Search Results (79)

The accurate classification of banana ripeness is essential for optimising agricultural practices and enhancing food industry processes. This study investigates the classification of banana ripeness using Machine Learning (ML) and Deep Learning (DL) techniques. The dataset consisted of 1565 high-resolution images of bananas captured over a 20-day ripening period using a Canon EOS 90D camera under controlled lighting and background conditions. High-resolution images of bananas at different ripeness stages were classified into ‘unripe’, ‘ripe’, and ‘overripe’ categories. The training set consisted of 1398 images (89.33%), and the validation set consisted of 167 images (10.67%), allowing for robust model evaluation. Various ML models, including Decision Tree, Random Forest, KNN, SVM, CNN, and VGG models, were trained and evaluated for ripeness classification. Among these, DL models, particularly CNN and VGG, outperformed traditional ML algorithms, with the CNN and VGG achieving accuracy rates of 90.42% and 89.22%, respectively. These rates surpassed those of Decision Trees (71.86%), Random Forests (85.63%), KNNs (86.83%), and SVMs (89.22%). The study points out the importance of dataset quality, model selection, and preprocessing techniques in achieving accurate ripeness classification. Practical applications of these results include optimised harvesting practices, enhanced post-harvest handling, improved consumer experience, streamlined supply chain logistics, and automation in sorting systems. These results confirm the feasibility of using deep learning for the automated classification of ripening stages, with implications for reducing postharvest losses and improving supply chain logistics. These findings have significant implications for stakeholders in the banana industry, from farmers to consumers, and pave the way for the development of innovative solutions for banana ripeness classification. Full article

The growing demand for natural preservatives in the food industry has highlighted the importance of essential oils (EOs), despite their limitations related to volatility and oxidative instability. This study addresses these challenges by developing pectin-based microcapsules for encapsulating lemon essential oil (LEO) using ultrasound-assisted ionotropic gelation. The EO, extracted from Citrus limon (Eureka variety), exhibited a high limonene content (56.18%) and demonstrated significant antioxidant (DPPH IC50: 28.43 ± 0.14 µg/mL; ABTS IC50: 35.01 ± 0.11 µg/mL) and antifungal activities, particularly against A. niger and Botrytis spp. Encapsulation efficiency improved to 82.3% with ultrasound pretreatment, and SEM imaging confirmed spherical, uniform capsules. When applied to fresh-cut apples, LEO-loaded capsules significantly reduced browning (browning score: 1.2 ± 0.3 vs. 2.8 ± 0.2 in control), microbial load (4.9 ± 0.2 vs. 6.5 ± 0.4 log CFU/g), and weight loss (4.2% vs. 6.4%) after 10 days of storage at 4 °C. These results underscore the potential of ultrasound-enhanced pectin encapsulation for improving EO stability and efficacy in food preservation systems. Full article

Purpose: The purpose of this scoping review was to systematically identify and summarize the existing literature on non-spinal clinical applications of EOS imaging and identify related evidence gaps. Method: The study followed the PRISMA-ScR guidelines. A systematic literature search was conducted in Embase, MEDLINE, CINAHL, Scopus, Cochrane, Academic Search Premier, and OpenGrey databases in November 2022 and updated in December 2023. Original research from 2003 to 2023 was eligible if in English, Danish, French, German, Norwegian, or Swedish. Two authors screened articles by title and abstract, while data extraction from full texts was performed by seven authors using a structured template. Results: A total of 8176 articles were identified, with 1350 selected for full-text review and 268 included in data extraction. Among adults, 187 articles were included, with 88 focused on surgical applications like hip arthroplasty or osteotomy. In pediatrics, 68 general and 13 surgery-related articles were included. Lower extremity analysis was the most frequent topic, with other uses identified, such as rib cage geometry, patellar dislocation, and X-linked hypophosphatemia. Conclusions: Key clinical applications of EOS imaging include lower extremity analysis, e.g., leg length assessment and knee/hip arthroplasty planning), pelvic and spinal alignment studies, and emerging uses in rib cage geometry. Evidence gaps include limited research on the diagnostic accuracy of EOS for cerebral shunt placement, reliability in bone age estimation, and an unclear role in foot and ankle morphology. Full article

Esophageal fibrotic remodeling is a major complication of chronic inflammation in eosinophilic esophagitis (EoE) and represents one of the main determinants of symptoms in adult patients with EoE, with a remarkable impact on patients’ quality of life and the healthcare system. Esophageal fibrotic remodeling is diagnosed through upper gastrointestinal endoscopy, radiological studies, and a functional luminal imaging probe. However, diagnostic underestimation of esophageal strictures and suboptimal adherence to EoE guidelines still represent limitations of current clinical practice. Combined with medical therapy and/or elimination diets, endoscopic dilation remains the cornerstone treatment for esophageal strictures and rings, offering a safe and effective option for managing obstructive symptoms. Different modalities are available for esophageal endoscopic dilation of EoE, including mechanical and balloon dilators. Mechanical dilators provide tactile feedback during the procedure and exert longitudinal and radial forces. In contrast, balloon dilators apply a purely radial force and enable direct visualization of the esophageal mucosa during the procedure. Both mechanical and balloon dilators are safe and effective, with no single modality demonstrating clear superiority. Consequently, the choice of dilation technique is guided by stricture characteristics, the expertise of the endoscopist, and considerations related to the financial and environmental sustainability of the devices. This review aims to summarize the most relevant evidence on the endoscopic evaluation and dilation of fibrostenotic complications in EoE, also providing practical guidance for clinicians to optimize the endoscopic management of these patients. Full article

Objective: Current good manufacturing practice (cGMP) guidance for positron emission tomography (PET) drugs has been established in Europe and the United States. In Japan, the Pharmaceuticals and Medical Devices Agency (PMDA) approved the use of radiosynthesizers as medical devices for the in-house manufacturing of PET drugs in hospitals and clinics, regardless of the cGMP environment. Without adequate facilities, equipment, and personnel required by cGMP regulations, the quality assurance (QA) and clinical effectiveness of PET drugs largely depend on the radiosynthesizers themselves. To bridge the gap between radiochemistry standardization and site qualification, the Japanese Society of Nuclear Medicine (JSNM) has issued guidance for the in-house manufacturing of small-scale PET drugs under academic GMP (a-GMP) environments. The goals of cGMP and a-GMP are different: cGMP focuses on process optimization, certification, and commercialization, while a-GMP facilitates the small-scale, in-house production of PET drugs for clinical trials and patient-specific standard of care. Among PET isotopes, N-13 has a short half-life (10 min) and must be synthesized on site. [¹³N]Ammonia ([¹³N]NH₃) is used for myocardial perfusion imaging under the Japan Health Insurance System (JHIS) and was thus selected as a working example for the manufacturing of PET drugs in an a-GMP environment. Methods: A [¹³N]NH₃-radiosynthesizer was installed in a hot cell within an a-GMP-compliant radiopharmacy unit. To comply with a-GMP regulations, the air flow was adjusted through HEPA filters. All cabinets and cells were disinfected to ensure sterility once a month. Standard operating procedures (SOPs) were applied, including analytical methods. Batch records, QA data, and radiation exposure to staff in the synthesis of [¹³N]NH₃ were measured and documented. Results: 2.52 GBq of [¹³N]NH₃ end-of-synthesis (EOS) was obtained in an average of 13.5 min in 15 production runs. The radiochemical purity was more than 99%. Exposure doses were 11 µSv for one production run and 22 µSv for two production runs. The pre-irradiation background dose rate was 0.12 µSv/h. After irradiation, the exposed dosage in the front of the hot cell was 0.15 µSv/h. The leakage dosage measured at the bench was 0.16 µSv/h. The exposure and leakage dosages in the manufacturing of [¹³N]NH₃ were similar to the background level as measured by radiation monitoring systems in an a-GMP environments. All QAs, environmental data, bacteria assays, and particulates met a-GMP compliance standards. Conclusions: In-house a-GMP environments require dedicated radiosynthesizers, documentation for batch records, validation schedules, radiation protection monitoring, air and particulate systems, and accountable personnel. In this study, the in-house manufacturing of [¹³N]NH₃ under a-GMP conditions was successfully demonstrated. These findings support the international harmonization of small-scale PET drug manufacturing in hospitals and clinics for future multi-center clinical trials and the development of a standard of care. Full article

This paper proposes a frequency-modulated continuous-wave synthetic aperture radar (FMCW-SAR) imaging system for fast target detection. The system’s antenna array improves azimuthal resolution while maintaining low complexity using a 44-element equivalent virtual array and improves the data acquisition efficiency by employing the trigger and MCU control board. A series of improved algorithms are adopted to increase the speed of radar imaging and achieve fast detection. To solve the problem of large data volumes in traditional array antenna switching control methods, an array switching control algorithm is proposed based on the enhanced ordered statistical constant false alarm rate (EOS-CFAR). The data volume is reduced by dividing the array into several subarrays in advance. The echo signals acquired by the array switching control method are not continuous in the azimuthal direction, and data anomalies are handled by interpolating and compensating the received radar data to form compensated periodic data. The coherent background is subtracted from the padded signal using recursive averaging, resulting in high-resolution imaging while improving the data-processing speed. The TensorFlow-based Omega-K algorithm is employed for synthetic aperture radar (SAR) imaging, which customizes the optimization of TensorFlow for array radar signals. For the radar signal phase optimization, an improved Adam Optimizer optimizes the phase of the radar signal to maintain phase smoothing, thereby improving the clarity of the radar image. The Omega-K algorithm is optimized by TensorFlow and accelerated on the GPU to improve the efficiency of the large-scale fast Fourier transform (FFT) and Stolt interpolation operations, which improves the speed of radar imaging and enables fast detection. Full article

Accurate forest monitoring is critical for achieving the objectives of the European Green Deal. While national forest inventories provide consistent information on the state of forests, their temporal frequency is inadequate for monitoring fast-growing species with 15-year rotations when inventories are conducted every 10 years. However, Earth observation (EO) satellite systems can be used to address this challenge. Remote sensing satellites enable the continuous acquisition of land cover data with high temporal frequency (annually or shorter), at a spatial resolution of 10-30 m per pixel. This study focused on northern Spain, a highly productive forest region. This study aimed to improve models for predicting forest variables in forest plantations in northern Spain by integrating optical (Sentinel-2) and imaging radar (Sentinel-1, ALOS-2 PALSAR-2 and TanDEM-X) datasets supported by climatic and terrain variables. Five popular machine learning algorithms were compared, namely kNN, LightGBM, Random Forest, MLR, and XGBoost. The study findings show an improvement in R² from 0.24 when only Sentinel-2 data are used with MultiLinear Regression to 0.49 when XGboost is used with multi-source EO data. It can be concluded that the combination of multi-source datasets, regardless of the model used, significantly enhances model performance, with TanDEM-X data standing out for their remarkable ability to provide valuable radar information on forest height and volume, particularly in a complex terrain such as northern Spain. Full article

In this study, we proposed a YOLOv8-based Multi-Level Multi-Head Attention mechanism utilizing EO and IR cameras to enable rapid and accurate detection of vessels of various sizes in maritime environments. The proposed method integrates the Scale-Sensitive Cross Attention module and the Self-Attention module, with a particular focus on enhancing small object detection performance in low-resolution IR imagery. By leveraging a multi-level attention mechanism, the model effectively improves detection performance for both small and large objects, outperforming the baseline YOLOv8 model. To further optimize the performance of IR cameras, we introduced a color palette preprocessing technique and identified the optimal palette through a comparative analysis. Experimental results demonstrated that the Average Precision increased from 85.3 to 88.2 in EO camera images and from 68.2 to 73 in IR camera images when the Black Hot palette was applied. The Black Hot palette, in particular, provided high luminance contrast, effectively addressing the single-channel and low-resolution limitations of IR imagery, and significantly improved small object detection performance. The proposed technique shows strong potential for enhancing vessel detection performance under diverse environmental conditions and is anticipated to make a practical contribution to real-time maritime monitoring systems. Furthermore, by delivering high reliability and efficiency in data-constrained environments, this method demonstrates promising scalability for applications in various object detection domains. Full article

Given the recent proliferation of Unmanned Aerial Systems (UASs) and the consequent importance of counter-UASs, this project aims to perform the detection and tracking of small non-cooperative UASs using Electro-optical (EO) and Infrared (IR) sensors. Two data integration techniques, at the decision and pixel levels, are compared with the use of each sensor independently to evaluate the system robustness in different operational conditions. The data are submitted to a YOLOv7 detector merged with a ByteTrack tracker. For training and validation, additional efforts are made towards creating datasets of spatially and temporally aligned EO and IR annotated Unmanned Aerial Vehicle (UAV) frames and videos. These consist of the acquisition of real data captured from a workstation on the ground, followed by image calibration, image alignment, the application of bias-removal techniques, and data augmentation methods to artificially create images. The performance of the detector across datasets shows an average precision of 88.4%, recall of 85.4%, and mAP@0.5 of 88.5%. Tests conducted on the decision-level fusion architecture demonstrate notable gains in recall and precision, although at the expense of lower frame rates. Precision, recall, and frame rate are not improved by the pixel-level fusion design. Full article

Background/Objectives: This study investigated whether decompression surgery for lumbar spinal stenosis can effectively improve sagittal alignment in elderly patients. With the growing focus on sagittal balance in spinal surgery, this study aimed to evaluate post-decompression alignment changes and identify the factors influencing these changes using the EOS imaging system. Methods: A retrospective analysis was conducted on 49 elderly patients who underwent decompression surgery alone for lumbar spinal stenosis. Radiologic parameters, measured using the EOS system, and clinical outcomes were assessed preoperatively, at two weeks postoperatively, and at one year postoperatively. Patients were grouped based on the improvement of the sagittal vertical axis (SVA) by 25 mm or more. A multivariate analysis was performed to identify factors affecting sagittal alignment changes. Results: Significant sagittal alignment improvements were observed postoperatively, including a notable increase in thoracic kyphosis and a decrease in SVA observed at one year. Clinical outcomes, such as the Oswestry disability index (ODI) and EQ-5D, significantly improved at both two weeks and one year postoperatively compared to preoperative values (all p-values < 0.05). Multivariate analysis revealed that greater preoperative SVA and higher ODI were significant predictors of sagittal alignment changes (odds ratio [OR] for SVA = 1.014, OR for ODI = 1.034). Conclusions: Decompression surgery for lumbar spinal stenosis in elderly patients can result in significant improvements in sagittal alignment and clinical outcomes. The study suggests that decompression alone is a viable surgical option for elderly patients, particularly those with a greater preoperative sagittal imbalance and disability, even in the absence of major deformities. Full article

Background/Objectives: Obstructive hydrocephalus associated with vestibular schwannoma (VS) is the most common in giant VS. Despite tumor removal, some patients may require ongoing ventriculo-peritoneal (VP) surgery. This investigation explores the factors contributing to the requirement for VP surgery following VS surgery in instances of persistent hydrocephalus (HCP). Methods: Volumetric MRI analyses of pre- and postoperative tumor volumes, cerebellum, cerebrum, ventricle system, fourth ventricle, brainstem, and peritumoral edema were conducted using Brainlab Smartbrush and 3D Slicer. The total brain volume was defined as the sum of the cerebrum, cerebellum, and brainstem. ROC analyses were performed to identify the optimum cut-off values of the volumetric data. Results: Permanent cerebrospinal fluid (CSF) diversion after surgery was indicated in 12 patients (12/71; 16.9%). The ratio of baseline volume fraction of brain ventricles to total brain ventricle volume (VTB ratio) was found to predict postoperative VP shunt dependency. The AUC was 0.71 (95% CI: 0.51–0.91), and the optimum threshold value (</≥0.449) yielded a sensitivity and specificity of 67% and 81%, respectively. Multivariable logistic regression analyses of imaging data (pre- and postoperative VS volume, VTB ratio, and extent of resection (%) (EoR)) and patient-specific factors revealed that an increased VTB ratio (≥0.049, OR: 6.2, 95% CI: 1.0–38.0, p = 0.047) and an EoR < 96.4% (OR: 9.1, 95% CI: 1.2–69.3, p = 0.032) were independently associated with postoperative VP shunt dependency. Conclusions: Primary tumor removal remains the best treatment to reduce the risk of postoperative persistent hydrocephalus. However, patients with an increased preoperative VTB ratio are prone to needing postoperative VP shunt surgery and may benefit from perioperative EVD placement. Full article

Whole-spine radiography can be accomplished through two methods: (1) segmented imaging employing X-ray tube angulation and detectors, or (2) the Euronext Paris Advanced Orthopedic Solutions (EOS) 2D Imaging system that can capture the entire spine in a single image using X-ray tubes and detectors oriented at a 90-degree angle. This study aimed to establish optimal EOS examination parameters based on patient morphotype and scan speed to reduce patient radiation exposure, repeat examinations, heat stress on equipment, and X-ray tube cooling time. X-ray exposure conditions involved adjustments of scan speed ranging from two to four steps, contingent upon the patient’s morphotype (‘S’, small body; ‘M’, medium body; and ‘L’, large body. Patient dose measurements were conducted 20 times for each set of conditions. When transitioning from an ‘S’ to an ‘M’ morphotype at a constant scan speed, the entrance skin dose (ESD) exhibited an increase of approximately 41.25 ± 4.57%. A similar change from an ‘M’ to an ‘L’ morphotype resulted in an ESD increase of roughly 59.56 ± 24.00%. A transition from an ‘S’ to an ‘L’ morphotype at the same scan speed manifested an ESD elevation of approximately 124.21 ± 26.96%. This study underscores significant variations in radiation dose, ranging from 40% to 50%, when altering morphotype while maintaining a consistent scan speed. Full article

This study assessed the surface topography and plastic deformation (PD) of new and used contemporary reciprocating instruments. Twenty-six WaveOne Gold (WOG) and EdgeOne Fire (EO) instruments were photographed under magnification. The instruments were randomly assigned to a control group of new instruments preserved for surface roughness analysis (n = 6 each), or to an experimental group to shape the root canal system of a single molar (n = 20 each), making a total of four groups (WOGnew, EOnew, WOGused, EOused). Used instruments were also photographed after instrumentation. The presence of fractures was registered. Preoperative and postoperative images were randomly ordered for evaluation. Two blinded calibrated examiners evaluated the presence of PD. Inter-observer agreement was calculated with the Kappa coefficient (K = 0.89). 3D profilometry was also used for the surface roughness analysis of six randomly selected instruments from the WOGused and EOused groups. Chi-square and two-way ANOVA tests were used to, respectively, compare PD and changes in surface roughness among the groups. No instruments fractured; however, a significantly greater percentage of EO instruments suffered plastic deformation than WOG instruments (p < 0.001), (OR = 11.09 (CI 95% 2.6–56.3)). The overall surface roughness was higher for most parameters in the EO instruments (p < 0.05). Single uses of EO instruments produced significantly higher chances of PD and increased surface roughness values compared to WOG. Full article

Spine radiographs in the standing position are the recommended standard for diagnosing idiopathic scoliosis. Though the deformity exists in 3D, its diagnosis is currently carried out with the help of 2D radiographs due to the unavailability of an efficient, low-cost 3D alternative. Computed tomography (CT) and magnetic resonance imaging (MRI) are not suitable in this case, as they are obtained in the supine position. Research on 3D modelling of scoliotic spine began with multiplanar radiographs and later moved on to biplanar radiographs and finally a single radiograph. Nonetheless, modern advances in diagnostic imaging have the potential to preserve image quality and decrease radiation exposure. They include the DIERS formetric scanner system, the EOS imaging system, and ultrasonography. This review article briefly explains the technology behind each of these methods. They are compared with the standard imaging techniques. The DIERS system and ultrasonography are radiation free but have limitations with respect to the quality of the 3D model obtained. There is a need for 3D imaging technology with less or zero radiation exposure and that can produce a quality 3D model for diseases like adolescent idiopathic scoliosis. Accurate 3D models are crucial in clinical practice for diagnosis, planning surgery, patient follow-up examinations, biomechanical applications, and computer-assisted surgery. Full article

Introduction: Artificial intelligence (AI) tools are increasingly being integrated into computer-aided diagnosis systems that can be applied to improve the recognition and clinical and molecular characterization of allergic diseases, including eosinophilic esophagitis (EoE). This review aims to systematically evaluate current applications of AI, machine learning (ML), and deep learning (DL) methods in EoE characterization and management. Methods: We conducted a systematic review using a registered protocol published in the International Prospective Register of Systematic Reviews (CRD42023451048). The risk of bias and applicability of eligible studies were assessed according to the prediction model study risk of bias assessment tool (PROBAST). We searched PubMed, Embase, and Web of Science to retrieve the articles. The literature review was performed in May 2023. We included original research articles (retrospective or prospective studies) published in English in peer-reviewed journals, studies whose participants were patients with EoE, and studies assessing the application of AI, ML, or DL models. Results: A total of 120 articles were found. After removing 68 duplicates, 52 articles were reviewed based on the title and abstract, and 34 were excluded. Eleven full texts were assessed for eligibility, met the inclusion criteria, and were analyzed for the systematic review. The AI models developed in three studies for identifying EoE based on endoscopic images showed high score performance with an accuracy that ranged from 0.92 to 0.97. Five studies developed AI models that histologically identified EoE with high accuracy (87% to 99%). We also found two studies where the AI model identified subgroups of patients according to their clinical and molecular features. Conclusions: AI technologies could promote more accurate evidence-based management of EoE by integrating the results of molecular signature, clinical, histology, and endoscopic features. However, the era of AI application in medicine is just beginning; therefore, further studies with model validation in the real-world environment are required. Full article