Search Results (32,507)

Onychomycosis is a common fungal nail infection that is difficult to diagnose due to its similarity to other nail conditions. Accurate identification is essential for effective treatment. The current gold standard methods include microscopic examination with potassium hydroxide, fungal cultures, and Periodic acid-Schiff biopsy staining. These conventional techniques, however, suffer from high turnover times, variable sensitivity, reliance on human interpretation, and costs. This study examines the potential of integrating AI (artificial intelligence) with visualization tools like dermoscopy and microscopy to improve the accuracy and efficiency of onychomycosis diagnosis. AI algorithms can further improve the interpretation of these images. The review includes 14 studies from PubMed and IEEE databases published between 2010 and 2024, involving clinical and dermoscopic pictures, histopathology slides, and KOH microscopic images. Data extracted include study type, sample size, image assessment model, AI algorithms, test performance, and comparison with clinical diagnostics. Most studies show that AI models achieve an accuracy comparable to or better than clinicians, suggesting a promising role for AI in diagnosing onychomycosis. Nevertheless, the niche nature of the topic indicates a need for further research. Full article

Symmetries and symmetry-breaking play significant roles in data security. While remote sensing images, being extremely sensitive geospatial data, require protection against tampering or destruction, as well as assurance of the reliability of the data source during application. In view of the increasing complexity of data security of remote sensing images, a single watermark algorithm is no longer adequate to meet the demand of sophisticated applications. Therefore, this study proposes a dual watermarking algorithm that considers both integrity authentication and copyright protection of remote sensing images. The algorithm utilizes Discrete Wavelet Transform (DWT) to decompose remote sensing images, then constructs integrity watermark information by applying Chebyshev mapping to the mean of horizontal and vertical components. This semi-fragile watermark information is embedded into the high-frequency region of DWT using Quantization Index Modulation (QIM). On the other hand, the robust watermarking uses entropy to determine the embedding position within the DWT domain. It combines the stability of Singular Value Decomposition (SVD) and embeds the watermark according to the relationship between the singular values of horizontal, vertical, and high-frequency components. The experiment showed that the proposed watermarking successfully maintains a high level of invisibility even if embedded with dual watermarks. The semi-fragile watermark can accurately identify tampered regions in remote sensing images under conventional image processing. Moreover, the robust watermark exhibits excellent resistance to various attacks such as noise, filtering, compression, panning, rotating, and scaling. Full article

Aim of the Study: To assess how virtual reality (VR) patient-specific simulations can support decision-making processes and improve care in pediatric urology, ultimately improving patient outcomes. Patients and Methods: Children diagnosed with urological conditions necessitating complex procedures were retrospectively reviewed and enrolled in the study. Patient-specific VR simulations were developed with medical imaging specialists and VR technology experts. Routine CT images were utilized to create a VR environment using advanced software platforms. The accuracy and fidelity of the VR simulations was validated through a multi-step process. This involved comparing the virtual anatomical models to the original medical imaging data and conducting feedback sessions with pediatric urology experts to assess VR simulations’ realism and clinical relevance. Results: A total of six pediatric patients were reviewed. The median age of the participants was 5.5 years (IQR: 3.5–8.5 years), with an equal distribution of males and females across both groups. A minimally invasive laparoscopic approach was performed for adrenal lesions (n = 3), Wilms’ tumor (n = 1), bilateral nephroblastomatosis (n = 1), and abdominal trauma in complex vascular and renal malformation (ptotic and hypoplastic kidney) (n = 1). Key benefits included enhanced visualization of the segmental arteries and the deep vascularization of the kidney and adrenal glands in all cases. The high depth perception and precision in the orientation of the arteries and veins to the parenchyma changed the intraoperative decision-making process in five patients. Preoperative VR patient-specific simulation did not offer accuracy in studying the pelvic and calyceal anatomy. Conclusions: VR patient-specific simulations represent an empowering tool in pediatric urology. By leveraging the immersive capabilities of VR technology, preoperative planning and intraoperative navigation can greatly impact surgical decision-making. As we continue to advance in medical simulation, VR holds promise in educational programs to include even surgical treatment of more complex urogenital malformations. Full article

(1) Background: Identifying acute aortic syndrome (AAS) and thoracic aortic aneurysm (TAA) in busy emergency departments (EDs) is crucial due to their life-threatening nature, necessitating timely and accurate diagnosis. (2) Methods: This retrospective case-control study was conducted in the ED of three hospitals. Adult patients visiting the ED between 1 January 2010 and 1 January 2020 with a chief complaint of chest or back pain were enrolled in the study. The collected chest radiography (CXRs) data were divided into training (80%) and testing (20%) datasets. The training dataset was trained by four different convolutional neural network (CNN) models. (3) Results: A total of 1625 patients were enrolled in this study. The InceptionV3 model achieved the highest F1 score of 0.76. (4) Conclusions: Analysis of CXRs using a CNN-based model provides a novel tool for clinicians to interpret ED patients with chest pain and suspected AAS and TAA. The integration of such imaging tools into ED could be considered in the future to enhance the diagnostic workflow for clinically fatal diseases. Full article

In this study, hyperspectral imaging technology with a wavelength range of 450 to 1000 nanometers was used to collect spectral data from 160 Korla pear samples at various maturity stages (immature, semimature, mature, and overripe). To ensure high-quality data, multiple preprocessing techniques such as multiplicative scatter correction (MSC), standard normal variate (SNV), and normalization were employed. Based on these preprocessed data, a custom convolutional neural network model (CNN-S) was constructed and trained to achieve precise classification and identification of the maturity stages of Korla pears. Additionally, a BP neural network model was used to determine the characteristic wavelengths for maturity assessment based on the sugar content feature wavelengths. The results demonstrated that the BP model, based on sugar content feature wavelengths, effectively discriminated the maturity stages of the pears. Specifically, the comprehensive recognition rates for the training, testing, and validation sets were 98.5%, 93.5%, and 90.5%, respectively. Furthermore, the combination of hyperspectral imaging technology and the custom CNN-S model significantly enhanced the detection performance of pear maturity. Compared to traditional CNN models, the CNN-S model improved the accuracy of the test set by nearly 10%. Moreover, the CNN-S model outperformed existing techniques based on partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) in capturing hyperspectral data features, showing superior generalization capability and detection efficiency. The superior performance of this method in practical applications further supports its potential in smart agriculture technology, providing a more efficient and accurate solution for agricultural product quality detection. Additionally, it plays a crucial role in the development of smart agricultural technology. Full article

Circular synthetic aperture radar (CSAR) has attracted a lot of interest, recently, for its excellent performance in civilian and military applications. However, in CSAR imaging, the result is to be defocused when the height of an object deviates from a reference height. Existing approaches for this problem rely on digital elevation models (DEMs) for error compensation. It is difficult and costly to collect DEM using specific equipment, while the inversion of DEM based on echo is computationally intensive, and the accuracy of results is unsatisfactory. Inspired by multi-focus image fusion in optical images, a spatial-domain fusion method is proposed based on the sum of modified Laplacian (SML) and guided filter. After obtaining CSAR images in a stack of different reference heights, an all-in-focus image can be computed by the proposed method. First, the SMLs of all source images are calculated. Second, take the rule of selecting the maximum value of SML pixel by pixel to acquire initial decision maps. Secondly, a guided filter is utilized to correct the initial decision maps. Finally, fuse the source images and decision maps to obtain the result. A comparative experiment has been processed to verify the exceptional performance of the proposed method. The final processing result of real-measured CSAR data demonstrated that the proposed method is effective and practical. Full article

Effective X-ray photon-counting spectral imaging (x-CSI) detector design involves the optimisation of a wide range of parameters both regarding the sensor (e.g., material, thickness and pixel pitch) and electronics (e.g., signal-processing chain and count-triggering scheme). Our previous publications have looked at the role of pixel pitch, sensor thickness and a range of additive charge sharing correction algorithms (CSCAs), and in this work, we compare additive and subtractive CSCAs to identify the advantages and disadvantages. These CSCAs differ in their approach to dealing with charge sharing: additive approaches attempt to reconstruct the original event, whilst subtractive approaches discard the shared events. Each approach was simulated on data from a wide range of x-CSI detector designs (pixel pitches 100–600 µm, sensor thickness 1.5 mm) and X-ray fluxes (10⁶–10⁹ photons mm⁻² s⁻¹), and their performance was characterised in terms of absolute detection efficiency (ADE), absolute photopeak efficiency (APE), relative coincidence counts (RCC) and binned spectral efficiency (BSE). Differences between the two approaches were explained mechanistically in terms of the CSCA’s effect on both charge sharing and pule pileup. At low X-ray fluxes, the two approaches perform similarly, but at higher fluxes, they differ in complex ways. Generally, additive CSCAs perform better on absolute metrics (ADE and APE), and subtractive CSCAs perform better on relative metrics (RCC and BSE). Which approach to use will, thus, depend on the expected operating flux and whether dose efficiency or spectral efficiency is more important for the application in mind. Full article

This study evaluates multimodal imaging for characterizing microstructures in partially impregnated thermoplastic matrix composites made of woven glass fiber and polypropylene. The research quantifies the impregnation degree of fiber bundles within composite plates manufactured through a simplified compression resin transfer molding process. For comparison, a reference plate was produced using compression molding of film stacks. An original surface polishing procedure was introduced to minimize surface defects while polishing partially impregnated samples. Extended-field 2D imaging techniques, including polarized light, fluorescence, and scanning electron microscopies, were used to generate images of the same microstructure at fiber-scale resolutions throughout the plate. Post-processing workflows at the macro-scale involved stitching, rigid registration, and pixel classification of FM and SEM images. Meso-scale workflows focused on 0°-oriented fiber bundles extracted from extended-filed images to conduct quantitative analyses of glass fiber and porosity area fractions. A one-way ANOVA analysis confirmed the reliability of the statistical data within the 95% confidence interval. Porosity quantification based on the conducted multimodal approach indicated the sensitivity of the impregnation degree according to the layer distance from the pool of melted polypropylene in the context of simplified-CRTM. The findings underscore the potential of multimodal imaging for quantitative analysis in composite material production. Full article

Background: The rate of in-stent restenosis (ISR) is decreasing; however, it is still a challenge for contemporary invasive cardiologists. Therapeutic methods, including drug-eluting balloons (DEBs), intravascular lithotripsy, excimer laser coronary atherectomy, and imaging-guided percutaneous coronary intervention (PCI) with drug-eluting stents (DES), have been implemented. Patients with diabetes mellitus (DM) are burdened with a higher risk of ISR than the general population. Aims: DM-Dragon is aimed at evaluating the clinical outcomes of ISR treatment with DEBs vs. DES, focusing on patients with co-existing diabetes mellitus. Methods: The DM-Dragon registry is a retrospective study comprising data from nine high-volume PCI centers in Poland. A total of 1117 patients, of whom 473 individuals had DM and were treated with PCI due to ISR, were included. After propensity-score matching (PSM), 198 pairs were created for further analysis. The primary outcome of the study was target lesion revascularization (TLR). Results: In DM patients after PSM, TLR occurred in 21 (10.61%) vs. 20 (10.1%) in non-diabetic patients, p = 0.8690. Rates of target vessel revascularization (TVR), target vessel myocardial infarction, device-oriented composite endpoint (DOCE), and cardiac death did not differ significantly. Among diabetic patients, the risk of all-cause mortality was significantly lower in the DEB group (2.78% vs. 11.11%, HR 3.67 (95% confidence interval, CI) [1.01–13.3), p = 0.0483). Conclusions: PCI with DEBs is almost as effective as DES implantation in DM patients treated for ISR. In DM-Dragon, the rate of all-cause death was significantly lower in patients treated with DEBs. Further large-scale, randomized clinical trials would be needed to support these findings. Full article

Computed tomography (CT) is a widely utilised imaging technique in both clinical and industrial applications. CT scan results, presented as a volume revealing linear attenuation coefficients, are intricately influenced by scan parameters and the sample’s geometry and material composition. Accurately mapping these coefficients to specific materials is a complex task. Traditionally, material decomposition in CT relied on classical algorithms using handcrafted features based on X-ray physics. However, there is a rising trend towards data-driven approaches, particularly deep learning, which offer promising improvements in accuracy and efficiency. This survey explores the transition from classical to data-driven approaches in material-sensitive CT, examining a comprehensive corpus of literature identified through a detailed and reproducible search using Scopus. Our analysis addresses several key research questions: the origin and generation of training datasets, the models and architectures employed, the extent to which deep learning methods reduce the need for domain-specific expertise, and the hardware requirements for training these models. We explore the implications of these findings on the integration of deep learning into CT practices and the potential reduction in the necessity for extensive domain knowledge. In conclusion, this survey highlights a significant shift towards deep learning in material-resolving CT and discusses the challenges and opportunities this presents. The transition suggests a future where data-driven approaches may dominate, offering enhanced precision and robustness in material-resolving CT while potentially transforming the role of domain experts in the field. Full article

This study aims to enhance the detection accuracy and efficiency of cotton bolls in complex natural environments. Addressing the limitations of traditional methods, we developed an automated detection system based on computer vision, designed to optimize performance under variable lighting and weather conditions. We introduced COTTON-YOLO, an improved model based on YOLOv8n, incorporating specific algorithmic optimizations and data augmentation techniques. Key innovations include the C2F-CBAM module to boost feature recognition capabilities, the Gold-YOLO neck structure for enhanced information flow and feature integration, and the WIoU loss function to improve bounding box precision. These advancements significantly enhance the model’s environmental adaptability and detection precision. Comparative experiments with the baseline YOLOv8 model demonstrated substantial performance improvements with COTTON-YOLO, particularly a 10.3% increase in the AP₅₀ metric, validating its superiority in accuracy. Additionally, COTTON-YOLO showed efficient real-time processing capabilities and a low false detection rate in field tests. The model’s performance in static and dynamic counting scenarios was assessed, showing high accuracy in static cotton boll counting and effective tracking of cotton bolls in video sequences using the ByteTrack algorithm, maintaining low false detections and ID switch rates even in complex backgrounds. Full article

Real-time and accurate temperature monitoring during microwave hyperthermia (MH) remains a critical challenge for ensuring treatment efficacy and patient safety. This study presents a novel approach to simulate real MH and precisely determine the temperature of the target region within biological tissues using a temporal-informed neural network. We conducted MH experiments on 30 sets of phantoms and 10 sets of ex vivo pork tissues. We proposed a novel perspective: the evolving tissue responses to continuous electromagnetic radiation stimulation are a joint evolution in temporal and spatial dimensions. Our model leverages TimesNet to extract periodic features and Cloblock to capture global information relevance in two-dimensional periodic vectors from ultrasound images. By assimilating more ultrasound temporal data, our model improves temperature-estimation accuracy. In the temperature range 25–65 °C, our neural network achieved temperature-estimation root mean squared errors of approximately 0.886 °C and 0.419 °C for fresh ex vivo pork tissue and phantoms, respectively. The proposed temporal-informed neural network has a modest parameter count, rendering it suitable for deployment on ultrasound mobile devices. Furthermore, it achieves temperature accuracy close to that prescribed by clinical standards, making it effective for non-destructive temperature monitoring during MH of biological tissues. Full article

Soil freeze-thaw (FT) cycles over agricultural lands are of great importance due to their vital role in controlling soil moisture distribution, nutrient availability, health of microbial communities, and water partitioning during flood events. Active microwave sensors such as C-band Sentinel-1 synthetic aperture radar (SAR) can serve as powerful tools to detect field-scale soil FT state. Using Sentinel-1 SAR observations, this study compares the performance of two FT detection approaches, a commonly used seasonal threshold approach (STA) and a computationally inexpensive general threshold approach (GTA) at an agricultural field in New Hampshire, US. It also explores the applicability of an interferometric coherence approach (ICA) for FT detection. STA and GTA achieved 85% and 78% accuracy, respectively, using VH polarization. We find a marginal degradation in the performance of STA (82%) and GTA (76%) when employing VV-polarized data. While there was approximately a 6 percentage point difference between STA’s and GTA‘s overall accuracy, we recommend GTA for FT detection using SAR images at sub-field-scale over extended regions because of its higher computational efficiency. Our analysis shows that interferometric coherence is not suitable for detecting FT transitions under mild and highly dynamic winter conditions. We hypothesize that the relatively mild winter conditions and therefore the subtle FT transitions are not able to significantly reduce the correlation between the phase values. Also, the ephemeral nature of snowpack in our study area, further compounded by frequent rainfall, could cause decorrelation of SAR images even in the absence of a FT transition. We conclude that despite Sentinel-1’s ~80% mapping accuracy at a mid-latitude site, understanding the cause of misclassification remains challenging, even when detailed ground data are readily available and employed in error attribution efforts. Full article

The timely and accurate mapping of sugarcane cultivation is significant to ensure the sustainability of the sugarcane industry, including sugarcane production, rural society, sugar futures, and crop insurance. Synthetic aperture radar (SAR), due to its all-weather and all-time imaging capability, plays an important role in mapping sugarcane cultivation in cloudy areas. However, the inherent speckle noise of SAR data worsens the “salt and pepper” effect in the sugarcane map. Therefore, in previous studies, an additional land cover map or optical image was still required. This study proposes a new application paradigm of time series SAR data for sugarcane mapping to tackle this limitation. First, the locally estimated scatterplot smoothing (LOESS) smoothing technique was exploited to reconstruct time series SAR data and reduce SAR noise in the time domain. Second, temporal importance was evaluated using RF MDA ranking, and basic parcel units were obtained only based on multi-temporal SAR images with high importance values. Lastly, the parcel-based classification method, combining time series smoothing SAR data, RF classifier, and basic parcel units, was used to generate a sugarcane extent map without unreasonable sugarcane spots. The proposed paradigm was applied to map sugarcane cultivation in Suixi County, China. Results showed that the proposed paradigm was able to produce an accurate sugarcane cultivation map with an overall accuracy of 96.09% and a Kappa coefficient of 0.91. Compared with the pixel-based classification result with original time series SAR data, the new paradigm performed much better in reducing the “salt and pepper” spots and improving the completeness of the sugarcane plots. In particular, the unreasonable non-vegetation spots in the sugarcane map were eliminated. The results demonstrated the efficacy of the new paradigm for mapping sugarcane cultivation. Unlike traditional methods that rely on optical remote sensing data, the new paradigm offers a high level of practicality for mapping sugarcane in large regions. This is particularly beneficial in cloudy areas where optical remote sensing data is frequently unavailable. Full article

Revision total hip arthroplasty (rTHA) presents significant challenges, particularly in patients with severe acetabular bone defects. Traditional treatment options often fall short, leading to the emergence of custom-made 3D-printed acetabular implants. Accurate assessment of implant positioning is crucial for ensuring optimal postoperative outcomes and for providing feedback to the surgical team. This single-center, retrospective cohort study evaluates the accuracy of standard 2D radiographs versus 3D CT scans in assessing the positioning of these implants, aiming to determine if 2D imaging could serve as a viable alternative for the postoperative evaluation. We analyzed the implant positions of seven rTHA patients with severe acetabular defects (Paprosky ≥ Type IIIA) using an alignment technique that integrates postoperative 2D radiographs with preoperative CT plans. Two independent investigators, one inexperienced and one experienced, measured the positioning accuracy with both imaging modalities. Measurements included translational shifts from the preoperatively templated implant position in the craniocaudal (CC), lateromedial (LM), and ventrodorsal (VD) directions, as well as rotational differences in anteversion (AV) and inclination (INCL). The study demonstrated that 2D radiographs, when aligned with preoperative CT data, could accurately assess implant positions with precision nearly comparable to that of 3D CT scans. Observed deviations were 1.4 mm and 2.7 mm in CC and LM directions, respectively, and 3.6° in AV and 0.7° in INCL using 2D imaging, all within clinically acceptable ranges. For 3D CT assessments, mean interobserver variability was up to 0.9 mm for translational shifts and 1.4° for rotation, while for 2D alignment, observer differences were 1.4 mm and 3.2° for translation and rotation, respectively. Comparative analysis of mean results from both investigators, across all dimensions (CC, LM, AV, and INCL) for 2D and 3D matching, showed no significant differences. In conclusion, conventional anteroposterior 2D radiographs of the pelvis can sufficiently determine the positioning of custom-made acetabular implants in rTHA. This suggests that 2D radiography is a viable alternative to 3D CT scans, potentially enhancing the implementation and quality control of advanced implant technologies. Full article