Revealing speckle obscured living human retinal cells with artificial intelligence assisted adaptive optics optical coherence tomography.

BACKGROUND: In vivo imaging of the human retina using adaptive optics optical coherence tomography (AO-OCT) has transformed medical imaging by enabling visualization of 3D retinal structures at cellular-scale resolution, including the retinal pigment epithelial (RPE) cells, which are essential for maintaining visual function. However, because noise inherent to the imaging process (e.g., speckle) makes it difficult to visualize RPE cells from a single volume acquisition, a large number of 3D volumes are typically averaged to improve contrast, substantially increasing the acquisition duration and reducing the overall imaging throughput. METHODS: Here, we introduce parallel discriminator generative adversarial network (P-GAN), an artificial intelligence (AI) method designed to recover speckle-obscured cellular features from a single AO-OCT volume, circumventing the need for acquiring a large number of volumes for averaging. The combination of two parallel discriminators in P-GAN provides additional feedback to the generator to more faithfully recover both local and global cellular structures. Imaging data from 8 eyes of 7 participants were used in this study. RESULTS: We show that P-GAN not only improves RPE cell contrast by 3.5-fold, but also improves the end-to-end time required to visualize RPE cells by 99-fold, thereby enabling large-scale imaging of cells in the living human eye. RPE cell spacing measured across a large set of AI recovered images from 3 participants were in agreement with expected normative ranges. CONCLUSIONS: The results demonstrate the potential of AI assisted imaging in overcoming a key limitation of RPE imaging and making it more accessible in a routine clinical setting.

The retinal pigment epithelium (RPE) is a single layer of cells within the eye that is crucial for vision. These cells are unhealthy in many eye diseases, and this can result in vision problems, including blindness. Imaging RPE cells in living human eyes is time consuming and difficult with the current technology. Our method substantially speeds up the process of RPE imaging by incorporating artificial intelligence. This enables larger areas of the eye to be imaged more efficiently. Our method could potentially be used in the future during routine eye tests. This could lead to earlier detection and treatment of eye diseases, and the prevention of some causes of blindness.

A diagnostic information based framework for super-resolution and quality assessment of retinal OCT images.

High-resolution (HR) retinal optical coherence tomography (OCT) images are preferred by the ophthalmologists to diagnose retinal diseases. These images can be obtained by dense scanning of the target retinal region during acquisition. However, a dense scanning increases the image acquisition time and introduces motion artefacts, which corrupt diagnostic information. Therefore, researchers have a growing interest in developing image processing techniques to recover HR images from low-resolution (LR) OCT images. In this paper, we present an automated super-resolution (SR) scheme using diagnostic information weighted sparse representation framework to reconstruct HR images from LR OCT images. The proposed method performs fast and reliable reconstruction of the LR images. We also propose a 2D- variational mode decomposition (VMD) based OCT diagnostic distortion measure (QOCT) to quantify diagnostic distortion in the reconstructed OCT images. The SR method is evaluated on clinical grade OCT images with the proposed diagnostic distortion measure along with the conventional non-diagnostic measures like the contrast to noise ratio (CNR), the equivalent number of looks (ENL) and the peak signal to noise ratio (PSNR). The results show an average CNR of 4.07, ENL of 58.96 and PSNR of 27.72 dB. An average score of 1.53 is obtained using the proposed diagnostic distortion measure. Experimental results quantify that the proposed QOCT metric can effectively capture diagnostic distortion.

A novel diagnostic information based framework for super-resolution of retinal fundus images.

Advancements in tele-medicine have led to the development of portable and cheap hand-held retinal imaging devices. However, the images obtained from these devices have low resolution (LR) and poor quality that may not be suitable for retinal disease diagnosis. Therefore, this paper proposes a novel framework for the super-resolution (SR) of the LR fundus images. The method takes into consideration the diagnostic information in the fundus images during the SR process. In this work, SR is performed on the zone of interest of the fundus images. Clinical information of the selected zone is captured using the Shannon entropy, the contrast sensitivity index (CSI), the multi-resolution (MR) intra-band energy and the MR inter-band eigen features. The support vector machine (SVM) classifier is used to decide the clinical significance of the zone. Highly accurate learning based SR method or the bicubic interpolation is applied to the selected zone based on the classification output. The method is tested on the Standard Diabetic Retinopathy Database Calibration level 1 (DIARETDB1) and the Digital Retinal Images for Vessel Extraction (DRIVE) databases. Classification accuracy of 85.22% and 85.77% is achieved for the DIARETDB1 and DRIVE databases respectively. The SR performance of the algorithm is quantified in terms of peak signal to noise ratio (PSNR), structural similarity index measure (SSIM) and computational time. The proposed diagnostic information based SR achieves computational time efficiency without compromising with the high resolution (HR) reconstruction accuracy of the fundus image zones.

Tsallis entropy and sparse reconstructive dictionary learning for exudate detection in diabetic retinopathy.

Computer-assisted automated exudate detection is crucial for large-scale screening of diabetic retinopathy (DR). The motivation of this work is robust and accurate detection of low contrast and isolated hard exudates using fundus imaging. Gabor filtering is first performed to enhance exudate visibility followed by Tsallis entropy thresholding. The obtained candidate exudate pixel map is useful for further removal of falsely detected candidates using sparse-based dictionary learning and classification. Two reconstructive dictionaries are learnt using the intensity, gradient, local energy, and transform domain features extracted from exudate and background patches of the training fundus images. Then, a sparse representation-based classifier separates the true exudate pixels from false positives using least reconstruction error. The proposed method is evaluated on the publicly available e-ophtha EX and standard DR database calibration level 1 (DIARETDB1) databases and high exudate detection performance is achieved. In the e-ophtha EX database, mean sensitivity of 85.80% and positive predictive value of 57.93% are found. For the DIARETDB1 database, an area under the curve of 0.954 is obtained.

PARFLEX--a very useful drug for management of surgical pain.

The aim was to assess and document the efficacy and tolerability of parflex (FDC of aceclofenac with paracetamol and serratiopeptidase) in management of pain and inflammation in adult patients undergoing surgical procedures (or operations). The design was open, prospective, non-comparative and multi-dose study of patients undergoing surgical procedures at a leading, tertiary-care, teaching hospital (setting) in Lucknow, the name being, King George's Medical College, Lucknow 226003. The patients were 50 adult patients of either sex undergoing surgery. They were given 1 tablet twice daily, taken after meals. Treatment duration was for a total of up to 7 days (intervention). Primary efficacy variables were relief from postoperative pain. Secondary efficacy variables were global assessment of efficacy and toleration by patients and treating physicians. Record was made of spontaneously reported adverse events with their nature, intensity and outcome (tolerability). Out of 50 patients, 31% were (ENT), 36% were (Orthopaedic) and 33% were (Gynaecology). They were enroled in this study. The observations made were mean pain score showed significant improvement with study drug - decreasing from 2.66 at baseline to 1.36 after 48 hours, and to 0.8 at the end of study. Composite score for pain, fever and swelling also showed substantial gains visit-on- visit-decreasing from 3.62 at baseline to 2.04 after 48 hours, and to 0.98 at final visit. None of the patients reported any adverse event. Global efficacy assessment was rated as 'excellent or good' by 54% of patients and in 59% of patients by their treating physicians. To conclude, parflex is an effective analgesic, anti-inflammatory drug that has a valuable therapeutic option for controlling pain and inflammation after surgical procedures.

Immunological alterations in pregnant women with acute hepatitis E.

BACKGROUND: Infection with hepatitis E virus (HEV) is a major cause of acute viral hepatitis in several developing countries. Although usually self-limiting and benign, the disease is particularly severe among pregnant women, with mortality rates reaching 15-20%. METHODS: Immune parameters among pregnant women with acute hepatitis E (P-HEV) were investigated and compared with those in non-pregnant patients with hepatitis E (N-HEV), and healthy pregnant (PC) and non-pregnant (NPC) women. RESULTS: Peripheral blood mononuclear cells (PBMC) from P-HEV patients had lower lymphocyte proliferation response to phytohemagglutinin (PHA) than those in the PC and NPC groups. A positive lymphocyte proliferation response to HEV antigen (HEVAg), a mixture of eight peptides derived from HEV proteins, was observed in 7/19 (37%) P-HEV patients, 3/9 (33%) N-HEV patients and only 2/21 (10%) PC and 2/14 (14%) NPC subjects; the stimulation indices in the P-HEV group were similar to the N-HEV group and higher than the PC group. Measurement of cytokine production by PBMC in response to PHA and HEVAg showed a reduction in production of T-helper 1 (Th1) cytokines and an increase in that of Th2 cytokines in the P-HEV group. Cytokine mRNA levels showed similar changes. CONCLUSION: These results show the existence of a Th2 bias in pregnant women with acute hepatitis E. The role of this Th2 bias in the greater severity of hepatitis E among pregnant women needs further investigation.