Search Results (212)

Background: Artificial intelligence (AI) and machine learning (ML) have been reshaping maternal, fetal, neonatal, and reproductive healthcare by enhancing risk prediction, diagnostic accuracy, and operational efficiency across the perinatal continuum. However, no comprehensive synthesis has yet been published. Objective: To conduct a scoping review of reviews of AI/ML applications spanning reproductive, prenatal, postpartum, neonatal, and early child-development care. Methods: We searched PubMed, Embase, the Cochrane Library, Web of Science, and Scopus through April 2025. Two reviewers independently screened records, extracted data, and assessed methodological quality using AMSTAR 2 for systematic reviews, ROBIS for bias assessment, SANRA for narrative reviews, and JBI guidance for scoping reviews. Results: Thirty-nine reviews met our inclusion criteria. In preconception and fertility treatment, convolutional neural network-based platforms can identify viable embryos and key sperm parameters with over 90 percent accuracy, and machine-learning models can personalize follicle-stimulating hormone regimens to boost mature oocyte yield while reducing overall medication use. Digital sexual-health chatbots have enhanced patient education, pre-exposure prophylaxis adherence, and safer sexual behaviors, although data-privacy safeguards and bias mitigation remain priorities. During pregnancy, advanced deep-learning models can segment fetal anatomy on ultrasound images with more than 90 percent overlap compared to expert annotations and can detect anomalies with sensitivity exceeding 93 percent. Predictive biometric tools can estimate gestational age within one week with accuracy and fetal weight within approximately 190 g. In the postpartum period, AI-driven decision-support systems and conversational agents can facilitate early screening for depression and can guide follow-up care. Wearable sensors enable remote monitoring of maternal blood pressure and heart rate to support timely clinical intervention. Within neonatal care, the Heart Rate Observation (HeRO) system has reduced mortality among very low-birth-weight infants by roughly 20 percent, and additional AI models can predict neonatal sepsis, retinopathy of prematurity, and necrotizing enterocolitis with area-under-the-curve values above 0.80. From an operational standpoint, automated ultrasound workflows deliver biometric measurements at about 14 milliseconds per frame, and dynamic scheduling in IVF laboratories lowers staff workload and per-cycle costs. Home-monitoring platforms for pregnant women are associated with 7–11 percent reductions in maternal mortality and preeclampsia incidence. Despite these advances, most evidence derives from retrospective, single-center studies with limited external validation. Low-resource settings, especially in Sub-Saharan Africa, remain under-represented, and few AI solutions are fully embedded in electronic health records. Conclusions: AI holds transformative promise for perinatal care but will require prospective multicenter validation, equity-centered design, robust governance, transparent fairness audits, and seamless electronic health record integration to translate these innovations into routine practice and improve maternal and neonatal outcomes. Full article

Healthcare data rapidly increases, and patients seek customized, effective healthcare services. Big data and machine learning (ML) enabled smart healthcare systems hold revolutionary potential. Unlike previous reviews that separately address AI or big data, this work synthesizes their convergence through real-world case studies, cross-domain ML applications, and a critical discussion on ethical integration in smart diagnostics. The review focuses on the role of big data analysis and ML towards better diagnosis, improved efficiency of operations, and individualized care for patients. It explores the principal challenges of data heterogeneity, privacy, computational complexity, and advanced methods such as federated learning (FL) and edge computing. Applications in real-world settings, such as disease prediction, medical imaging, drug discovery, and remote monitoring, illustrate how ML methods, such as deep learning (DL) and natural language processing (NLP), enhance clinical decision-making. A comparison of ML models highlights their value in dealing with large and heterogeneous healthcare datasets. In addition, the use of nascent technologies such as wearables and Internet of Medical Things (IoMT) is examined for their role in supporting real-time data-driven delivery of healthcare. The paper emphasizes the pragmatic application of intelligent systems by highlighting case studies that reflect up to 95% diagnostic accuracy and cost savings. The review ends with future directions that seek to develop scalable, ethical, and interpretable AI-powered healthcare systems. It bridges the gap between ML algorithms and smart diagnostics, offering critical perspectives for clinicians, data scientists, and policymakers. Full article

Digital images have been widely applied in fields such as mobile devices, the Internet of Things, and medical imaging. Although significant progress has been made in image encryption technology, it still faces many challenges, such as attackers using powerful computing resources and advanced algorithms to crack encryption systems. To address these challenges, this paper proposes a novel image encryption algorithm based on one-dimensional sawtooth wave chaotic system (1D-SAW) and the three-strand structure of DNA. Firstly, a new 1D-SAW chaotic system was designed. By introducing nonlinear terms and periodic disturbances, this system is capable of generating chaotic sequences with high randomness and initial value sensitivity. Secondly, a new diffusion rule based on the three-strand structure of DNA is proposed. Compared with the traditional DNA encoding and XOR operation, this rule further enhances the complexity and anti-attack ability of the encryption process. Finally, the security and randomness of the 1D-SAW and image encryption algorithms were verified through various tests. Results show that this method exhibits better performance in resisting statistical attacks and differential attacks. Full article

This paper presents the PRONOBIS project, an ultrasound-only, robotically assisted, deep learning-based system for prostate scanning and biopsy treatment planning. The proposed system addresses the challenges of precise prostate segmentation, reconstruction and inter-operator variability by performing fully automated prostate scanning, real-time CNN-transformer-based image processing, 3D prostate reconstruction, and biopsy needle position planning. Fully automated prostate scanning is achieved by using a robotic arm equipped with an ultrasound system. Real-time ultrasound image processing utilizes state-of-the-art deep learning algorithms with intelligent post-processing techniques for precise prostate segmentation. To create a high-quality prostate segmentation dataset, this paper proposes a deep learning-based medical annotation platform, MedAP. For precise segmentation of the entire prostate sweep, DAF3D and MicroSegNet models are evaluated, and additional image post-processing methods are proposed. Three-dimensional visualization and prostate reconstruction are performed by utilizing the segmentation results and robotic positional data, enabling robust, user-friendly biopsy treatment planning. The real-time sweep scanning and segmentation operate at 30 Hz, which enable complete scan in 15 to 20 s, depending on the size of the prostate. The system is evaluated on prostate phantoms by reconstructing the sweep and by performing dimensional analysis, which indicates 92% and 98% volumetric accuracy on the tested phantoms. Three-dimansional prostate reconstruction takes approximately 3 s and enables fast and detailed insight for precise biopsy needle position planning. Full article

Physics-informed neural networks (PINNs) have emerged as a transformative methodology integrating deep learning with scientific computing. This review establishes a three-dimensional analytical framework to systematically decode PINNs’ development through methodological innovation, theoretical breakthroughs, and cross-disciplinary convergence. The contributions include threefold: First, identifying the co-evolutionary path of algorithmic architectures from adaptive optimization (neural tangent kernel-guided weighting achieving 230% convergence acceleration in Navier-Stokes solutions) to hybrid numerical-deep learning integration (5× speedup via domain decomposition) and second, constructing bidirectional theory-application mappings where convergence analysis (operator approximation theory) and generalization guarantees (Bayesian-physical hybrid frameworks) directly inform engineering implementations, as validated by $72\%$ cost reduction compared to FEM in high-dimensional spaces ($p<0.01,n=15$ benchmarks). Third, pioneering cross-domain knowledge transfer through application-specific architectures: TFE-PINN for turbulent flows ($5.12\pm 0.87\%$ error in NASA hypersonic tests), ReconPINN for medical imaging ($▵\mathrm{SSIM}=+0.18\pm 0.04$ on multi-institutional MRI), and SeisPINN for seismic systems ($0.52\pm 0.18$ km localization accuracy). We further present a technological roadmap highlighting three critical directions for PINN 2.0: neuro-symbolic, federated physics learning, and quantum-accelerated optimization. This work provides methodological guidelines and theoretical foundations for next-generation scientific machine learning systems. Full article

U-shaped architectures have achieved exceptional performance in medical image segmentation. Their aim is to extract features by two symmetrical paths: an encoder and a decoder. We propose a lightweight U-Net incorporating reverse attention and a preprocessing framework for accurate retinal vessel segmentation. This concept could be of benefit to portable or embedded recognition systems with limited resources for real-time operation. Compared to the baseline model (7.7 M parameters), the proposed U-Net model has only 1.9 M parameters and was tested on the DRIVE (Digital Retinal Images for Vesselness Extraction), CHASE (Child Heart and Health Study in England), and HRF (High-Resolution Fundus) datasets for vesselness analysis. The proposed model achieved Dice coefficients and IoU scores of 0.7871 and 0.6318 on the DRIVE dataset, 0.8036 and 0.6910 on the CHASE-DB1 Retinal Vessel Reference dataset, as well as 0.6902 and 0.5270 on the HRF dataset, respectively. Notably, the integration of the reverse attention mechanism contributed to a more accurate delineation of thin and peripheral vessels, which are often undetected by conventional models. The model comprised 1.94 million parameters and 12.21 GFLOPs. Furthermore, during inference, the model achieved a frame rate average of 208 FPS and a latency of 4.81 ms. These findings support the applicability of the proposed model in real-world clinical and mobile healthcare environments where efficiency and Accuracy are essential. Full article

The increasing demand for accurate and accessible medical imaging has driven efforts to develop technologies that overcome limitations associated with conventional imaging techniques, such as MRI and CT scans. This study presents the design and implementation of an electronic interface for acquiring signals from a piezoelectric ultrasound sensor with the aim of improving image reconstruction quality by addressing electromagnetic interference and speckle noise, two major factors that degrade image fidelity. The proposed interface is installed between the ultrasound transducer and acquisition system, allowing real-time signal capture without altering the medical equipment’s operation. Using a printed circuit board with 110-pin connectors, signals from individual piezoelectric elements were analyzed using an oscilloscope. Results show that noise amplitudes occasionally exceed those of the acoustic echoes, potentially compromising image quality. By enabling direct observation of these signals, the interface facilitates the future development of analog filtering solutions to mitigate high-frequency noise before digital processing. This approach reduces reliance on computationally expensive digital filtering, offering a low-cost, real-time alternative. The findings underscore the potential of the interface to enhance diagnostic accuracy and support further innovation in medical imaging technologies. Full article

Rectal cancer management necessitates a rigorous multidisciplinary strategy, emphasizing precise staging and detailed risk stratification to inform optimal therapeutic decision-making. Obtaining an accurate histological diagnosis before initiating treatment is essential. Comprehensive staging integrates clinical evaluation, thorough medical history analysis, assessment of carcinoembryonic antigen (CEA) levels, and computed tomography (CT) imaging of the abdomen and thorax. High-resolution pelvic magnetic resonance imaging (MRI), utilizing dedicated rectal protocols, is critical for identifying recurrence risks and delineating precise anatomical relationships. Endoscopic ultrasound further refines staging accuracy by determining the tumor infiltration depth in early-stage cancers, while preoperative colonoscopy effectively identifies synchronous colorectal lesions. In early-stage rectal cancers (T1–T2, N0, and M0), radical surgical resection remains the standard of care, although transanal local excision can be selectively indicated for certain T1N0 tumors. In contrast, locally advanced rectal cancers (T3, T4, and N+) characterized by microsatellite stability or proficient mismatch repair are optimally managed with total neoadjuvant therapy (TNT), which combines chemoradiotherapy with oxaliplatin-based systemic chemotherapy. Additionally, tumors exhibiting high microsatellite instability or mismatch repair deficiency respond favorably to immune checkpoint inhibitors (ICIs). The evaluation of tumor response following neoadjuvant therapy, utilizing MRI and endoscopic assessments, facilitates individualized treatment planning, including non-operative approaches for patients with confirmed complete clinical responses who comply with rigorous follow-up. Recent advancements in molecular characterization, targeted therapies, and immunotherapy highlight a significant evolution towards personalized medicine. The effective integration of these innovations requires enhanced interdisciplinary collaboration to improve patient prognosis and quality of life. Full article

High Dynamic Range (HDR) imaging is capable of capturing vivid and lifelike visual effects, which are crucial for fields such as computer vision, photography, and medical imaging. However, real-time processing of HDR content remains challenging due to the computational complexity of tone mapping algorithms and the inherent limitations of Low Dynamic Range (LDR) capture systems. This paper presents an adaptive HDR tone mapping processor that achieves high computational efficiency and robust image quality under varying exposure conditions. By integrating an exposure-adaptive factor into a bilateral filtering framework, we dynamically optimize parameters to achieve consistent performance across fluctuating illumination conditions. Further, we introduce a high-radix Taylor expansion technique to accelerate floating-point logarithmic and exponential operations, significantly reducing resource overhead while maintaining precision. The proposed architecture, implemented on a Xilinx XCVU9P FPGA, operates at 250 MHz and processes 4K video at 30 frames per second (FPS), outperforming state-of-the-art designs in both throughput and hardware efficiency. Experimental results demonstrate superior image fidelity with an average Tone Mapping Quality Index (TMQI): 0.9314 and 43% fewer logic resources compared to existing solutions, enabling real-time HDR processing for high-resolution applications. Full article

Swept-source optical coherence tomography (SS-OCT) is a widely used imaging technique, particularly in medical diagnostics, due to its ability to provide high-resolution cross-sectional images. However, one of the main challenges in SS-OCT systems is the nonlinearity in wavelength sweeping, which leads to degraded depth resolution after Fourier transform. Correcting for this nonlinearity typically requires complex re-sampling and chirp compensation methods. In this paper, we introduce the first ultrafast time-stretch optical coherence tomography (TS-OCT) system that utilizes reservoir computing (RC) to perform direct temporal signal analysis without relying on Fourier transform techniques. By focusing solely on the temporal characteristics of the interference signal, regardless of frequency chirp, we demonstrate a more efficient solution to address the nonlinear wavelength sweeping issue. By leveraging the dynamic temporal processing capabilities of RC, the proposed system effectively bypasses the challenges faced by Fourier analysis, maintaining high-resolution depth measurement without being affected by chirp-introduced spectral broadening. The system operates by categorizing the interference signals generated by variations in sample position. This classification-based approach simplifies the data processing pipeline. We developed an RC-based model to interpret the temporal patterns in the interferometric signals, achieving high classification accuracy. A proof-of-the-concept experiment demonstrated that this method allows for precise depth resolution, independent of system chirp. With an A-scan rate of 50 MHz, the classification model yielded 100% accuracy with a root mean square error (RMSE) of 0.2416. This approach offers a robust alternative to Fourier-based analysis, particularly in systems prone to nonlinearities during signal acquisition. Full article

This paper comprehensively examines symmetric difference operators within logical systems generated by nilpotent t-norms and t-conorms, specifically addressing their behavior and applicability in bounded and Łukasiewicz fuzzy logic systems. We identify two distinct symmetric difference operators and analyze their fundamental properties, revealing their inherent non-associativity. Recognizing the limitations posed by non-associative behavior in practical multi-step logical operations, we introduce a novel aggregated symmetric difference operator constructed through the arithmetic mean of the previously defined operators. The primary theoretical contribution of our research is establishing the associativity of this new aggregated operator, significantly enhancing its effectiveness for consistent multi-stage computations. Moreover, this operator retains critical properties including symmetry, neutrality, antitonicity, and invariance under negation, thus making it particularly valuable for various computational and applied domains such as image processing, pattern recognition, fuzzy neural networks, cryptographic schemes, and medical data analysis. The demonstrated theoretical robustness and practical versatility of our associative operator provide a clear improvement over existing methodologies, laying a solid foundation for future research in fuzzy logic and interdisciplinary applications. Our broader aim is to derive and study symmetric difference operators in both bounded and Łukasiewicz systems, as this represents a new direction of research. Full article

Identifying parathyroid glands during surgery is challenging and time-consuming due to their small size (3–5 mm) and camouflaged appearance in the background of the thyroid, lymph nodes, fat, and other neck structures. For the gland itself, it is also important to differentiate abnormal ones from normal ones. Accidental damage or removal of the normal glands can result in complications like hypocalcemia, which may necessitate lifelong medication dependence, and, in extreme cases, lead to death. The study of autofluorescence optical properties of normal and abnormal parathyroid glands and the surrounding tissue will be helpful for developing non-invasive detection devices. The near-infrared (NIR) autofluorescence characteristics of parathyroid and thyroid tissues have been studied extensively and are now used for parathyroid gland detection during surgery. Additionally, there have been a few reports on the UV-visible light-excited autofluorescence characteristics of these tissues with a focus on spectroscopy. However, there is a lack of high-resolution, side-by-side autofluorescence imaging comparisons of both tissue types under various excitation wavelengths, ranging from visible to NIR. We developed a standalone tabletop autofluorescence imaging system to acquire images of ex vivo specimens in the operating room under different excitation wavelengths: visible 405 nm, 454 nm, 520 nm, 628 nm, and NIR 780 nm. Autofluorescence imaging features of parathyroid adenomas for each excitation wavelength were described and compared. It was found that visible light excites much stronger autofluorescence from parathyroid adenoma tissue compared to NIR light. However, NIR excitation provides the best intensity difference/contrast between parathyroid adenoma and thyroid tissue, making it optimal for differentiating these two tissue types, and detecting parathyroid adenoma during surgery. The high fluorescent site under the NIR 780 nm excitation also generates high fluorescence under visible excitation wavelengths. Heterogeneous fluorescence patterns were observed in most of the parathyroid adenoma cases across all the excitation wavelengths. Full article

We propose a method to reconstruct a 3D coordinate system and a vascular map for the operation of magnetic medical robots (MMRs) controlled by a magnetic navigation system (MNS) using two 2D X-ray images and four corners of an MNS. Utilizing the proposed method, we calculated the relative rotation angle of a C-arm considering its rotational precision error. We derived the position information and 3D coordinate system of an MNS workspace in which the magnetic fields are generated and controlled by an MNS. The proposed method can also be utilized to reconstruct vascular maps. Reconstructed vascular maps are in the 3D coordinate system of the C-arm and can be transformed into the 3D coordinate system of an MNS workspace to generate the magnetic flux density with the desired direction and magnitude at the position of the MMR. The proposed method allows us to remotely and precisely control the MMR inserted into the vessel by controlling the external magnetic field. The proposed method was validated through in vitro experiments with an MNS mock-up and a vascular jig. Finally, the proposed method was applied to in vivo experiments where the MMR was inserted into the superficial femoral artery of a mini pig to remotely control the motion of the MMR. This research will enable precise and effective control of MMRs in various medical procedures utilizing an MNS. 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

Imaging technologies are constantly being developed to improve not only melanoma diagnosis, but also staging, treatment planning, and disease progression. We start with a description of how melanoma is characterized using histology, and then continue by discussing nearly two dozen different technologies, including systems currently used in medical practice and those in development. For each technology, we describe its method of operation, how it is or would be projected to be most commonly used in diagnosing and managing melanoma, and for systems in current use, we identify at least one current manufacturer. We also provide a table including the biomarkers identified by and main limitations associated with each technology and conclude by offering suggestions on specific characteristics that might best enhance a technology’s potential for widespread clinical adoption. Full article