Biomedical Physics & Engineering Express

Developing an efficient and cost-effective wound-healing substance to treat wounds and regenerate skin is desperately needed in the current world. The present study evaluated in vivo wound healing and in vitro antioxidant, antibacterial, anti-inflammatory activities of propolis mediated silver nanoparticles. Extract of Bee propolis from northeast Punjab, Pakistan, has been prepared via maceration and subjected to chemical identification. The results revealed that it is rich in phenolic contents (88± 0.004 mg GAE/mL, 34 ± 0.1875 mg QE/ mL) hence, employed as a reducer and capping agent to afford silver nanoparticles (AgNPs) by green approach. The prepared nanoparticles have been characterized by UV-visible (UV-Vis), Fourier transform infrared spectroscopy (FTIR), Scanning electron microscopy (SEM), X-ray diffraction (XRD). The propolis mediated AgNPs possess cubic face center with spherical shape and measured 50-60 nm in size. Moreover, propolis mediated silver nanoparticles have been studied for various biological activities. The results showed excellent antioxidant (0.4696µg/mL), anti-inflammatory (0.3996 µg/mL) and antibacterial activities against Staphylococcus aureus (MIC 0.462 µg/mL) and Proteus mirabilis (MIC 0.659 µg/mL) bacterium. An ointment was prepared by mixing AgNPs with polymeric gels for burn wound treatment in rabbits. We found rapid wound healing and higher collagen deposition in AgNPs treated wounds than in control group. Our data suggest that AgNPs from propolis ameliorate excision wounds, and hence, these AgNPs could be potential therapeutic agents for the treatment of burns.

The prediction of epileptic seizures is a classical research problem, representing one of the most challenging tasks in the analysis of brain disorders. There is active research into digital twins (DT) for various healthcare applications, as they can transform research into customized and personalized healthcare. The widespread adoption of DT technology relies on ample patient data to ensure precise monitoring and decision-making, leveraging Machine Learning (ML) and Deep Learning (DL) algorithms. Given the non-stationarity of EEG recordings, characterized by substantial frequency variations over time, there is a notable preference for advanced time-frequency methods in seizure prediction. This research proposes a DT-based seizure prediction system by applying an advanced time-frequency analysis approach known as Time-Reassigned MultiSynchroSqueezing Transform (TMSST) to EEG data to extract patient-specific impulse features and subsequently, a Deep Learning strategy, CNN-BiLSTM-Attention mechanism model is utilized in learning and classifying features for seizure prediction. The proposed architecture is named as ‘Digital Twin-Net’. By estimating the group delay in the time direction, TMSST produces the frequency components that are responsible for the EEG signal's temporal behavior and those time-frequency signatures are learned by the developed CNN-BiLSTM-Attention mechanism model. Thus the combination acts as a digital twin of a patient for the prediction of epileptic seizures. The experimental results showed that the suggested approach achieved an accuracy of 99.70% when tested on 22 patients from the publicly accessible CHB-MIT dataset. The proposed method surpasses previous solutions in terms of overall performance. Consequently, the suggested method can be regarded as an efficient approach to EEG seizure prediction.

Introduction. Open reduction internal fixation (ORIF) and external fixation are traditional surgical techniques for treating type VI Schatzker tibial plateau fractures. A newly developed technique integrates the intramedullary tibial nail with condylar bolts. This finite element study investigated the mechanical response of three surgical techniques for fixing type VI Schatzker tibial plateau fractures. We compared the intramedullary nail-bolt (IMNB) technique with the single lateral locking plate (SLLP) and dual plating (DP) techniques. Materials and Methods. A 4th generation Sawbone model of a left tibia with a Type VI tibial plateau fracture was scanned using computed tomography and reconstructed into a 3D model. The plates were digitally reconstructed using 3D scanning technology, while the screws, condylar bolt, and nail were replicated using commercial computer-aided design software. An application engineer guided by a surgeon, virtually positioned the bone-implant construct for the three surgical techniques to align with physical constructs from a previous in-vitro biomechanical study. A commercial finite element analysis software was used for the computer simulation, with the tibial plateau subjected to uniaxial loads at 500, 1000, and 1500 Newton while the distal tip of the tibia remained fixed. Measurements of vertical subsidence, horizontal diastasis, and passive construct stiffness were recorded and compared to those of the previous in-vitro biomechanical experiment. Results. DP had the highest stiffness, followed by IMNB and SLLP techniques. DP also resulted in smaller values for measured subsidence and diastasis compared to SLLP and IMNB. The simulation results aligned with those of the in-vitro biomechanical study. Conclusions. The simulation results may further support the initial suggestion of the in-vitro biomechanical study that the IMNB technique is a biomechanically suitable method for fixing Type VI Schatzker injuries.

Despite new approaches in the treatment of cardiovascular disease (CVD) such as percutaneous coronary intervention, coronary artery bypass graft, and left ventricular assist devices, which cannot fully compensate for the effectiveness of the original heart, heart transplantation still remains as the most effective solution. A growing body of literature recognizes the importance of developing a whole heart constructed from living tissues to provide an alternative option for patients suffering from diseases of the cardiovascular system. A potential solution that shows a promise is to generate cell-free, i.e., decellularized, scaffolds using native heart tissue to be later cellularized and transplanted. This study reports the decellularization process and efficiency in an effort to create a whole heart scaffold. The hearts harvested from rabbits were perfused and the final bioartificial scaffolds were characterized for the efficiency of decellularization in terms of DNA content, collagen, and glycosaminoglycan. The compressive biomechanical properties of decellularized and native hearts were also determined and compared. Findings revealed that the DNA content of decellularized hearts was significantly reduced while keeping collagen and GAG content unchanged. Biomechanical properties of the hearth became inferior upon removal of the nuclear material. Decellularized hearts have significant importance in treating CVD as they serve as bioartificial hearts, providing a more clinically relevant model for potential human use. Future work will focus on the recellularization of the heart using induced pluripotent or embryonic stem cells to test its functionality.

In this paper, we propose the design of extending collimators aimed at reducing the radiation dose received by patients with normal tissues and protecting organs at risk in Boron Neutron Capture Therapy (BNCT). Three types of extended collimators are studied: Type 1, which is a traditional design; Type 2, which is built upon Type 1 by incorporating additional polyethylene material containing lithium fluoride (PE(LiF)); Type 3, which adds lead (Pb) to Type 1. We evaluated the dose distribution characteristics of the above-extended collimators using Monte Carlo methods simulations under different configurations: in air, in a homogeneous phantom, and a humanoid phantom model. Firstly, the neutron and gamma-ray fluxes at the collimator outlet of the three designs showed no significant changes, thus it can be expected that their therapeutic effects on tumors will be similar. Then, the dose distribution outside the irradiation field was studied. The results showed that, compared with Type 1, Type 2 has a maximum reduction of 57.14% in neutron leakage dose, and Type 3 has a maximum reduction of 21.88% in gamma-ray leakage dose. This will help to reduce the radiation dose to the local skin. Finally, the doses of different organs were simulated. The results showed that the neutron dose of Type 2 was relatively low, especially for the skin, thyroid, spinal cord, and left lung, with the neutron dose reduced by approximately 20.34%, 16.18%, 26.05%, and 18.91% respectively compared to Type 1. Type 3 collimator benefits in reducing gamma-ray dose for the thyroid, esophagus, and right lung organs, with gamma-ray dose reductions of around 10.81%, 9.45%, and 10.42% respectively. This indicates that attaching PE(LiF) or Pb materials to a standard collimator can suppress the dose distribution of patient organs, which can provide valuable insights for the design of extended collimators in BNCT.

Conventional deep brain stimulation (DBS) for movement disorders is a well-established clinical treatment. Over the last few decades, over 200,000 people have been treated by DBS worldwide for several neurological conditions, including Parkinson’s disease and Essential Tremor. DBS involves implanting electrodes into disorder-specific targets in the brain and applying an electric current. Although the hardware has developed in recent years, the clinically used stimulation pattern has remained as a regular frequency square pulse. Recent studies have suggested that phase-locking, coordinated reset or irregular patterns may be as or more effective at desynchronising the pathological neural activity. Such studies have shown efficacy using detailed neuron models or highly simplified networks and considered one frequency band. We previously described a population level model which generates oscillatory activity in both the beta band (20 Hz) and the tremor band (4 Hz). Here we use this model to look at the impact of applying regular, irregular and phase dependent bursts of stimulation, and show how this influences both tremor- and beta-band activity. We found that bursts are as or more effective at suppressing the pathological oscillations compared to continuous DBS. Importantly however, at higher amplitudes we found that the stimulus drove the network activity, as seen previously. Strikingly, this suppression was most apparent for the tremor band oscillations, with beta band pathological activity being more resistant to the burst stimulation compared to continuous, conventional DBS. Furthermore, our simulations showed that phase-locked bursts of stimulation did not convey much improvement on regular bursts of oscillation. Using a genetic algorithm optimisation approach to find the best stimulation parameters for regular, irregular and phase-locked bursts, we confirmed that tremor band oscillations could be more readily suppressed. Our results allow exploration of stimulation mechanisms at the network level to formulate testable predictions regarding parameter settings in DBS.

Purpose/Objective(s): Electron beams are frequently used for superficial tumors. However, due to electron beam characteristics the surface dose is 75-95% of the prescribed dose depending on beam energy thus requiring placement of bolus to augment surface dose. Various types of boluses are commonly used in clinics, each having it’s own unique limitation. Most bolus devices do not conform to the skin contour and create airgaps that are known to produce dose perturbations creating hot and cold spots. A cloth-like high-Z materials; Tungsten, (Z=74) and Bismuth, (Z=83) impregnated in silicone gel is investigated for electron bolus. Materials/Methods: Super soft silicone-gel based submillimeter thin tungsten and bismuth sheets were investigated for bolus for 6-12 MeV. Parallel plate ion chamber measurements were performed in a solid water phantom on a Varian machine. Depth dose characteristics were measured to optimize the thickness for surface dose to be 100% for selected electron therapy and validated with Monte Carlo simulations. Results: Silicone-gel tungsten and bismuth sheets produce significant electrons thus increasing surface dose. Based on measured depth dose, our data showed that tungsten sheets of 0.14 mm, 0.18 mm and 0.2 mm and Bismuth sheets of 0.42 mm, 0.18 mm and 0.2 mm provide 100% surface dose for 6, 9 and 12 MeV beams, respectively without any significant changes in depth dose except increasing surface dose. Conclusions: The new high-Z clothlike sheets are extremely soft but high tensile metallic bolus materials that can fit flawlessly on any skin contour. Only 0.2 mm thick sheets are needed for 100% surface dose without degradation of the depth dose characteristics. These materials are reusable and ideal for bolus in electron beam treatment. This investigation opens a new frontier in designing new bolus materials optimum for patient treatment.

Orthopedic surgery relies on bone drills to create tunnels for fracture fixation, bone fusion, or tendon repair. Traditional rigid and straight bone drills often pose challenges in accessing the desired entry points without risking damage to the surrounding anatomical structures, especially in minimal invasive procedures. In this study, we explore the use of hydraulic pressure waves in a flexible bone design to facilitate bone drilling. The HydroFlex Drill includes a handle for generating a hydraulic pressure wave in the flexible, fluid-filled shaft to transmit an impulse to the hammer tip, enabling bone drilling. We evaluated seven different hammer tip shapes to determine their impact on drilling efficiency. Subsequently, the most promising tip was implemented in the HydroFlex Drill. The HydroFlex Drill Validation demonstrated the drill’s ability to successfully transfer the impulse generated in the handle to the hammer tip, with the shaft in different curves. This combined with the drill’s ability to create indentations in bone phantom material is a promising first step towards the development of a flexible or even steerable bone drill. With ongoing research to enhance the drilling efficiency, the HydroFlex Drill opens possibilities for a range of orthopedic surgical procedures where minimally invasive drilling is essential.

Fracture-related infections are burdensome conditions that affect both a patient’s health and financial well-being. Preventing an infection and stabilizing the fracture are critical aspects in a care plan that rely on antibiotics and orthopedic implants, both which need to be improved. Bacteriophage or phage are viruses that specifically kill bacteria and are a promising alternative/companion to antibiotics while enhanced orthopedic implants that are osteoinductive and biodegradable are needed for bone healing. In this work we report the inhibitory effectiveness of three phages Ø K, Ø 0146, and Ø 104023 alone and in combination against a strain of methicillin-resistant Staphylococcus aureus. Single phage and cocktails were mixed with bacteria at multiplicities of infection of 5 and 2.5 and growth was measured using optical density over 48 hours. Ø K alone and Ø K + Ø 0146 were able to completely inhibit bacterial growth. We also present and the ability of Ø K to bind to and be released from a biodegradable and biocompatible orthopedic carbon scaffold. The carbon scaffold was soaked in a solution of Ø K, washed, and then incubated in sequential buffer baths while samples were removed at timepoints up to seven days to calculate phage elution. At every timepoint measured including seven days, phages were found to still be eluting from the scaffold. These results indicate that the studied phages are effective bacterial inhibitors and could be used to prevent infections. Furthermore, orthopedic implants such as a carbon scaffold can be coated with phage to provide long-term protection. In vivo infection experiments on phage loaded scaffold that test bacterial clearance, phage persistence in tissue, resolution of inflammation, and bone regrowth with an active infection are needed to further this work.

The diagnosis of neurological disorders often involves analyzing EEG data, which can be contaminated by artifacts from eye movements or blinking (EOG). To improve the accuracy of EEG-based analysis, we propose a novel framework, VME-EFD, which combines Variational Mode Extraction (VME) and Empirical Fourier Decomposition (EFD) for effective EOG artifact removal. In this approach, the EEG signal is first decomposed by VME into two segments: the desired EEG signal and the EOG artifact. The EOG component is further processed by EFD, where decomposition levels are analyzed based on energy and skewness. The level with the highest energy and skewness, corresponding to the artifact, is discarded, while the remaining levels are reintegrated with the desired EEG. Simulations on both synthetic and real EEG datasets demonstrate that VME-EFD outperforms existing methods, with lower RRMSE (0.1358 vs. 0.1557, 0.1823, 0.2079, 0.2748), lower ΔPSD in the α band (0.10±0.01 and 0.17±0.04 vs. 0.89±0.91 and 0.22±0.19, 1.32±0.23 and 1.10±0.07, 2.86±1.30 and 1.19±0.07, 3.96±0.56 and 2.42±2.48), and higher correlation coefficient (CC: 0.9732 vs. 0.9695, 0.9514, 0.8994, 0.8730). The framework effectively removes EOG artifacts and preserves critical EEG features, particularly in the α band, making it highly suitable for brain-computer interface (BCI) applications.

Background and purpose. This study presents machine learning (ML) models that predict if deep inspiration breath hold (DIBH) is needed based on lung dose in right-sided breast cancer patients during the initial computed tomography (CT) appointment. Materials and methods. Anatomic distances were extracted from a single-institution dataset of free breathing (FB) CT scans from locoregional right-sided breast cancer patients. Models were developed using combinations of anatomic distances and ML classification algorithms (gradient boosting, k-nearest neighbors, logistic regression, random forest, and support vector machine) and optimized over 100 iterations using stratified 5-fold cross-validation. Models were grouped by the number of anatomic distances used during development; those with the highest validation accuracy were selected as final models. Final models were compared based on their predictive ability, measurement collection efficiency, and robustness to simulated user error during measurement collection. Results. This retrospective study included 238 patients treated between 2016 and 2021. Model development ended once eight anatomic distances were included, and the validation accuracy plateaued. The best performing model used logistic regression with four anatomic distances achieving 80.5% average testing accuracy, with minimal false negatives and positives (< 27%). The anatomic distances required for prediction were collected within 3 minutes and were robust to simulated user error during measurement collection, changing accuracy by < 5%. Conclusion. Our logistic regression model using four anatomic distances provided the best balance between efficiency, robustness, and ability to predict if DIBH was needed for locoregional right-sided breast cancer patients.

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.

Purpose. This work proposes a new method to assess the performance of radiographic anti-scatter grids (ASGs) without the use of a narrow primary beam, which is difficult to achieve. Method. Three general purpose ASGs were evaluated, two marketed ASGs and a low frequency and high ratio prototype ASG with molybdenum lamellae. A range of high scatter x-ray beams were used in a standardized geometry, with energies ranging from 60 kV to 121 kV, for five beam sizes between 10 × 10 and 30 × 30 cm². The scatter fraction (SF) of each beam was measured in the image plane with and without ASG using the lead beam stop method with an extrapolation function derived from the scatter point spread function (PSF). Results. The primary, scatter and total transmissions of the three ASGs measured for the different x-ray beams allowed the calculation of the grid factor, contrast improvement factor and detective quantum efficiency (DQE) as functions of the input SF. The results obtained for the three ASGs are consistent with those obtained with the standard narrow-beam method and data published in the literature, confirmed the prime importance of the ASG primary transmission and revealed important variations in ASG performance, especially as a function of the input SF and beam size. The break-even input SFs at which the imaging system efficiency was improved by the ASG ranged between 0.18 and 0.52 for the different ASGs and beam characteristics. Significance. The method is proposed as an alternative to current ASG characterization techniques.

Cherenkov radiation is emitted during x-ray irradiation in a linear accelerator (LINAC). Cherenkov light contains many short wavelength components, including ultraviolet (UV) light, which is well-known for its bactericidal effects. A similar phenomenon is probable for human cancer cells. To assess the effect of Cherenkov light on cell death in x-ray irradiation, we employed simulations and UV cell survival data. We measured the survival rates of HeLa cells exposed to 254 nm (UVC) and 310 nm (UVB) light to determine the 50% lethal dose (LD50) required to kill 50% of the cells. For other wavelengths, we utilized literature values to establish the relationship between wavelength and LD50. Due to the broad range of the Cherenkov light spectrum, we need LD50 as a function of wavelength to estimate cell survival solely from Cherenkov light. A Monte Carlo simulation was used to calculate the fluence distribution of Cherenkov light in a 300 mm³ phantom comprised of soft tissue, both with and without absorption of visible light. The latter scenario is considered to be most influenced by Cherenkov light. By combining the fluence distribution and the wavelength-LD50 relationship, we determined the distribution of the survival rate. Our findings indicate that, in the absence of absorption, a radiation dose of approximately 90 Gy or greater is necessary for Cherenkov light to have any effect. As a result, the impact of Cherenkov light on cell survival can be considered negligible for typical dose of 2 Gy.

Understanding the mechanics of the respiratory system is crucial for optimizing ventilator settings and ensuring patient safety. While simple models of the respiratory system typically consider only flow resistance and lung compliance, lung tissue resistance is usually neglected. This study investigated the effect of lung tissue viscoelasticity on delivered mechanical power in a physical model of the respiratory system and the possibility of distinguishing tissue resistance from airway resistance using proximal pressure measured at the airway opening. Three different configurations of a passive physical model of the respiratory system representing different mechanical properties (Tissue resistance model, Airway resistance model, and No-resistance model) were tested. The same volume-controlled ventilation and parameters were set for each configuration, with only the inspiratory flow rates being adjusted. Pressure and flow were measured with a Datex-Ohmeda S/5 vital signs monitor (Datex-Ohmeda, Madison, WI, USA). Tissue resistance was intentionally tuned so that peak pressures and delivered mechanical energy measured at airway opening were similar in Tissue and Airway Resistance models. However, measurements inside the artificial lung revealed significant differences, with Tissue resistance model yielding up to 20% higher values for delivered mechanical energy. The results indicate the need to revise current methods of calculating mechanical power delivery, which do not distinguish between tissue resistance and airway flow resistance, making it difficult to evaluate and interpret the significance of mechanical power delivery in terms of lung ventilation protectivity.

Reactive oxygen species (ROS), which are expressed at high levels in many diseases, can be scavenged by cerium oxide nanoparticles (CeO2NPs). CeO2NPs can cause significant cytotoxicity when administered directly to cells, but this cytotoxicity can be reduced if CeO2NPs can be encapsulated in biocompatible polymers. In this study, CeO2NPs were synthesized using a one-stage process, then purified, characterized, and then encapsulated into an electrospun poly-ε-caprolactone (PCL) scaffold. The direct administration of CeO2NPs to RAW 264.7 Macrophages resulted in reduced ROS levels but lower cell viability. Conversely, the encapsulation of nanoceria in a PCL scaffold was shown to lower ROS levels and improve cell survival. The study demonstrated an effective technique for encapsulating nanoceria in PCL fiber and confirmed its biocompatibility and efficacy. This system has the potential to be utilized for developing tissue engineering scaffolds, targeted delivery of therapeutic CeO2NPs, wound healing, and other biomedical applications.

Purpose. Precise identification of pancreatic tumors is challenging for radiotherapy planning due to the tumor's anatomical variability and poor visualization on 3D cross-sectional imaging. Low extracellular volume fraction (ECVf) correlates with poor vasculature uptake and possible necrosis or hypoxia in pancreatic tumors. This work investigates the feasibility of delineating pancreatic tumors using ECVf spatial distribution maps derived from contrast enhanced dual-energy CT (DECT). Methods and Materials. Data acquired from radiotherapy simulation of 12 pancreatic cancer patients, using a dual source DECT scanner, were analyzed. For each patient, an ECVf distribution of the pancreas was computed from the simultaneously acquired low and high energy DECT series during the late arterial contrast phase combined with the patient’s hematocrit level. Volume of interest (VECVf) maps in ECVf distribution of pancreas were identified by applying an appropriate threshold condition and a connected components clustering algorithm. The obtained VECVf was compared with the clinical gross tumor volume (GTV) using the positive predictive value (PPV), Dice similarity coefficient (DSC), mean distance to agreement (MDA) and true positive rate (TPR). Results. As a proof of concept, our hypothetical threshold condition based on the first quartile separation of the ECVf distribution to find VECVf of the pancreas elucidates the tumor volume within the pancreas. Notably, 7 out of 12 cases studied for VECVf matched well with the GTV and the mean PPV of 0.83 ± 0.12. The mean MDA (2.83 ± 1.0) of the cases confirms that VECVf lies within the tolerance for comparing to the pancreatic GTV. For the remaining 5 cases, the VECVf is substantially affected by other compounding factors, e.g., large cysts, dilate ducts, and thus did not align with the GTVs. Conclusions. This work demonstrated the promising application of the ECVf map, derived from contrast enhanced DECT, to help delineate tumor target for RT planning of pancreatic cancer.

This large multicenter study of 37 magnetic resonance imaging scanners aimed at characterizing, for the first time, spatial profiles of inaccuracy (namely, Δ-profiles) in apparent diffusion coefficient (ADC) values with varying acquisition plan orientation and diffusion weighting gradient direction, using a statistical approach exploiting unsupervised clustering analysis. A diffusion-weighted imaging (DWI) protocol (b-value: 0–200–400–600–800–1000 s mm⁻²) with different combinations of acquisition plan orientation (axial/sagittal/coronal) and diffusion weighting gradient direction (anterior-posterior/left-right/feet-head) was acquired on a standard water phantom. For each acquisition setup, Δ-profiles along the 3 main orthogonal directions were characterized by fitting data with a second order polynomial function (ar² + br + c). Moreover, for each Δ-profile, the maximum minus minimum of the fitting function (δmax) was calculated. The parameters a, b, c, and δmax showed some significant variations between scanner systems by different manufacturers or with different static magnetic field strengths, as well as between different acquisition/estimation setups. Unsupervised clustering analysis showed two evident clusters with significantly different values of parameter a (p < 0.0001), which can be grouped by acquisition protocol/Δ-profile direction but not scanner system. The results of ∆-profiles confirm an appreciable inter-scanner variability in ADC measurement and corroborate the importance of guarantying the reliability of ADC estimations in clinical or research studies, considering for each scanner system the specific acquisition sequence in terms of acquisition plan orientation and diffusion weighting gradient direction.

Iron Deficiency Anemia (IDA) is the nutritional disorder that occurs when the body does not contain enough iron, an essential component of hemoglobin (Hb). The World Health Organization (WHO) estimated that IDA is the main cause of anemia in 1.62 billion cases worldwide [1]. Although IDA rarely results in death, it has significant adverse impacts on human health. During diagnosis, the hemoglobin indices show low mean corpuscular hemoglobin and mean corpuscular hemoglobin volume. On Peripheral Blood Smear (PBS) images viewed under a microscope by hematologists, IDA shows hypochromic and microcytic red cells. The purpose of the proposed research is to develop a computer-aided system that will allow hematologists to diagnose and detect diseases more accurately and quickly, therefore saving them time and effort. In order to diagnose or detect clinical disorders, non-invasive techniques like machine learning algorithms are employed. This work aims to identify IDA by utilizing the RetinaNet-Disentangled Dense Object Detector (DDOD) to localize hypochromic microcytes in PBS images. To the best of our knowledge, this is the first work using the object detection technique to detect IDA based on the Red Blood Cell (RBC) morphology. We carried out an extensive quantitative and qualitative evaluation of the model. Additionally, a comparison was made between the performance of our model and other object detection models. The results showed that our approach outperformed state-of-the-art techniques, with a mean average precision that was more than 8% higher.

Smartphone-assisted urine analyzers estimate the urinary albumin by quantifying color changes at sensor pad of test strips. These strips yield color variations due to the total protein present in the sample, making it difficult to relate to color changes due to specific analyte. We have addressed it using a Lateral Flow Assay (LFA) device for automatic detection and quantification of urinary albumin. LFAs are specific to individual analytes, allowing color changes to be linked to the specific analyte, minimizing the interference. The proposed reader performs automatic segmentation of the region of interest (ROI) using YOLOv5, a deep learning-based model. Concentrations of urinary albumin in clinical samples were classified using customized machine learning algorithms. An accuracy of 96% was achieved on the test data using the k-Nearest Neighbour (k-NN) algorithm. Performance of the model was also evaluated under different illumination conditions and with different smartphone cameras, and validated using standard nephelometer.

The primary objective of this study was to compare organ doses measured using optically stimulated luminescent dosimeters (OSLDs) with those estimated by the CT-EXPO software for common CT protocols. An anthropomorphic ATOM phantom was employed to measure organ doses across head, chest, and abdominal CT scans performed on a Hitachi Supria 16-slice CT scanner. These OSLD measurements were then compared to the estimates provided by the widely used CT-EXPO software. Organ doses were assessed using OSLDs placed in an adult anthropomorphic phantom, with calibration performed through a comprehensive process involving multiple tube potentials and sensitivity corrections. Results from three CT acquisitions per protocol were compared to estimates provided by CT-EXPO software. Findings reveal significant discrepancies between measured and estimated organ doses, with p-values consistently below 0.05 across all organs. For head CT, measured eye lens doses averaged 33.51 mGy, 6.0% lower than the estimated 35.65 mGy. In chest CT, the thyroid dose was 9.82 mGy, 13.5% higher than the estimated 8.65 mGy. For abdominal CT, the liver dose measured 12.11 mGy, 9.6% higher than the estimated 11.05 mGy. Measured doses for the rest of organs were generally lower than those predicted by CT-EXPO, showing some limitations in current estimation models and the importance of precise dosimetry. This study highlights the potential of OSLD measurements as a complementary method for organ dose assessment in CT imaging, emphasizing the need for more accurate organ dose measurement to optimize patient care.

A quantitative assessment for measuring the placenta during gross examination is a crucial step in evaluating the health status of both the mother and the fetus. However, in the current clinical practice, time-consuming and observer variant drawbacks are caused due to manual measurement and subjective determination of placental characteristics. Therefore, we propose a quantitative assessment system for placenta gross examination to efficiently and accurately measuring placental characteristics according to Amsterdam Consensus, including weight and thickness of placenta, length and width of placental disc, length and diameter of umbilical cord, distance from umbilical cord insertion point to placental edges, etc. The proposed system consists of (1) an instrument designed for standard acquisition of image, weight, and thickness of placenta and (2) an algorithm for quantitative morphological assessment based on precise segmentation of placental disc and umbilical cord and localization of umbilical cord insertion point. Considering the complexity of spatial distribution and ambiguous texture of umbilical cord insertion point, we design Umbilical Cord Insertion Point Candidate Generator to provide reliable umbilical cord insertion point location by employing prior structural knowledge of umbilical cord. Therefore, we integrate the Umbilical Cord Insertion Point Candidate Generator with a Base Detector to ensure umbilical cord insertion point is provided when the Base Detector fails to generate high-scoring candidate points. Experimental results on our self-collected placenta dataset demonstrate the effectiveness of our proposed algorithm. The measurements of placental morphological assessment are calculated based on segmentation and localization results. Our proposed quantitative assessment system, along with its associated instrument and algorithm, can automatically extract numerical measurements to boost the standardization and efficiency of placental gross examination.

Purpose. This paper introduces a deep learning method for myocardial strain analysis while also evaluating the efficacy of the method across a public and a private dataset for cardiac pathology discrimination. Methods. We measure the global and regional myocardial strain in cSAX CMR images by first identifying a ROI centered in the LV, obtaining the cardiac structures (LV, RV and Myo) and estimating the motion of the myocardium. Finally, we compute the strain for the heart coordinate system and report the global and regional strain. Results. We validated our method in two public datasets (ACDC, 80 subjects, and CMAC, 16 subjects) and a private dataset (SSC, 75 subjects), containing healthy and pathological cases (acute myocardial infarction, DCM and HCM). We measured the mean Dice coefficient and Hausdorff distance for segmentation accuracy, and the absolute end point error for motion accuracy, and we conducted a study of the discrimination power of the strain and strain rate between populations of healthy and pathological subjects. The results demonstrated that our method effectively quantifies myocardial strain and strain rate, showing distinct patterns across different cardiac conditions achieving notable statistical significance. Results also show that the method’s accuracy is on par with iterative non-parametric registration methods and is also capable of estimating regional strain values. Conclusion. Our method proves to be a powerful tool for cardiac strain analysis, achieving results comparable to other state-of-the-art methods, and computational efficiency over traditional methods.

Objective assessments of shoulder motion are paramount for effective rehabilitation and evaluation of surgical outcomes. Inertial Measurement Units (IMU) have demonstrated promise in providing unbiased movement data. This study is dedicated to evaluating the concurrent construct validity and accuracy of a wearable IMU-based sensor system, called "Motion Shirt", for the assessment of shoulder motion arcs in patients awaiting shoulder replacement surgery. This evaluation was conducted by comparing Motion Shirt data with the Dartfish Motion Analyzer software during the Functional Impairment Test-Hand and Neck/Shoulder/Arm (FIT-HaNSA) test. Thirteen patients (age>50), who were awaiting shoulder replacement surgery, were recruited. The Motion Shirt was employed to measure angular shoulder movements in two planes during the FIT-HaNSA test. Simultaneously, two cameras recorded the participants' movements to provide reference data. Bland-Altman plots were generated to visualize agreement between the Motion Shirt and the reference data obtained from the Dartfish Motion Analyzer software. The data analysis on Bland-Altman plots revealed a substantial level of agreement between the Motion Shirt and Dartfish analysis in measuring shoulder motion. In Task-1, no significant systematic errors were exhibited, with only 3.27% and 2.18% of points exceeding the limits of agreement (LOA) in both elevation and the Plane of Elevation (POE), signifying a high level of concordance. In Task-2, a high level of agreement was also observed in Elevation, with only 3.8% of points exceeding the LOA. However, 5.98% of points exceeded LOA in POE for Task-2. In Task-3, focused on sustained overhead activity, the Motion Shirt showed strong agreement with Dartfish in Elevation (2.44% points exceeded LOA), but in POE, 7.32% points exceeded LOA. The Motion Shirt demonstrated a robust concordance with Dartfish Motion Analyzer system in assessing shoulder motion during the FIT-HaNSA test. These results affirm the Motion Shirt's suitability for objective motion analysis in patients awaiting shoulder replacement surgery.

Postural balance is crucial for daily activities, relying on the coordination of sensory systems. Balance impairment, common in the elderly, is a leading cause of mortality in this population. To analyze balance, methods like postural adjustment analysis using electromyography (EMG) have been developed. With age, women tend to experience reduced mobility and greater muscle loss compared to men. However, few studies have focused on postural adjustments in women of different ages using EMG of the lower limbs during laterolateral and anteroposterior movements. This gap could reveal a decrease in muscle activation time with aging, as activation time is vital for postural adjustments. This study aimed to analyze muscle activation times in women of different ages during postural adjustments. Thirty women were divided into two groups: young and older women. A controlled biaxial force platform was used for static and dynamic balance tests while recording lower limb muscle activity using EMG. Data analysis focused on identifying muscle activation points and analyzing postural adjustment times. Results showed significant differences in muscle activation times between the two groups across various muscles and platform tilt conditions. Younger women had longer muscle activation times than older women, particularly during laterolateral platform inclinations. In anteroposterior movements, older women exhibited longer activation times compared to their laterolateral performance, with fewer differences between the groups. Overall, older women had shorter muscle activation times than younger women, suggesting a potential indicator of imbalance and increased fall risk.