EPMC8144924 | biostudies-literature
Project description:A plethora of classification models for the detection of glaucoma from fundus images have been proposed in recent years. Often trained with data from a single glaucoma clinic, they report impressive performance on internal test sets, but tend to struggle in generalizing to external sets. This performance drop can be attributed to data shifts in glaucoma prevalence, fundus camera, and the definition of glaucoma ground truth. In this study, we confirm that a previously described regression network for glaucoma referral (G-RISK) obtains excellent results in a variety of challenging settings. Thirteen different data sources of labeled fundus images were utilized. The data sources include two large population cohorts (Australian Blue Mountains Eye Study, BMES and German Gutenberg Health Study, GHS) and 11 publicly available datasets (AIROGS, ORIGA, REFUGE1, LAG, ODIR, REFUGE2, GAMMA, RIM-ONEr3, RIM-ONE DL, ACRIMA, PAPILA). To minimize data shifts in input data, a standardized image processing strategy was developed to obtain 30° disc-centered images from the original data. A total of 149,455 images were included for model testing. Area under the receiver operating characteristic curve (AUC) for BMES and GHS population cohorts were at 0.976 [95% CI: 0.967-0.986] and 0.984 [95% CI: 0.980-0.991] on participant level, respectively. At a fixed specificity of 95%, sensitivities were at 87.3% and 90.3%, respectively, surpassing the minimum criteria of 85% sensitivity recommended by Prevent Blindness America. AUC values on the eleven publicly available data sets ranged from 0.854 to 0.988. These results confirm the excellent generalizability of a glaucoma risk regression model trained with homogeneous data from a single tertiary referral center. Further validation using prospective cohort studies is warranted.
| S-EPMC10264390 | biostudies-literature
Project description:In the contemporary world, security and safety are significant concerns for any country that wants to succeed in tourism, attracting investors, and economics. Manually, guards monitoring 24/7 for robberies or crimes becomes an exhaustive task, and real-time response is essential and helpful for preventing armed robberies at banks, casinos, houses, and ATMs. This paper presents a study based on real-time object detection systems for weapons auto-detection in video surveillance systems. We propose an early weapon detection framework using state-of-the-art, real-time object detection systems such as YOLO and SSD (Single Shot Multi-Box Detector). In addition, we considered closely reducing the number of false alarms in order to employ the model in real-life applications. The model is suitable for indoor surveillance cameras in banks, supermarkets, malls, gas stations, and so forth. The model can be employed as a precautionary system to prevent robberies by implying the model in outdoor surveillance cameras.
| S-EPMC10188611 | biostudies-literature
Project description:Establishing early warning systems for anthrax attacks is crucial in biodefense. Despite numerous studies for decades, the limited sensitivity of conventional biochemical methods essentially requires preprocessing steps and thus has limitations to be used in realistic settings of biological warfare. We present an optical method for rapid and label-free screening of Bacillus anthracis spores through the synergistic application of holographic microscopy and deep learning. A deep convolutional neural network is designed to classify holographic images of unlabeled living cells. After training, the network outperforms previous techniques in all accuracy measures, achieving single-spore sensitivity and subgenus specificity. The unique "representation learning" capability of deep learning enables direct training from raw images instead of manually extracted features. The method automatically recognizes key biological traits encoded in the images and exploits them as fingerprints. This remarkable learning ability makes the proposed method readily applicable to classifying various single cells in addition to B. anthracis, as demonstrated for the diagnosis of Listeria monocytogenes, without any modification. We believe that our strategy will make holographic microscopy more accessible to medical doctors and biomedical scientists for easy, rapid, and accurate point-of-care diagnosis of pathogens.
| S-EPMC5544395 | biostudies-other