Novel opportunities for clinical pharmacy research: development of a machine learning model to identify medication related causes of delirium in different patient groups.

The advent of artificial intelligence (AI) technologies has taken the world of science by storm in 2023. The opportunities of this easy to access technology for clinical pharmacy research are yet to be fully understood. The development of a custom-made large language model (LLM) (DELSTAR) trained on a wide range of internationally recognised scientific publication databases, pharmacovigilance sites and international product characteristics to help identify and summarise medication related information on delirium, as a proof-of-concept model, identified new facilitators and barriers for robust clinical pharmacy practice research. This technology holds great promise for the development of much more comprehensive prescribing guidelines, practice support applications for clinical pharmacy, increased patient and prescribing safety and resultant implications for healthcare costs. The challenge will be to ensure its methodologically robust use and the detailed and transparent verification of its information accuracy.

Object recognition using sparse, reduced-dimension point cloud data.

It has been shown that point separations as a feature derived from point clouds can be used to discriminate between two objects of similar class. Here we show that the same feature derived from sparse point clouds can maintain significant discrimination capability. Using the point-separation feature, templates created from random realizations of a point cloud are developed for several vehicles. The templates are then used in two-class discrimination tests. The point-separation feature is shown to produce reliable discrimination using a log-likelihood ratio between two objects.

Applications of Lasers for Sensing and Free Space Communications: introduction to the feature issue.

This feature issue highlights the latest developments in laser-based sensing and free space communications. In total, 15 papers were published in Applied Optics, including an invited review paper that celebrates the legacy of David L. Fried.

Forecasting atmospheric turbulence conditions from prior environmental parameters using artificial neural networks.

Atmospheric turbulence (C n2) modeling has been proposed by physics-based models, but they are unable to capture the many cases. Recently, machine learning surrogate models have been used to learn the relationship between local meteorological conditions and turbulence strength. These models predict C n2 at time t from weather at time t. This work expands modeling capabilities by proposing a technique to forecast 3 h of future turbulence conditions at 30 min intervals from prior environmental parameters using artificial neural networks. First, local weather and turbulence measurements are formatted to pairs of the input sequence and output forecast. Next, a grid search is used to find the best combination of model architecture, input variables, and training parameters. The architectures investigated are the multilayer perceptron and three variants of the recurrent neural network (RNN): the simple RNN, the long short-term memory RNN (LSTM-RNN), and the gated recurrent unit RNN (GRU-RNN). A GRU-RNN architecture that uses 12 h of prior inputs is found to have the best performance. Finally, this model is applied to the test dataset and analyzed. It is shown that the model has generally learned the relationship between prior environmental and future turbulence conditions.

Viewpoint-independent object recognition using reduced-dimension point cloud data.

Point cloud data offer the potential for viewpoint-independent object recognition based solely on the geometrical information about an object that they contain. We consider two types of one-dimensional data products extracted from point clouds: range histograms and point-separation histograms. We evaluate each histogram in terms of its viewpoint independence. The Jensen-Shannon divergence is used to show that point-separation histograms have the potential for viewpoint independence. We demonstrate viewpoint-independent recognition performance using lidar data sets from two vehicles and a simple algorithm for a two-class recognition problem. We find that point-separation histograms have good potential for viewpoint-independent recognition over a hemisphere.

Coherent sensing performance comparison of framed and asynchronous GMAPD arrays.

Single photon counting Geiger mode avalanche photodiode (GMAPD) arrays are typically used for high-resolution 3D ranging. These high-gain, high-bandwidth detectors are also feasible for coherent sensing. GMAPD arrays have two different readout architectures: asynchronous and synchronous (or framed). The individual pixels in asynchronous GMAPD arrays operate independently, reducing the loss due to blocking during the reset time. In contrast, framed GMAPD arrays are prone to saturation as they reset the entire array of pixels simultaneously. This study presents a performance comparison of asynchronous and framed GMAPD arrays for coherent sensing as a function of common system parameters. Expressions for the arm probability and blocking loss are defined to contrast the mechanisms of missed detection opportunities for both types of GMAPD detectors.

Attribution bias underlying burns-induced anxiety symptoms.

INTRODUCTION: Burn injuries are a debilitating cause of morbidity and mortality associated with the long-term impact of psychological factors on quality of life. Accurate assessment of the differential impact of burn sequelae and anxiety is often complicated by the overlap between psychological and somatic symptoms in burns patients. The Beck Anxiety Inventory (BAI) is one validated psychometric tool for anxiety assessment. The primary objective of this study is to investigate whether utilising the BAI as a tool to assess for anxiety in burns patients is biased due to the confounding of symptoms of anxiety with the physical sequelae of a burn injury. METHODS: This is a single-centre, prospective, cross-sectional study. The study was conducted in accordance with the UK Good Clinical Practice guidelines (CAPP reference number 506). Patients were recruited over a three-month period from November 2016 to February 2017 and were offered a modified BAI questionnaire to complete. Patients were asked to indicate to what degree they attributed each symptom to their physical injury or their psychological state on a visual analogue scale (VAS). RESULTS: 50 patients, comprising 33 females (66%) and 17 males (34%), participated in the study with a median age of 33.5 years (range: 20-88). Date of injury spanned May 1991 to January 2017. Percentage of the total body surface area (% TBSA) affected by burn ranged from 1 to 86%. Patients attributed eight of the 21 self-report items within the BAI as being more physical than psychological in origin. The results reveal a statistical significant difference in patient VAS scores between physical (mean: 34.16, 95% CI: 29.04-39.28) and psychological (mean: 61.2, 95% CI: 56.33-66.17) BAI items, with p<0.0001. In addition, patients with a facial burn injury were more likely to report 'face flushed' (Mann-Whitney U Test, Z=-2.11, p<0.05) and patients with a hand burn injury were more likely to report 'hands trembling' (Mann-Whitney U Test, Z=-2.52, p<0.05). CONCLUSIONS: This feasibility study found preliminary evidence suggesting that the BAI may, in part, represent misattributed symptoms of cutaneous injury from burns. However, whilst our findings suggest an attribution bias, there is not enough evidence from this data to comment on whether its use should be restricted in burns patients. Further research is needed to formally quantify convergent and divergent validity through structured interviews. In addition, further research using other self-report tools of anxiety in burns patients would be useful to corroborate the prospect of biased and confounded anxiety scores.

Perioperative Research into Memory (PRiMe): Cognitive impairment following a severe burn injury and critical care admission, part 1.

INTRODUCTION: An investigation into long-term cognitive impairment and Quality of Life (QoL) after severe burns. METHODS: A proof of principle, cohort design, prospective, observational clinical study. Patients with severe burns (>15% TBSA) admitted to Burns ICU for invasive ventilation were recruited for psychocognitive assessment with a convenience sample of age and sex-matched controls. Participants completed psychological and QoL questionnaires, the Cogstate® electronic battery, Hopkins Verbal Learning, Verbal Fluency and Trail making tasks. RESULTS: 15 patients (11M, 4F; 41±14 years; TBSA 38.4%±18.5) and comparators (11M, 4F; 40±13 years) were recruited. Burns patients reported worse QoL (Neuro-QoL Short Form v2, patient 30.1±8.2, control 38.7±3.2, p=0.0004) and cognitive function (patient composite z-score 0.01, IQR -0.11 to 0.33, control 0.13, IQR 0.47-0.73, p=0.02). Compared to estimated premorbid FSIQ, patients dropped an equivalent of 8 IQ points (p=0.002). Cognitive function negatively correlated with burn severity (rBaux score, p=0.04). QoL strongly correlated with depressive symptoms (Rho=-0.67, p=0.009) but not cognitive function. CONCLUSIONS: Severe burns injuries are associated with a significant, global, cognitive deficit. Patients also report worse QoL, depression and post-traumatic stress. Perceived QoL from cognitive impairment was more closely associated with depression than cognitive impairment.

High resolution non-iterative aperture synthesis.

The maximum resolution of a multiple-input multiple-output (MIMO) imaging system is determined by the size of the synthetic aperture. The synthetic aperture is determined by a coordinate shift using the relative positions of the illuminators and receive apertures. Previous methods have shown non-iterative phasing for multiple illuminators with a single receive aperture for intra-aperture synthesis. This work shows non-iterative phasing with both multiple illuminators and multiple receive apertures for inter-aperture synthesis. Simulated results show that piston, tip, and tilt can be calculated using inter-aperture phasing after intra-aperture phasing has been performed. Use of a fourth illuminator for increased resolution is shown. The modulation transfer function (MTF) is used to quantitatively judge increased resolution.

Camera phasing in multi-aperture coherent imaging.

The resolution of a diffraction-limited imaging system is inversely proportional to the aperture size. Instead of using a single large aperture, multiple small apertures are used to synthesize a large aperture. Such a multi-aperture system is modular, typically more reliable and less costly. On the other hand, a multi-aperture system requires phasing sub-apertures to within a fraction of a wavelength. So far in the literature, only the piston, tip, and tilt type of inter-aperture errors have been addressed. In this paper, we present an approach to correct for rotational and translational errors as well.

Three dimensional object recognition with photon counting imagery in the presence of noise.

Three dimensional (3D) imaging systems have been recently suggested for passive sensing and recognition of objects in photon-starved environments where only a few photons are emitted or reflected from the object. In this paradigm, it is important to make optimal use of limited information carried by photons. We present a statistical framework for 3D passive object recognition in presence of noise. Since in quantum-limited regime, detector dark noise is present, our approach takes into account the effect of noise on information bearing photons. The model is tested when background noise and dark noise sources are present for identifying a target in a 3D scene. It is shown that reliable object recognition is possible in photon-counting domain. The results suggest that with proper translation of physical characteristics of the imaging system into the information processing algorithms, photon-counting imagery can be used for object classification.

Three dimensional imaging with randomly distributed sensors.

As a promising three dimensional passive imaging modality, Integral Imaging (II) has been investigated widely within the research community. In virtually all of such investigations, there is an implicit assumption that the collection of elemental images lie on a simple geometric surface (e.g. flat, concave, etc), also known as pickup surface. In this paper, we present a generalized framework for 3D II with arbitrary pickup surface geometry and randomly distributed sensor configuration. In particular, we will study the case of Synthetic Aperture Integral Imaging (SAII) with random location of cameras in space, while all cameras have parallel optical axes but different distances from the 3D scene. We assume that the sensors are randomly distributed in 3D volume of pick up space. For 3D reconstruction, a finite number of sensors with known coordinates are randomly selected from within this volume. The mathematical framework for 3D scene reconstruction is developed based on an affine transform representation of imaging under geometrical optics regime. We demonstrate the feasibility of the methods proposed here by experimental results. To the best of our knowledge, this is the first report on 3D imaging using randomly distributed sensors.

Three dimensional visualization by photon counting computational Integral Imaging.

In this paper, we present three dimensional (3D) object reconstruction using photon-counted elemental images acquired by a passive 3D Integral Imaging (II) system. The maximum likelihood (ML) estimator is derived to reconstruct the irradiance of the 3D scene pixels and the reliability of the estimator is described by confidence intervals. For applications in photon scarce environments, our proposed technique provides 3D reconstruction for better visualization as well as significant reduction in the computational burden and required bandwidth for transmission of integral images. The performance of the reconstruction is illustrated qualitatively and compared quantitatively with Peak to Signal to Noise Ratio (PSNR) criterion.

Three-dimensional distortion-tolerant object recognition using photon-counting integral imaging.

This paper addresses three-dimensional distortion-tolerant object recognition using photon-counting integral imaging (II). A photon-counting linear discriminant analysis (LDA) is proposed for classification photonlimited images. In the photon-counting LDA, classical irradiance images are used to train the classifier. The unknown objects used to test the classifier are labeled by the number of photons detected. The optimal solution of the Fisher's LDA for photon-limited images is found to be different from the case when irradiance values are used. This difference results in one of the merits of a photon-counting LDA, namely that the high dimensionality of the image can be handled without preprocessing. Thus, the singularity problem of the Fisher's LDA encountered in the use of irradiance images can be avoided. By using photon-counting II, we build a compact distortiontolerant recognition system that makes use of the multiple-perspective imaging of II to enhance the recognition performance. Experimental and simulation results are presented to classify out-of-plane rotated objects. The performance is analyzed in terms of mean-squared distance (MSD) between the irradiance images. It is shown that a low level of photons is sufficient in the proposed technique.

Performance of 3D integral imaging with position uncertainty.

We present the theoretical and simulation results on the analysis of Synthetic Aperture Integral Imaging (SAII) technique and its sensitivity to pickup position uncertainty. SAII is a passive three dimensional imaging technique based on multiple image acquisitions with different perspective of the scene under incoherent or natural illumination. In practical SAII applications, there is always an uncertainty associated with the position at which each sensor captures the elemental image. We present a theoretical analysis that quantifies image degradation in terms of Mean Square Error (MSE) metric. Simulation results are also presented to identify the parameters affecting the reconstruction degradation and to confirm the analysis. We show that in SAII with a given uncertainty in the sensor locations, the high spatial frequency content of the 3D reconstructed images are most degraded. We also show an inverse relationship between the reconstruction distance and degradation metric. To the best of our knowledge, this is the first time that the effects of sensor position uncertainty on 3D computational reconstruction in synthetic aperture integral imaging systems have been quantitatively analyzed.

Photon-counting passive 3D image sensing for reconstruction and recognition of partially occluded objects.

In this paper, we discuss the reconstruction and the recognition of partially occluded objects using photon counting integral imaging (II). Irradiance scenes are numerically reconstructed for the reference target in three-dimensional (3D) space. Photon counting scenes are estimated for unknown input objects using maximum likelihood estimation (MLE) of Poisson parameter. We propose nonlinear matched filtering in 3D space to recognize partially occluded targets. The recognition performance is substantially improved from the nonlinear matched filtering of elemental images without 3D reconstruction. The discrimination capability is analyzed in terms of Fisher ratio (FR) and receiver operating characteristic (ROC) curves.

Photon counting passive 3D image sensing for automatic target recognition.

In this paper, we propose photon counting three-dimensional (3D) passive sensing and object recognition using integral imaging. The application of this approach to 3D automatic target recognition (ATR) is investigated using both linear and nonlinear matched filters. We find there is significant potential of the proposed system for 3D sensing and recognition with a low number of photons. The discrimination capability of the proposed system is quantified in terms of discrimination ratio, Fisher ratio, and receiver operating characteristic (ROC) curves. To the best of our knowledge, this is the first report on photon counting 3D passive sensing and ATR with integral imaging.

Enhanced signal coupling into periodically poled lithium niobate with microlens arrays.

The return signal frequency of an eye-safe ladar system is upconverted from the infrared to the visible through sum-frequency generation by incorporation of periodically poled LiNbO3 into the receiver. A quantitative analysis of the angular acceptance and the quantum efficiency is then presented for a single macroscopic receiver optic and a multiaperture microlens array. Comparing both results, a 6x increase in the receiver field of regard and an 18% increase in beam coupling were realized for the microlens design over the macroscopic system.

Space-bandwidth product enhancement of a monostatic, multiaperture infrared image upconversion ladar receiver incorporating periodically poled liNbO3.

We investigate the space-bandwidth product of a ladar system incorporating an upconversion receiver. After illuminating a target with an eye-safe beam, we direct the return into a piece of periodically poled LiNbO3 where it is upconverted into the visible spectrum and detected with a CCD camera. The theoretical and experimental transfer functions are then found. We show that the angular acceptance of the upconversion process severely limits the receiver field of regard for macroscopic coupling optics. This limitation is overcome with a pair of microlens arrays, and a 43% increase in the system's measured space-bandwidth product is demonstrated.