Robustness of single random phase encoding lensless imaging with camera noise.

In this paper, we assess the noise-susceptibility of coherent macroscopic single random phase encoding (SRPE) lensless imaging by analyzing how much information is lost due to the presence of camera noise. We have used numerical simulation to first obtain the noise-free point spread function (PSF) of a diffuser-based SRPE system. Afterwards, we generated a noisy PSF by introducing shot noise, read noise and quantization noise as seen in a real-world camera. Then, we used various statistical measures to look at how the shared information content between the noise-free and noisy PSF is affected as the camera-noise becomes stronger. We have run identical simulations by replacing the diffuser in the lensless SRPE imaging system with lenses for comparison with lens-based imaging. Our results show that SRPE lensless imaging systems are better at retaining information between corresponding noisy and noiseless PSFs under high camera noise than lens-based imaging systems. We have also looked at how physical parameters of diffusers such as feature size and feature height variation affect the noise robustness of an SRPE system. To the best of our knowledge, this is the first report to investigate noise robustness of SRPE systems as a function of diffuser parameters and paves the way for the use of lensless SRPE systems to improve imaging in the presence of image sensor noise.

3D object tracking using integral imaging with mutual information and Bayesian optimization.

Integral imaging has proven useful for three-dimensional (3D) object visualization in adverse environmental conditions such as partial occlusion and low light. This paper considers the problem of 3D object tracking. Two-dimensional (2D) object tracking within a scene is an active research area. Several recent algorithms use object detection methods to obtain 2D bounding boxes around objects of interest in each frame. Then, one bounding box can be selected out of many for each object of interest using motion prediction algorithms. Many of these algorithms rely on images obtained using traditional 2D imaging systems. A growing literature demonstrates the advantage of using 3D integral imaging instead of traditional 2D imaging for object detection and visualization in adverse environmental conditions. Integral imaging's depth sectioning ability has also proven beneficial for object detection and visualization. Integral imaging captures an object's depth in addition to its 2D spatial position in each frame. A recent study uses integral imaging for the 3D reconstruction of the scene for object classification and utilizes the mutual information between the object's bounding box in this 3D reconstructed scene and the 2D central perspective to achieve passive depth estimation. We build over this method by using Bayesian optimization to track the object's depth in as few 3D reconstructions as possible. We study the performance of our approach on laboratory scenes with occluded objects moving in 3D and show that the proposed approach outperforms 2D object tracking. In our experimental setup, mutual information-based depth estimation with Bayesian optimization achieves depth tracking with as few as two 3D reconstructions per frame which corresponds to the theoretical minimum number of 3D reconstructions required for depth estimation. To the best of our knowledge, this is the first report on 3D object tracking using the proposed approach.

Underwater optical signal detection system using diffuser-based lensless imaging.

We propose a diffuser-based lensless underwater optical signal detection system. The system consists of a lensless one-dimensional (1D) camera array equipped with random phase modulators for signal acquisition and one-dimensional integral imaging convolutional neural network (1DInImCNN) for signal classification. During the acquisition process, the encoded signal transmitted by a light-emitting diode passes through a turbid medium as well as partial occlusion. The 1D diffuser-based lensless camera array is used to capture the transmitted information. The captured pseudorandom patterns are then classified through the 1DInImCNN to output the desired signal. We compared our proposed underwater lensless optical signal detection system with an equivalent lens-based underwater optical signal detection system in terms of detection performance and computational cost. The results show that the former outperforms the latter. Moreover, we use dimensionality reduction on the lensless pattern and study their theoretical computational costs and detection performance. The results show that the detection performance of lensless systems does not suffer appreciably. This makes lensless systems a great candidate for low-cost compressive underwater optical imaging and signal detection.

Three-dimensional integral imaging-based image descattering and recovery using physics informed unsupervised CycleGAN.

Image restoration and denoising has been a challenging problem in optics and computer vision. There has been active research in the optics and imaging communities to develop a robust, data-efficient system for image restoration tasks. Recently, physics-informed deep learning has received wide interest in scientific problems. In this paper, we introduce a three-dimensional integral imaging-based physics-informed unsupervised CycleGAN algorithm for underwater image descattering and recovery using physics-informed CycleGAN (Generative Adversarial Network). The system consists of a forward and backward pass. The base architecture consists of an encoder and a decoder. The encoder takes the clean image along with the depth map and the degradation parameters to produce the degraded image. The decoder takes the degraded image generated by the encoder along with the depth map and produces the clean image along with the degradation parameters. In order to provide physical significance for the input degradation parameter w.r.t a physical model for the degradation, we also incorporated the physical model into the loss function. The proposed model has been assessed under the dataset curated through underwater experiments at various levels of turbidity. In addition to recovering the original image from the degraded image, the proposed algorithm also helps to model the distribution under which the degraded images have been sampled. Furthermore, the proposed three-dimensional Integral Imaging approach is compared with the traditional deep learning-based approach and 2D imaging approach under turbid and partially occluded environments. The results suggest the proposed approach is promising, especially under the above experimental conditions.

Underwater object detection and temporal signal detection in turbid water using 3D-integral imaging and deep learning.

Underwater scattering caused by suspended particles in the water severely degrades signal detection performance and poses significant challenges to the problem of object detection. This paper introduces an integrated dual-function deep learning-based underwater object detection and classification and temporal signal detection algorithm using three-dimensional (3D) integral imaging (InIm) under degraded conditions. The proposed system is an efficient object classification and temporal signal detection system for degraded environments such as turbidity and partial occlusion and also provides the object range in the scene. A camera array captures the underwater objects in the scene and the temporally encoded binary signals transmitted for the purpose of communication. The network is trained using a clear underwater scene without occlusion, whereas test data is collected in turbid water with partial occlusion. Reconstructed 3D data is the input to a You Look Only Once (YOLOv4) neural network for object detection and a convolutional neural network-based bidirectional long short-term memory network (CNN-BiLSTM) is used for temporal optical signal detection. Finally, the transmitted signal is decoded. In our experiments, 3D InIm provides better image reconstruction in a degraded environment over 2D sensing-based methods. Also, reconstructed 3D images segment out the object of interest from occlusions and background which improves the detection accuracy of the network with 3D InIm. To the best of our knowledge, this is the first report that combines deep learning with 3D InIm for simultaneous and integrated underwater object detection and optical signal detection in degraded environments.

Photoluminescence Properties of a Chiral One-Dimensional Silver Chalcogenolate Chain.

Atom-precise metal nanoclusters, which contain a few tens to hundreds of atoms, have drawn significant interest due to their interesting physicochemical properties. Structural analysis reveals a fundamental architecture characterized by a central core or kernel linked to a staple motif with metal-ligand bonding playing a pivotal role. Ligands not only protect the surface but also exert a significant influence in determining the overall assembly of the larger superstructures. The assemblies of nanoclusters are driven by weak interaction between the ligand molecules; it also depends on the ligand type and functional group present. Here, we report an achiral ligand and Ag(I)···Ag(I) interaction-driven spontaneous resolution of silver-thiolate structure, [Ag18(C6H11S)12(CF3COO)6(DMA)2], where silver atoms and cyclohexanethiolate are connected to form a one-dimensional chain with helicity. Notably, silver atoms adopt different types of coordination modes and geometries. The photoluminescence properties of the one-dimensional (1D) chain structure were investigated, and it was found to exhibit excitation-dependent emission properties attributed to hydrogen-bonding interactions. Experimental and theoretical investigations corroborate the presence of triplet-emitting ligand-to-metal charge-transfer transitions.

Artificial intelligence in clinical medicine: catalyzing a sustainable global healthcare paradigm.

As the demand for quality healthcare increases, healthcare systems worldwide are grappling with time constraints and excessive workloads, which can compromise the quality of patient care. Artificial intelligence (AI) has emerged as a powerful tool in clinical medicine, revolutionizing various aspects of patient care and medical research. The integration of AI in clinical medicine has not only improved diagnostic accuracy and treatment outcomes, but also contributed to more efficient healthcare delivery, reduced costs, and facilitated better patient experiences. This review article provides an extensive overview of AI applications in history taking, clinical examination, imaging, therapeutics, prognosis and research. Furthermore, it highlights the critical role AI has played in transforming healthcare in developing nations.

A Case of Disseminated Melioidosis With Cerebritis.

Melioidosis is caused by Burkholderia pseudomallei, a Gram-negative, facultative intracellular bacterium. Because melioidosis can mimic many diseases, it requires more advanced laboratory facilities with the necessary expertise and can become an underdiagnosed yet serious infection with high mortality and morbidity. Our patient is a middle-aged male with new-onset uncontrolled type 2 diabetes mellitus who presented with high-grade fever, productive cough and altered mental status. CT thorax showed diffuse middle and lower zone consolidation while MRI brain noted meningitis with cerebritis. Blood culture grew Burkholderia pseudomallei. The patient was started on meropenem for melioidosis, however, no adequate improvement was seen. In view of this inadequate response, parenteral cotrimoxazole was added. Significant improvement was noted and cotrimoxazole was continued for six months.

A Strategic Research Framework for Defeating Diabetes in India: A 21st-Century Agenda.

Indian people are at high risk for type 2 diabetes (T2DM) even at younger ages and lower body weights. Already 74 million people in India have the disease, and the proportion of those with T2DM is increasing across all strata of society. Unique aspects, related to lower insulin secretion or function, and higher hepatic fat deposition, accompanied by the rise in overweight (related to lifestyle changes) may all be responsible for this unrelenting epidemic of T2DM. Yet, research to understand the causes, pathophysiology, phenotypes, prevention, treatment, and healthcare delivery of T2DM in India seriously lags behind. There are major opportunities for scientific discovery and technological innovation, which if tapped can generate solutions for T2DM relevant to the country's context and make leading contributions to global science. We analyze the situation of T2DM in India, and present a four-pillar (etiology, precision medicine, implementation research, and health policy) strategic research framework to tackle the challenge. We offer key research questions for each pillar, and identify infrastructure needs. India offers a fertile environment for shifting the paradigm from imprecise late-stage diabetes treatment toward early-stage precision prevention and care. Investing in and leveraging academic and technological infrastructures, across the disciplines of science, engineering, and medicine, can accelerate progress toward a diabetes-free nation.

Aspirin vs Clopidogrel: Antiplatelet Agent of Choice for Those With Recent Bleeding or at Risk for Gastrointestinal Bleed.

Antiplatelet agents are used worldwide mainly for primary and secondary prevention of cardiovascular events on a long-term basis for mortality benefit. Gastrointestinal bleeding is a well-known adverse effect. Various factors are to be considered while choosing antiplatelet agents to prevent the risk of bleed and rebleed incidents. These range from deciding on the agent, timing of therapy, underlying indications, coadministration of proton pump inhibitor, etc. At the same time, one must weigh the risks of cardiovascular events secondary to the stoppage of antiplatelet therapy. With this review, we have tried to guide the clinician on decision-making regarding the care of patients on management of acute upper and lower gastrointestinal bleeding, stoppage, restarting of agents, and measures to prevent a recurrence. We have focused on aspirin and clopidogrel as they are among the most widely used antiplatelet agents.

End-to-end integrated pipeline for underwater optical signal detection using 1D integral imaging capture with a convolutional neural network.

Underwater optical signal detection performance suffers from occlusion and turbidity in degraded environments. To tackle these challenges, three-dimensional (3D) integral imaging (InIm) with 4D correlation-based and deep-learning-based signal detection approaches have been proposed previously. Integral imaging is a 3D technique that utilizes multiple cameras to capture multiple perspectives of the scene and uses dedicated algorithms to reconstruct 3D images. However, these systems may require high computational requirements, multiple separate preprocessing steps, and the necessity for 3D image reconstruction and depth estimation of the illuminating modulated light source. In this paper, we propose an end-to-end integrated signal detection pipeline that uses the principle of one-dimensional (1D) InIm to capture angular and intensity of ray information but without the computational burden of full 3D reconstruction and depth estimation of the light source. The system is implemented with a 1D camera array instead of 2D camera array and is trained with a convolutional neural network (CNN). The proposed approach addresses many of the aforementioned shortcomings to improve underwater optical signal detection speed and performance. In our experiment, the temporal-encoded signals are transmitted by a light-emitting diode passing through a turbid and partial occluded environment which are captured by a 1D camera array. Captured video frames containing the spatiotemporal information of the optical signals are then fed into the CNN for signal detection without the need for depth estimation and 3D scene reconstruction. Thus, the entire processing steps are integrated and optimized by deep learning. We compare the proposed approach with the previously reported depth estimated 3D InIm with 3D scene reconstruction and deep learning in terms of computational cost at receiver's end and detection performance. Moreover, a comparison with conventional 2D imaging is also included. The experimental results show that the proposed approach performs well in terms of detection performance and computational cost. To the best of our knowledge, this is the first report on signal detection in degraded environments with computationally efficient end-to-end integrated 1D InIm capture stage with integrated deep learning for classification.

Standardised Critical Care Strategies Improve Outcomes Following Cytoreductive Surgery with Hyperthermic Intraperitoneal Chemotherapy in an Indian Peritoneal Malignancy Centre.

Cytoreductive surgery (CRS) with hyperthermic intraperitoneal chemotherapy (HIPEC) is a major undertaking with profound peri-operative metabolic and haemodynamic alterations. It requires standardised protocols for immediate postoperative intensive care management to improve patient-related outcomes. A retrospective analysis of a prospectively maintained data-base of 244 patients who underwent CRS and HIPEC between June 2017 and July 2022 in our institute was done. Based on the audit, six strategies were implemented, namely, (1) dynamic multiparameter-based IVF therapy to aggressively correct the hyperlactatemia, (2) initiation of IV 20% human albumin infusion from POD-0, (3) correction of serum iCa2+ levels, (4) initiation of diuresis from POD-1, (5) prophylactic use of HFNO immediately post-extubation and (6) serum procalcitonin level-based empiric escalation of IV antibiotics. Patients were divided into two cohorts, pre-protocol group of 145 patients (from June 2017 to December 2020) and post-protocol group comprising of 99 patients (from January 2021 to July 2022), and were analysed for compliance and patient-related outcomes. Implementation of these strategies improved the patient-related outcomes among the two cohorts with significant reduction of Clavien-Dindo grade III/IV complications and improvement in failure to rescue (FTR) index (p < 0.05). There was highly significant reduction in median ICU and hospital stay among the two cohorts (p < 0.001). The formulated protocols of management strategies especially multiparameter-based dynamic fluid therapy, planned diuresis and prophylactic HFNO have improved the outcomes in our patients undergoing CRS and HIPEC.

Hemophagocytic lymphohistiocytosis: A scourge for the physician and bane to the bone marrow.

Background: Hemophagocytic lymphohistiocytosis (HLH) is a condition characterized by hyperinflammation. It can occur due to primary genetic defect or secondary to other etiology such as infection and rheumatological conditions. Clinical features include fever, cytopenia, organomegaly and several laboratory abnormalities. It can be a life-threatening condition secondary to worsening cytopenia and multiorgan dysfunction. Aims and Objectives: To study the clinical profile of HLH in a tertiary care hospital in Southern India. Materials and Methods: Our study has reviewed nine cases of HLH among adult patients presented over 5 years (2017-2022). Results: The majority of our cases were secondary to infection and had a hospital stay over two weeks and with a good response to steroid and immunomodulators. Conclusion: We would like to stress upon the importance of awareness of such a condition so that there can be early suspicion and workup including bone marrow examination, enabling early initiating of specific therapy for this fatal condition.

Résumé Contexte: L'hémophagocytose lymphohistiocytaire (HLH) est une affection caractérisée par une hyperinflammation. Elle peut survenir en raison d'un défaut génétique primaire ou être secondaire à d'autres étiologies telles que l'infection et les affections rhumatologiques. Les caractéristiques cliniques comprennent de la fièvre, une cytopenie, une organomégalie et plusieurs anomalies de laboratoire. Il s'agit d'une affection potentiellement mortelle en raison de l'aggravation de la cytopenie et du dysfonctionnement multi-organes. Objectifs: Étudier le profil clinique de l'HLH dans un hôpital de soins tertiaires du sud de l'Inde. Matériel et méthodes: Notre étude a examiné neuf cas d'HLH chez des patients adultes sur une période de 5 ans (2017-2022). Résultats: La majorité de nos cas étaient secondaires à une infection et ont nécessité une hospitalisation de plus de deux semaines, avec une bonne réponse aux stéroïdes et aux immunomodulateurs. Conclusion: Nous tenons à souligner l'importance de la sensibilisation à cette affection afin qu'il puisse y avoir une suspicion précoce et des examens approfondis, y compris une ponction de moelle osseuse, permettant ainsi de démarrer rapidement une thérapie spécifique pour cette affection mortelle. Mots-clés: Score H, hémophagocytose lymphohistiocytaire, immunosuppression, hémophagocytose lymphohistiocytaire secondaire.

Information theoretic performance evaluation of 3D integral imaging.

Integral imaging (InIm) has proved useful for three-dimensional (3D) object sensing, visualization, and classification of partially occluded objects. This paper presents an information-theoretic approach for simulating and evaluating the integral imaging capture and reconstruction process. We utilize mutual information (MI) as a metric for evaluating the fidelity of the reconstructed 3D scene. Also we consider passive depth estimation using mutual information. We apply this formulation for optimal pitch estimation of integral-imaging capture and reconstruction to maximize the longitudinal resolution. The effect of partial occlusion in integral imaging 3D reconstruction using mutual information is evaluated. Computer simulation tests and experiments are presented.

A Case of Basal Cell Carcinoma With Metastasis to the Pelvic Bone.

Our patient initially presented in 2015 with an ulcerative lesion over the scalp. Fine needle aspiration cytology (FNAC) from a regional enlarged lymph node showed features of metastatic poorly differentiated carcinoma and he underwent wide local excision with functional neck dissection. His next visit was after five years in 2020 with pain in the left hip region. Bone marrow biopsy was reported as metastatic carcinoma morphologically consistent with the patient's known basal cell carcinoma. He received palliative radiotherapy for the same at the hip region followed by platinum-based chemotherapy.

Lowlight object recognition by deep learning with passive three-dimensional integral imaging in visible and long wave infrared wavelengths.

Traditionally, long wave infrared imaging has been used in photon starved conditions for object detection and classification. We investigate passive three-dimensional (3D) integral imaging (InIm) in visible spectrum for object classification using deep neural networks in photon-starved conditions and under partial occlusion. We compare the proposed passive 3D InIm operating in the visible domain with that of the long wave infrared sensing in both 2D and 3D imaging cases for object classification in degraded conditions. This comparison is based on average precision, recall, and miss rates. Our experimental results demonstrate that cold and hot object classification using 3D InIm in the visible spectrum may outperform both 2D and 3D imaging implemented in long wave infrared spectrum for photon-starved and partially occluded scenes. While these experiments are not comprehensive, they demonstrate the potential of 3D InIm in the visible spectrum for low light applications. Imaging in the visible spectrum provides higher spatial resolution, more compact optics, and lower cost hardware compared with long wave infrared imaging. In addition, higher spatial resolution obtained in the visible spectrum can improve object classification accuracy. Our experimental results provide a proof of concept for implementing visible spectrum imaging in place of the traditional LWIR spectrum imaging for certain object recognition tasks.

Sentinel Lymph Node Biopsy by Lymphatic Flare Technique: a Feasibility Study-"The FLAIR Is in the FLARE".

Background: Sentinel lymph node (SLN) biopsy is a standard procedure in evaluating the status of node negative axilla. Numerous techniques have been described in literature. We hereby describe a new technique of intradermal injection of blue dye called the lymphatic flare technique. Methods: The study was conducted in two phases over a year from August 2020 to May 2021with an internal audit to validate and standardize the technique in January 2021. Results: Between August 2020 and December 2020, 32 patients were evaluated for validation of this technique by two senior surgeons, which yielded a SLN identification rate of 93.75% (30 out of 32). After validating, standardizing, and educating the entire surgical team of the technique, another consecutive 27 patients were evaluated. The SLN identification rate increased to 100% (27 out of 27). Overall, SLN positivity for cancer was 16.6% (10 out of 60). Conclusion: SLN identification by the lymphatic flare technique is feasible, accurate, and reproducible.

Adult-onset adrenoleukodystrophy presenting with status epilepticus and psychosis.

Adrenoleukodystrophy (ALD) is an X linked recessive genetic disorder caused by an abnormality in the ABCD1 gene on the X chromosome, that affects 1 in 20 000 people. In X linked adrenoleukodystrophy (X-ALD), a defect in lignoceroyl-coenzyme A ligase causes pathognomonic tissue accumulation of very long chain fatty acids (VLCFA) in the adrenal cortex and nervous system. The phenotypic variability ranges from cerebral inflammatory demyelination of childhood onset, leading to death within 5 years, to adults remaining presymptomatic through more than five decades. Our case is that of a man who was previously diagnosed with bipolar affective disorder presented with dystonic posturing. During transit, he had an episode of generalised convulsive status epilepticus. He presented with spasticity and exaggerated reflexes. Three important signs of adrenal insufficiency were observed: hypotension, hyperpigmentation and comatose state. The diagnosis of X-ALD should be considered in young men presenting with gradually progressive unexplained cognitive and behavioural problems, a strong family history, adrenal insufficiency, bilateral upper motor signs with absent ankle reflexes.

Optical signal detection in turbid water using multidimensional integral imaging with deep learning.

Optical signal detection in turbid and occluded environments is a challenging task due to the light scattering and beam attenuation inside the medium. Three-dimensional (3D) integral imaging is an imaging approach which integrates two-dimensional images from multiple perspectives and has proved to be useful for challenging conditions such as occlusion and turbidity. In this manuscript, we present an approach for the detection of optical signals in turbid water and occluded environments using multidimensional integral imaging employing temporal encoding with deep learning. In our experiments, an optical signal is temporally encoded with gold code and transmitted through turbid water via a light-emitting diode (LED). A camera array captures videos of the optical signals from multiple perspectives and performs the 3D signal reconstruction of temporal signal. The convolutional neural network-based bidirectional Long Short-Term Network (CNN-BiLSTM) network is trained with clear water video sequences to perform classification on the binary transmitted signal. The testing data was collected in turbid water scenes with partial signal occlusion, and a sliding window with CNN-BiLSTM-based classification was performed on the reconstructed 3D video data to detect the encoded binary data sequence. The proposed approach is compared to previously presented correlation-based detection models. Furthermore, we compare 3D integral imaging to conventional two-dimensional (2D) imaging for signal detection using the proposed deep learning strategy. The experimental results using the proposed approach show that the multidimensional integral imaging-based methodology significantly outperforms the previously reported approaches and conventional 2D sensing-based methods. To the best of our knowledge, this is the first report on underwater signal detection using multidimensional integral imaging with deep neural networks.

Spatio-temporal continuous gesture recognition under degraded environments: performance comparison between 3D integral imaging (InIm) and RGB-D sensors.

In this paper, we introduce a deep learning-based spatio-temporal continuous human gesture recognition algorithm under degraded conditions using three-dimensional (3D) integral imaging. The proposed system is shown as an efficient continuous human gesture recognition system for degraded environments such as partial occlusion. In addition, we compare the performance between the 3D integral imaging-based sensing and RGB-D sensing for continuous gesture recognition under degraded environments. Captured 3D data serves as the input to a You Look Only Once (YOLOv2) neural network for hand detection. Then, a temporal segmentation algorithm is employed to segment the individual gestures from a continuous video sequence. Following segmentation, the output is fed to a convolutional neural network-based bidirectional long short-term memory network (CNN-BiLSTM) for gesture classification. Our experimental results suggest that the proposed deep learning-based spatio-temporal continuous human gesture recognition provides substantial improvement over both RGB-D sensing and conventional 2D imaging system. To the best of our knowledge, this is the first report of 3D integral imaging-based continuous human gesture recognition with deep learning and the first comparison between 3D integral imaging and RGB-D sensors for this task.