Comput Biol Med. 2024 Dec 4;185:109494. doi: 10.1016/j.compbiomed.2024.109494. Online ahead of print.

ABSTRACT

Cancer, a global health threat, demands effective diagnostic solutions to combat its impact on public health, particularly for breast, colon, and lung cancers. Early and accurate diagnosis is essential for successful treatment, prompting the rise of Computer-Aided Diagnosis Systems as reliable and cost-effective tools. Histopathology, renowned for its precision in cancer imaging, has become pivotal in the diagnostic landscape of breast, colon, and lung cancers. However, while deep learning models have been widely explored in this domain, they often face challenges in generalizing to diverse clinical settings and in efficiently capturing both local and global feature representations, particularly for multi-class tasks. This underscores the need for models that can reduce biases, improve diagnostic accuracy, and minimize error susceptibility in cancer classification tasks. To this end, we introduce ResoMergeNet (RMN), an advanced deep-learning model designed for both multi-class and binary cancer classification using histopathological images of breast, colon, and lung. ResoMergeNet integrates the Resboost mechanism which enhances feature representation, and the ConvmergeNet mechanism which optimizes feature extraction, leading to improved diagnostic accuracy. Comparative evaluations against state-of-the-art models show ResoMergeNet's superior performance. Validated on the LC-25000 and BreakHis (400× and 40× magnifications) datasets, ResoMergeNet demonstrates outstanding performance, achieving perfect scores of 100 % in accuracy, sensitivity, precision, and F1 score for binary classification. For multi-class classification with five classes from the LC25000 dataset, it maintains an impressive 99.96 % across all performance metrics. When applied to the BreakHis dataset, ResoMergeNet achieved 99.87 % accuracy, 99.75 % sensitivity, 99.78 % precision, and 99.77 % F1 score at 400× magnification. At 40× magnification, it still delivered robust results with 98.85 % accuracy, sensitivity, precision, and F1 score. These results emphasize the efficacy of ResoMergeNet, marking a substantial advancement in diagnostic and prognostic systems for breast, colon, and lung cancers. ResoMergeNet's superior diagnostic accuracy can significantly reduce diagnostic errors, minimize human biases, and expedite clinical workflows, making it a valuable tool for enhancing cancer diagnosis and treatment outcomes.

PMID:39637456 | DOI:10.1016/j.compbiomed.2024.109494

Biomed Phys Eng Express. 2024 Dec 5. doi: 10.1088/2057-1976/ad9aab. Online ahead of print.

ABSTRACT

Cardiovascular diseases rank among the leading causes of mortality worldwide and the early identification of diseases is of paramount importance. This work focuses on developing a novel machine learning-based framework for early detection and classification of heart murmurs by analysing phonocardiogram signals. Our heart murmur detection and classification pipeline encompasses three classification settings. We first develop a set of methods based on transfer learning to determine the existence of heart murmurs and categorize them as present, absent, or unknown. If a murmur is present it will be classified as normal or abnormal based on its clinical
outcome by using 1D convolution and audio spectrogram transformers. Finally, we use Wav2Vec encoder with raw audio data and AdaBoost abstain classifier for heart murmur quality identification. Heart murmurs are categorized based on their specific attributes, including murmur pitch, murmur shape, and murmur timing which are important for diagnosis. Using the PhysioNet 2022 dataset for training and validation, we achieve an 81.08% validation accuracy for murmur presence classification and a 68.23% validation accuracy for clinical outcome classification with 60.52% sensitivity and 74.46% specificity. The suggested approaches provide a promising framework for using phonocardiogram signals for the detection, classification, and quality analysis of heart murmurs. This has significant implications for the diagnosis and treatment of cardiovascular diseases.

PMID:39637440 | DOI:10.1088/2057-1976/ad9aab

PLoS One. 2024 Dec 5;19(12):e0311967. doi: 10.1371/journal.pone.0311967. eCollection 2024.

ABSTRACT

Environmental challenges are rarely confined to national, disciplinary, or linguistic domains. Convergent solutions require international collaboration and equitable access to new technologies and practices. The ability of international, multidisciplinary and multilingual research teams to work effectively can be challenging. A major impediment to innovation in diverse teams often stems from different understandings of the terminology used. These can vary greatly according to the cultural and disciplinary backgrounds of the team members. In this paper we take an empirical approach to examine sources of terminological confusion and their effect in a technically innovative, multidisciplinary, multinational, and multilingual research project, adhering to Open Science principles. We use guided reflection of participant experience in two contrasting teams-one applying Deep Learning (Artificial Intelligence) techniques, the other developing guidance for Open Science practices-to identify and classify the terminological obstacles encountered and reflect on their impact. Several types of terminological incongruities were identified, including fuzziness in language, disciplinary differences and multiple terms for a single meaning. A novel or technical term did not always exist in all domains, or if known, was not fully understood or adopted. Practical matters of international data collection and comparison included an unanticipated need to incorporate different types of data labels from country to country, authority to authority. Sometimes these incongruities could be solved quickly, sometimes they stopped the workflow. Active collaboration and mutual trust across the team enhanced workflows, as incompatibilities were resolved more speedily than otherwise. Based on the research experience described in this paper, we make six recommendations accompanied by suggestions for their implementation to improve the success of similar multinational, multilingual and multidisciplinary projects. These recommendations are conceptual drawing on a singular experience and remain to be sources for discussion and testing by others embarking on their research journey.

PMID:39637194 | DOI:10.1371/journal.pone.0311967

PLoS One. 2024 Dec 5;19(12):e0315127. doi: 10.1371/journal.pone.0315127. eCollection 2024.

ABSTRACT

Convolutional neural network (CNN)-based deep learning (DL) methods have transformed the analysis of geospatial, Earth observation, and geophysical data due to their ability to model spatial context information at multiple scales. Such methods are especially applicable to pixel-level classification or semantic segmentation tasks. A variety of R packages have been developed for processing and analyzing geospatial data. However, there are currently no packages available for implementing geospatial DL in the R language and data science environment. This paper introduces the geodl R package, which supports pixel-level classification applied to a wide range of geospatial or Earth science data that can be represented as multidimensional arrays where each channel or band holds a predictor variable. geodl is built on the torch package, which supports the implementation of DL using the R and C++ languages without the need for installing a Python/PyTorch environment. This greatly simplifies the software environment needed to implement DL in R. Using geodl, geospatial raster-based data with varying numbers of bands, spatial resolutions, and coordinate reference systems are read and processed using the terra package, which makes use of C++ and allows for processing raster grids that are too large to fit into memory. Training loops are implemented with the luz package. The geodl package provides utility functions for creating raster masks or labels from vector-based geospatial data and image chips and associated masks from larger files and extents. It also defines a torch dataset subclass for geospatial data for use with torch dataloaders. UNet-based models are provided with a variety of optional ancillary modules or modifications. Common assessment metrics (i.e., overall accuracy, class-level recalls or producer's accuracies, class-level precisions or user's accuracies, and class-level F1-scores) are implemented along with a modified version of the unified focal loss framework, which allows for defining a variety of loss metrics using one consistent implementation and set of hyperparameters. Users can assess models using standard geospatial and remote sensing metrics and methods and use trained models to predict to large spatial extents. This paper introduces the geodl workflow, design philosophy, and goals for future development.

PMID:39637071 | DOI:10.1371/journal.pone.0315127

PLoS One. 2024 Dec 5;19(12):e0315031. doi: 10.1371/journal.pone.0315031. eCollection 2024.

ABSTRACT

Tomato cultivation is expanding rapidly, but the tomato sector faces significant challenges from various sources, including environmental (abiotic stress) and biological (biotic stress or disease) threats, which adversely impact the crop's growth, reproduction, and overall yield potential. The objective of this work is to build deep learning based lightweight convolutional neural network (CNN) architecture for the real-time classification of biotic stress in tomato plant leaves. This model proposes to address the drawbacks of conventional CNNs, which are resource-intensive and time-consuming, by using optimization methods that reduce processing complexity and enhance classification accuracy. Traditional plant disease classification methods predominantly utilize CNN based deep learning techniques, originally developed for fundamental image classification tasks. It relies on computationally intensive CNNs, hindering real-time application due to long training times. To address this, a lighter CNN framework is proposed to enhance with two key components. Firstly, an Elephant Herding Optimization (EHO) algorithm selects pertinent features for classification tasks. The classification module integrates a Hessian-based Optimal Brain Surgeon (HOBS) approach with a pruned Extreme Learning Machine (ELM), optimizing network parameters while reducing computational complexity. The proposed pruned model gives an accuracy of 95.73%, Cohen's kappa of 0.81%, training time of 2.35sec on Plant Village dataset, comprising 8,000 leaf images across 10 distinct classes of tomato plant, which demonstrates that this framework effectively reduces the model's size of 9.2Mb and parameters by reducing irrelevant connections in the classification layer. The proposed classifier performance was compared to existing deep learning models, the experimental results show that the pruned DenseNet achieves an accuracy of 86.64% with a model size of 10.6 MB, while GhostNet reaches an accuracy of 92.15% at 10.9 MB. CACPNET demonstrates an accuracy of 92.4% with a model size of 18.0 MB. In contrast, the proposed approach significantly outperforms these models in terms of accuracy and processing time.

PMID:39637070 | DOI:10.1371/journal.pone.0315031

PLoS One. 2024 Dec 5;19(12):e0314533. doi: 10.1371/journal.pone.0314533. eCollection 2024.

ABSTRACT

Forensic entomology can help estimate the postmortem interval in criminal investigations. In particular, forensically important fly species that can be found on a body and in its environment at various times after death provide valuable information. However, the current method for identifying fly species is labor intensive, expensive, and may become more serious in view of a shortage of specialists. In this paper, we propose the use of computer vision and deep learning to classify adult flies according to three different families, Calliphoridae, Sarcophagidae, Rhiniidae, and their corresponding genera Chrysomya, Lucilia, Sarcophaga, Rhiniinae, and Stomorhina, which can lead to efficient and accurate estimation of time of death, for example, with the use of camera-equipped drones. The development of such a deep learning model for adult flies may be particularly useful in crisis situations, such as natural disasters and wars, when people disappear. In these cases drones can be used for searching large areas. In this study, two models were evaluated using transfer learning with MobileNetV3-Large and VGG19. Both models achieved a very high accuracy of 99.39% and 99.79%. In terms of inference time, the MobileNetV3-Large model was faster with an average time per step of 1.036 seconds than the VGG19 model, which took 2.066 seconds per step. Overall, the results highlight the potential of deep learning models for the classification of fly species in forensic entomology and search and rescue operations.

PMID:39637032 | DOI:10.1371/journal.pone.0314533

Health Data Sci. 2024 Dec 4;4:0176. doi: 10.34133/hds.0176. eCollection 2024.

ABSTRACT

Background: Missing data in electronic health records (EHRs) presents significant challenges in medical studies. Many methods have been proposed, but uncertainty exists regarding the current state of missing data addressing methods applied for EHR and which strategy performs better within specific contexts. Methods: All studies referencing EHR and missing data methods published from their inception until 2024 March 30 were searched via the MEDLINE, EMBASE, and Digital Bibliography and Library Project databases. The characteristics of the included studies were extracted. We also compared the performance of various methods under different missingness scenarios. Results: After screening, 46 studies published between 2010 and 2024 were included. Three missingness mechanisms were simulated when evaluating the missing data methods: missing completely at random (29/46), missing at random (20/46), and missing not at random (21/46). Multiple imputation by chained equations (MICE) was the most popular statistical method, whereas generative adversarial network-based methods and the k nearest neighbor (KNN) classification were the common deep-learning-based or traditional machine-learning-based methods, respectively. Among the 26 articles comparing the performance among medical statistical and machine learning approaches, traditional machine learning or deep learning methods generally outperformed statistical methods. Med.KNN and context-aware time-series imputation performed better for longitudinal datasets, whereas probabilistic principal component analysis and MICE-based methods were optimal for cross-sectional datasets. Conclusions: Machine learning methods show significant promise for addressing missing data in EHRs. However, no single approach provides a universally generalizable solution. Standardized benchmarking analyses are essential to evaluate these methods across different missingness scenarios.

PMID:39635227 | PMC:PMC11615160 | DOI:10.34133/hds.0176

Nanophotonics. 2023 Oct 2;12(20):3883-3894. doi: 10.1515/nanoph-2023-0298. eCollection 2023 Oct.

ABSTRACT

State-of-the-art deep learning models can converse and interact with humans by understanding their emotions, but the exponential increase in model parameters has triggered an unprecedented demand for fast and low-power computing. Here, we propose a microcomb-enabled integrated optical neural network (MIONN) to perform the intelligent task of human emotion recognition at the speed of light and with low power consumption. Large-scale tensor data can be independently encoded in dozens of frequency channels generated by the on-chip microcomb and computed in parallel when flowing through the microring weight bank. To validate the proposed MIONN, we fabricated proof-of-concept chips and a prototype photonic-electronic artificial intelligence (AI) computing engine with a potential throughput up to 51.2 TOPS (tera-operations per second). We developed automatic feedback control procedures to ensure the stability and 8 bits weighting precision of the MIONN. The MIONN has successfully recognized six basic human emotions, and achieved 78.5 % accuracy on the blind test set. The proposed MIONN provides a high-speed and energy-efficient neuromorphic computing hardware for deep learning models with emotional interaction capabilities.

PMID:39635194 | PMC:PMC11501890 | DOI:10.1515/nanoph-2023-0298

Nanophotonics. 2024 Aug 29;13(22):4159-4168. doi: 10.1515/nanoph-2024-0328. eCollection 2024 Sep.

ABSTRACT

Spectroscopy is a technique that analyzes the interaction between matter and light as a function of wavelength. It is the most convenient method for obtaining qualitative and quantitative information about an unknown sample with reasonable accuracy. However, traditional spectroscopy is reliant on bulky and expensive spectrometers, while emerging applications of portable, low-cost and lightweight sensing and imaging necessitate the development of miniaturized spectrometers. In this study, we have developed a computational spectroscopy method that can provide single-shot operation, sub-nanometer spectral resolution, and direct materials characterization. This method is enabled by a metasurface integrated computational spectrometer and deep learning algorithms. The identification of critical parameters of optical cavities and chemical solutions is demonstrated through the application of the method, with an average spectral reconstruction accuracy of 0.4 nm and an actual measurement error of 0.32 nm. The mean square errors for the characterization of cavity length and solution concentration are 0.53 % and 1.21 %, respectively. Consequently, computational spectroscopy can achieve the same level of spectral accuracy as traditional spectroscopy while providing convenient, rapid material characterization in a variety of scenarios.

PMID:39635447 | PMC:PMC11501049 | DOI:10.1515/nanoph-2024-0328

Nanophotonics. 2021 Oct 14;11(4):779-786. doi: 10.1515/nanoph-2021-0489. eCollection 2022 Jan.

ABSTRACT

Structured light with customized topological patterns inspires diverse classical and quantum investigations underpinned by accurate detection techniques. However, the current detection schemes are limited to vortex beams with a simple phase singularity. The precise recognition of general structured light with multiple singularities remains elusive. Here, we report deep learning (DL) framework that can unveil multi-singularity phase structures in an end-to-end manner, after feeding only two intensity patterns upon beam propagation. By outputting the phase directly, rich and intuitive information of twisted photons is unleashed. The DL toolbox can also acquire phases of Laguerre-Gaussian (LG) modes with a single singularity and other general phase objects likewise. Enabled by this DL platform, a phase-based optical secret sharing (OSS) protocol is proposed, which is based on a more general class of multi-singularity modes than conventional LG beams. The OSS protocol features strong security, wealthy state space, and convenient intensity-based measurements. This study opens new avenues for large-capacity communications, laser mode analysis, microscopy, Bose-Einstein condensates characterization, etc.

PMID:39635381 | PMC:PMC11501744 | DOI:10.1515/nanoph-2021-0489

Nanophotonics. 2023 Aug 11;12(18):3589-3601. doi: 10.1515/nanoph-2023-0291. eCollection 2023 Sep.

ABSTRACT

The selective broadband absorption of solar radiation plays a crucial role in applying solar energy. However, despite being a decade-old technology, the rapid and precise designs of selective absorbers spanning from the solar spectrum to the infrared region remain a significant challenge. This work develops a high-performance design paradigm that combines deep learning and multi-objective double annealing algorithms to optimize multilayer nanostructures for maximizing solar spectral absorption and minimum infrared radiation. Based on deep learning design, we experimentally fabricate the designed absorber and demonstrate its photothermal effect under sunlight. The absorber exhibits exceptional absorption in the solar spectrum (calculated/measured = 0.98/0.94) and low average emissivity in the infrared region (calculated/measured = 0.08/0.19). This absorber has the potential to result in annual energy savings of up to 1743 kW h/m2 in areas with abundant solar radiation resources. Our study opens a powerful design method to study solar-thermal energy harvesting and manipulation, which will facilitate for their broad applications in other engineering applications.

PMID:39635349 | PMC:PMC11502052 | DOI:10.1515/nanoph-2023-0291

Nanophotonics. 2023 Apr 13;12(11):1943-1955. doi: 10.1515/nanoph-2022-0746. eCollection 2023 May.

ABSTRACT

Recent advances in inverse-design approaches for discovering optical structures based on desired functional characteristics have reshaped the landscape of nanophotonic structures, where most studies have focused on how light interacts with nanophotonic structures only. When quantum emitters (QEs), such as atoms, molecules, and quantum dots, are introduced to couple to the nanophotonic structures, the light-matter interactions become much more complicated, forming a rapidly developing field - quantum nanophotonics. Typical quantum functional characteristics depend on the intrinsic properties of the QE and its electromagnetic environment created by the nanophotonic structures, commonly represented by a scalar quantity, local-density-of-states (LDOS). In this work, we introduce a generalized inverse-design framework in quantum nanophotonics by taking LDOS as the bridge to connect the nanophotonic structures and the quantum functional characteristics. We take a simple system consisting of QEs sitting on a single multilayer shell-metal-nanoparticle (SMNP) as an example, apply fully-connected neural networks to model the LDOS of SMNP, inversely design and optimize the geometry of the SMNP based on LDOS, and realize desirable quantum characteristics in two quantum nanophotonic problems: spontaneous emission and entanglement. Our work introduces deep learning to the quantum optics domain for advancing quantum device designs; and provides a new platform for practicing deep learning to design nanophotonic structures for complex problems without a direct link between structures and functional characteristics.

PMID:39635698 | PMC:PMC11501149 | DOI:10.1515/nanoph-2022-0746

Nanophotonics. 2021 Dec 22;11(11):2483-2505. doi: 10.1515/nanoph-2021-0660. eCollection 2022 Jun.

ABSTRACT

Nanophotonics inverse design is a rapidly expanding research field whose goal is to focus users on defining complex, high-level optical functionalities while leveraging machines to search for the required material and geometry configurations in sub-wavelength structures. The journey of inverse design begins with traditional optimization tools such as topology optimization and heuristics methods, including simulated annealing, swarm optimization, and genetic algorithms. Recently, the blossoming of deep learning in various areas of data-driven science and engineering has begun to permeate nanophotonics inverse design intensely. This review discusses state-of-the-art optimizations methods, deep learning, and more recent hybrid techniques, analyzing the advantages, challenges, and perspectives of inverse design both as a science and an engineering.

PMID:39635678 | PMC:PMC11502023 | DOI:10.1515/nanoph-2021-0660

Nanophotonics. 2022 Jan 24;11(11):2507-2529. doi: 10.1515/nanoph-2021-0636. eCollection 2022 Jun.

ABSTRACT

A new type of spectrometer that heavily relies on computational technique to recover spectral information is introduced. They are different from conventional optical spectrometers in many important aspects. Traditional spectrometers offer high spectral resolution and wide spectral range, but they are so bulky and expensive as to be difficult to deploy broadly in the field. Emerging applications in machine sensing and imaging require low-cost miniaturized spectrometers that are specifically designed for certain applications. Computational spectrometers are well suited for these applications. They are generally low in cost and offer single-shot operation, with adequate spectral and spatial resolution. The new type of spectrometer combines recent progress in nanophotonics, advanced signal processing and machine learning. Here we review the recent progress in computational spectrometers, identify key challenges, and note new directions likely to develop in the near future.

PMID:39635673 | PMC:PMC11502016 | DOI:10.1515/nanoph-2021-0636

Front Neuroinform. 2024 Nov 19;18:1454583. doi: 10.3389/fninf.2024.1454583. eCollection 2024.

ABSTRACT

Elderly and individuals with disabilities can greatly benefit from human activity recognition (HAR) systems, which have recently advanced significantly due to the integration of the Internet of Things (IoT) and artificial intelligence (AI). The blending of IoT and AI methodologies into HAR systems has the potential to enable these populations to lead more autonomous and comfortable lives. HAR systems are equipped with various sensors, including motion capture sensors, microcontrollers, and transceivers, which supply data to assorted AI and machine learning (ML) algorithms for subsequent analyses. Despite the substantial advantages of this integration, current frameworks encounter significant challenges related to computational overhead, which arises from the complexity of AI and ML algorithms. This article introduces a novel ensemble of gated recurrent networks (GRN) and deep extreme feedforward neural networks (DEFNN), with hyperparameters optimized through the artificial water drop optimization (AWDO) algorithm. This framework leverages GRN for effective feature extraction, subsequently utilized by DEFNN for accurately classifying HAR data. Additionally, AWDO is employed within DEFNN to adjust hyperparameters, thereby mitigating computational overhead and enhancing detection efficiency. Extensive experiments were conducted to verify the proposed methodology using real-time datasets gathered from IoT testbeds, which employ NodeMCU units interfaced with Wi-Fi transceivers. The framework's efficiency was assessed using several metrics: accuracy at 99.5%, precision at 98%, recall at 97%, specificity at 98%, and F1-score of 98.2%. These results then were benchmarked against other contemporary deep learning (DL)-based HAR systems. The experimental outcomes indicate that our model achieves near-perfect accuracy, surpassing alternative learning-based HAR systems. Moreover, our model demonstrates reduced computational demands compared to preceding algorithms, suggesting that the proposed framework may offer superior efficacy and compatibility for deployment in HAR systems designed for elderly or individuals with disabilities.

PMID:39635647 | PMC:PMC11615478 | DOI:10.3389/fninf.2024.1454583

Nucl Med Mol Imaging. 2024 Dec;58(7):431-448. doi: 10.1007/s13139-024-00850-9. Epub 2024 Feb 14.

ABSTRACT

Recent technical innovation enables faster and more reliable cardiac magnetic resonance (CMR) imaging than before. Artificial intelligence is used in improving image resolution, fast scanning, and automated analysis of CMR. Fast CMR techniques such as compressed sensing technique enable fast cine, perfusion, and late gadolinium-enhanced imaging and improve patient throughput and widening CMR indications. CMR feature-tracking technique gives insight on diastolic function parameters of ventricles and atria with prognostic implications. Myocardial parametric mapping became to be included in the routine CMR protocol. CMR fingerprinting enables simultaneous quantification of myocardial T1 and T2. These parameters may give information on myocardial alteration in the preclinical stages in various myocardial diseases. Four-dimensional flow imaging shows hemodynamic characteristics in or through the cardiovascular structures visually and gives quantitative values of vortex, kinetic energy, and wall-shear stress. In conclusion, CMR is an essential modality in the diagnosis of various cardiovascular diseases, especially myocardial diseases. Recent progress in CMR techniques promotes more widespread use of CMR in clinical practice. This review summarizes recent updates in CMR technologies and clinical research.

PMID:39635630 | PMC:PMC11612075 | DOI:10.1007/s13139-024-00850-9

Front Med (Lausanne). 2024 Nov 20;11:1489139. doi: 10.3389/fmed.2024.1489139. eCollection 2024.

ABSTRACT

BACKGROUND: The application of Artificial Intelligence (AI) in diagnosing retinal diseases represents a significant advancement in ophthalmological research, with the potential to reshape future practices in the field. This study explores the extensive applications and emerging research frontiers of AI in retinal diseases.

OBJECTIVE: This study aims to uncover the developments and predict future directions of AI research in retinal disease over the past decade.

METHODS: This study analyzes AI utilization in retinal disease research through articles, using citation data sourced from the Web of Science (WOS) Core Collection database, covering the period from January 1, 2014, to December 31, 2023. A combination of WOS analyzer, CiteSpace 6.2 R4, and VOSviewer 1.6.19 was used for a bibliometric analysis focusing on citation frequency, collaborations, and keyword trends from an expert perspective.

RESULTS: A total of 2,861 articles across 93 countries or regions were cataloged, with notable growth in article numbers since 2017. China leads with 926 articles, constituting 32% of the total. The United States has the highest h-index at 66, while England has the most significant network centrality at 0.24. Notably, the University of London is the leading institution with 99 articles and shares the highest h-index (25) with University College London. The National University of Singapore stands out for its central role with a score of 0.16. Research primarily spans ophthalmology and computer science, with "network," "transfer learning," and "convolutional neural networks" being prominent burst keywords from 2021 to 2023.

CONCLUSION: China leads globally in article counts, while the United States has a significant research impact. The University of London and University College London have made significant contributions to the literature. Diabetic retinopathy is the retinal disease with the highest volume of research. AI applications have focused on developing algorithms for diagnosing retinal diseases and investigating abnormal physiological features of the eye. Future research should pivot toward more advanced diagnostic systems for ophthalmic diseases.

PMID:39635592 | PMC:PMC11614663 | DOI:10.3389/fmed.2024.1489139

Nanophotonics. 2022 Jun 14;11(14):3189-3214. doi: 10.1515/nanoph-2022-0197. eCollection 2022 Jul.

ABSTRACT

The deep-learning revolution is providing enticing new opportunities to manipulate and harness light at all scales. By building models of light-matter interactions from large experimental or simulated datasets, deep learning has already improved the design of nanophotonic devices and the acquisition and analysis of experimental data, even in situations where the underlying theory is not sufficiently established or too complex to be of practical use. Beyond these early success stories, deep learning also poses several challenges. Most importantly, deep learning works as a black box, making it difficult to understand and interpret its results and reliability, especially when training on incomplete datasets or dealing with data generated by adversarial approaches. Here, after an overview of how deep learning is currently employed in photonics, we discuss the emerging opportunities and challenges, shining light on how deep learning advances photonics.

PMID:39635557 | PMC:PMC11501725 | DOI:10.1515/nanoph-2022-0197

Nanophotonics. 2022 Sep 6;11(20):4465-4478. doi: 10.1515/nanoph-2022-0310. eCollection 2022 Sep.

ABSTRACT

A data enhanced iterative few-sample (DEIFS) algorithm is proposed to achieve the accurate and efficient inverse design of multi-shaped 2D chiral metamaterials. Specifically, three categories of 2D diffractive chiral structures with different geometrical parameters, including widths, separation spaces, bridge lengths, and gold lengths are studied utilising both the conventional rigorous coupled wave analysis (RCWA) approach and DEIFS algorithm, with the former approach assisting the training process for the latter. The DEIFS algorithm can be divided into two main stages, namely data enhancement and iterations. Firstly, some "pseudo data" are generated by a forward prediction network that can efficiently predict the circular dichroism (CD) response of 2D diffractive chiral metamaterials to reinforce the dataset after necessary denoising. Then, the algorithm uses the CD spectra and the predictions of parameters with smaller errors iteratively to achieve accurate values of the remaining parameters. Meanwhile, according to the impact of geometric parameters on the chiroptical response, a new functionality is added to interpret the experimental results of DEIFS algorithm from the perspective of data, improving the interpretability of the DEIFS. In this way, the DEIFS algorithm replaces the time-consuming iterative optimization process with a faster and simpler approach that achieves accurate inverse design with dataset whose amount is at least one to two orders of magnitude less than most previous deep learning methods, reducing the dependence on simulated spectra. Furthermore, the fast inverse design of multiple shaped metamaterials allows for different light manipulation, demonstrating excellent potentials in applications of optical coding and information processing. This work belongs to one of the first attempts to thoroughly characterize the flexibility, interpretability, and generalization ability of DEIFS algorithm in studying various chiroptical effects in metamaterials and accelerating the inverse design of hypersensitive photonic devices.

PMID:39635508 | PMC:PMC11501232 | DOI:10.1515/nanoph-2022-0310

Nanophotonics. 2023 Jan 18;12(14):2807-2812. doi: 10.1515/nanoph-2022-0612. eCollection 2023 Jul.

ABSTRACT

Particle counting is of critical importance for nanotechnology, environmental monitoring, pharmaceutical, food and semiconductor industries. Here we introduce a super-resolution single-shot optical method for counting and mapping positions of subwavelength particles on a surface. The method is based on the deep learning analysis of the intensity profile of the coherent light scattered on the group of particles. In a proof of principle experiment, we demonstrated particle counting accuracies of more than 90%. We also demonstrate that the particle locations can be mapped on a 4 × 4 grid with a nearly perfect accuracy (16-pixel binary imaging of the particle ensemble). Both the retrieval of number of particles and their mapping is achieved with super-resolution: accuracies are similar for sets with closely located optically unresolvable particles and sets with sparsely located particles. As the method does not require fluorescent labelling of the particles, is resilient to small variations of particle sizes, can be adopted to counting various types of nanoparticulates and high rates, it can find applications in numerous particles counting tasks in nanotechnology, life sciences and beyond.

PMID:39635469 | PMC:PMC11501414 | DOI:10.1515/nanoph-2022-0612