搜索结果：Journal of medical physics

The human biofield serves as an indicator of an individual's physical and emotional health status. Biofield-based therapeutic techniques, also known as complementary and alternative medicine (CAM) techniques such as Reiki, Therapeutic Touch, and Pranic Healing, leverage this information in the preliminary assessment phase before treatment initiation. These modalities are increasingly integrated as complementary methods within health diagnostic frameworks. Among the techniques employed for biofield visualization, gas discharge visualization (GDV) and polycontrast interference photography (PIP) are the predominant imaging methodologies. Notably, the majority of scientific investigations and empirical studies have primarily utilized GDV-derived images, with comparatively fewer studies focusing on PIP-based data. The primary objective of this study is to identify energy imbalances within the pancreatic region using biofield imaging and to utilize these patterns for classifying subjects as diabetic or nondiabetic. This work emphasizes the relevance of biofield information in health assessment and evaluates its potential for supporting energy-based diagnostic approaches. Color-based clustering methods were applied for segmentation. A transfer learning-based ensemble framework was developed using pretrained convolutional neural network (CNN) architectures ConvNeXtBase and ResNet50 to classify biofield images into diabetic and nondiabetic categories. Grid search optimization identified the optimal hyperparameters, which were applied during fine-tuning to improve feature learning. Ensemble model was evaluated, with the ConvNeXtBase + ResNet50 combination achieving the highest accuracy of 99.12%. Robust performance validation was ensured using 5-fold cross-validation to minimize sampling bias and enhance generalization. The ensemble of ResNet50 and ConvNeXtBase achieved the highest accuracy of 99.12%, outperforming individual models (ConvNeXtBase: 97.93% and ResNet50: 96.28%). Receiver operating characteristic analysis confirmed strong reliability with area under the curve values above 0.99 for both classes (diabetic and nondiabetic). The 5-fold cross-validation analysis further demonstrated the robustness of the proposed ensemble model, achieving a mean accuracy of 97.45%, indicating highly consistent performance across different dataset partitions. The CNN-based models can be trained to classify the biofield images, and this approach can enable automated analysis of biofield images. The approach of using clustering, deep learning, and ensemble modeling as analyzed and described in this study seems to be highly effective. The overall system of biofield imaging and automated clustering can act as a potential noninvasive diagnostic support tool, though further testing with larger datasets and expert validation is necessary for clinical application.

T2* Magnetic Resonance Imaging (MRI) is an established method for the assessment of iron overload in multiple organs. Quantification of iron in the soft tissues of the fingers with T2* MRI could be of clinical value in conditions characterized by microvasculopathy. Herein, we examined the feasibility and repeatability of quantitative T2* MRI for the presence of iron in the soft tissues of fingers. In this prospective cohort study, 10 healthy individuals and three patients with digital microvasculopathy were subjected to hand MRI. Gradient echo (GRE) images with progressively increasing echo times (TE's) were acquired in a 1.5 T MRI system. After visual inspection of dual-colored maps of the hands, multiple regions of interest (ROIs) were placed in the soft tissues at areas that indicated increased susceptibility. Three ROIs per finger with the lowest T2* values (ms) were recorded. Mean T2* values for healthy individuals ranged from 12.5 to 16.0 ms (mean ± standard deviation [SD]: 14.0 ± 2.6 ms) except for a single ROI measurement of 8.8 ms in one individual. In contrast, multiple ROIs with T2* values below 8.8 ms were detected in the soft tissues of the three patients with digital microvasculopathy. The range of values for the three patients were (mean ± SD [range]: 7.4 ± 0.9 [5.2-8.8] ms, 7.9 ± 1.0 [5.9-10.3] ms, and 7.9 ± 1.7 [5.7-14.3] ms, respectively), suggestive of iron excess. Furthermore, 4/10 healthy individuals repeated the examination 1 year after the first MRI study. No areas with T2* below 8.8 ms were detected in the second study. In conclusion, T2* GRE is a feasible and reproducible technique for the quantification of iron in the soft tissues of the fingers.

BackgroundQuality control (QC) in mammography is crucial for breast imaging and it relies predominantly on subjective assessment of image quality (IQ) in phantom.PurposeTo validate the efficacy of automated software assessment in comparison with human observers.Material and MethodsA total of 80 processed images of the ACR DM phantom were collected from mammography systems supplied by five different vendors. IQ was assessed using in-house developed software and 11 human observers (five experts and six non-experts). Three different target objects of various sizes and shapes were scored (six fibers, six speck groups, six masses) for each image.ResultsThe software assessment demonstrated good to moderate agreement with the human observers' scoring of target objects, especially with expert observers. The intraclass correlation coefficient (ICC) values between the software and all observers were 0.77, 0.61, and 0.78 for fibers, speck groups, and masses, respectively. There was variability in the scoring of low contrast objects, especially with non-expert observers. Meanwhile, high contrast objects (e.g. specks) showed the highest visibility rate and received more consistent scores. The comparison between the software and all observers indicated mean differences of 0.53 for fibers, 0.27 for specks, and 0.36 for masses.ConclusionThe software assessment effectively scored the phantom IQ, demonstrating comparability to assessments made by human observers, regardless of the image acquisition factors and manufacturers' design specifications. This software should therefore ensure consistent IQ assessment that mitigates the limitations of subjective human assessment in mammography quality testing.

We developed an innovative, active learning workshop to introduce foundations of point-of-care ultrasound (POCUS) physics and instrumentation. The workshop design aimed to reduce cognitive load and encourage learner-centered engagement through hands-on exploration that reinforces key principles and connects theory to practice. First-year medical students participated in small-group, student-driven exercises to explore POCUS machine controls, transducers, and image acquisition skills. Each group was supported by a faculty member to guide activities and ensure accuracy. Students scanned pickles, hard-boiled eggs, and olives with pits; these items were selected for their low cost, sonographic characteristics, and recognizability. Later in the semester, students demonstrated image optimization skills as part of a formal assessment. Survey results suggest that the workshop enhanced understanding of ultrasound physics and its relationship to imaging acquisition. Of 231 students assessed, 97% (n = 223) were able to select the correct probe and 71% (n = 164) successfully optimized their images independently during the subsequent assessment. This interactive workshop provided learners with hands-on experience using familiar household objects to demonstrate the relationship between ultrasound physics, instrumentation, and image generation. Instruction emphasized principles of image formation rather than image interpretation, distinguishing it from patient-based workshops. This approach aims to promote more deliberate image acquisition throughout the curriculum and into clinical practice.

A surface-enhanced Raman scattering (SERS) sensor built on a free-standing Au/Cu-covalent organic framework (COF) membrane was developed for the noninvasive Helicobacter pylori detection by quantifying trace ammonia in exhaled breath. The porphyrin-based Cu-COF acts as a multifunctional sensing interface, using its porous structure and accessible porphyrin-Cu coordination sites to efficiently capture and preconcentrate ammonia vapor. The change of intensity of the characteristic SERS peak at the 391 cm-1 Raman peak (ΔI391) shows a good linear relationship with ammonia concentration in the range of 0.5 to 3 ppm, with a limit of detection (LOD) down to 0.5 ppm. The sensor shows good stability and anti-interference ability toward common volatile organic compounds (VOCs) and inorganic gases in breath, allowing rapid detection without complex sample pretreatment. Notably, the introduction of Cu2+ significantly enhanced the crystallinity and structure ordering of the COF membrane, offering a new strategy for metal-ion-mediated structural regulation of 2D COF films. Preliminary clinical blind testing on exhaled breath samples yields a 70% diagnostic accuracy for H. pylori infection, validating the practical potential of this sensor. This work establishes a robust SERS platform for breath biomarker analysis and provides a promising route for noninvasive, real-time, and point-of-care diagnosis of H. pylori infection.

The pace of surgical innovation appears ever faster. Innovation is being freed from the design constraints of the opposable digits of a surgeon's hand through the use of programmable binary digits. Surgeons must be the drivers of change and central to the application of innovations. We should collaborate with industry, engineers and scientists to think out of the box but must consider also expense, environmental impact, equity, and ethics. But we should not be blinded by shiny technology: innovation without impact is mere noise. The ultimate considerations are the diagnosis and management of surgical disease, of improving the care of our patients. Expert surgeons, scientists and engineers across the world were identified and invited to describe areas of innovation within surgery. They were given free rein to review their areas of expertise and to discuss both current and future applications of technology within surgical care. The Commission spans multiple surgical specialties and scientific domains. It reviews translational genomics, including the role of ctDNA, alongside microbiomic and proteomic applications in improving the diagnosis, treatment and monitoring of surgical disease. Applications to enhance surgical procedures are described, from medical micro/nanorobots for minimally invasive interventions, sensory-enriched surgery with visual optimization and molecular image-guidance to intelligent and semiautomated instruments. The expansion and broad influence of artificial intelligence in surgical writing, training and simulation, diagnosis and robotics is widely described. The role of surgical innovation and technology in driving personalized care for benign and malignant surgical disease from genomic profiling to bespoke surgical and non-surgical treatment pathways and surveillance is considered. The future of surgery is poised to become more precise, personalized, and effective. Collaboration with engineers, data scientists, and industry partners not only represents an exciting opportunity for surgeons to participate in team science but is critical to focus innovation goals on optimizing patient care and outcomes.

Accurate sound localization relies on the transformation of binaural cues into stable spatial representations, yet the neural mechanisms supporting this process remain incompletely understood. Mild stroke provides a unique opportunity to study the vulnerability of auditory spatial processing within distributed neural networks. We investigated azimuthal sound localization in chronic-phase survivors of mild stroke without hearing aid use using broadband, low-frequency, and high-frequency noise and compared it against a headphone-based lateralization task. Most patients exhibited localization accuracy comparable to healthy listeners. However, 4 of 14 patients showed a striking alteration of auditory spatial perception: instead of a continuous mapping of azimuth, responses clustered into bi- or trimodal (left-center-right) categorical patterns. Comparable deficits were also observed in one of the five age-matched control participants. To our knowledge, such localization patterns have not been reported in listeners without neurological disease. Atypical localization was most pronounced with low-frequency stimuli, suggesting a different role of the respective binaural cues in forming a spatial representation of sound. Differences between loudspeaker-based localization and existing headphone-based lateralization data further suggest that these paradigms engage distinct auditory spatial representations. The findings support the view that auditory space is constructed through higher-order, supramodal spatial mechanisms that are particularly vulnerable to right-hemispheric damage. Overall, the data highlight the challenges of quantifying spatial hearing of stroke survivors both on an individual and on a population level. Challenges include previously undocumented stimulus and method dependencies, an unknown mixing of auditory and neurological factors, and the absence of normative data for a nonstroke control cohort.

Dosimetric comparison of conical collimator (CC) supplied by Varian Medical System on a TrueBeam (TB) for 6MV-flattening filter-free (FFF) beams versus CC on CyberKnife-G4 (CK) by Accuray Inc. 5 cones with nominal diameters of 5 mm, 7.5 mm, 10 mm, 12.5 mm, and 15 mm were considered in our study. Percentage depth dose (PDD), off-axis ratio (OAR), tissue maximum ratio (TMR), and output factor (OF) were presented and compared. PDD comparisons between TB and CK cones show good agreement across the range of cones; the mean difference of the % dose values, for all cones, was - 0.9% ±1.2% across the five considered depths along the curve. The agreement between CK and TB cones is poorer for TMR; the discrepancies between CK and TB values increase with depth and lightly decrease with increased cone size. OAR profiles are in agreement, although CK cones tend to overestimate the dose between 80% and 5% dose; consequently, the FHWM (full width at half maximum) for CK cones is slightly larger. Except for the 5-mm cone with a difference percentage of -3.7% between CK and Varian cones, CK cones show the largest output factors, with a maximum difference percentage was 1.9% for the 7.5-mm cone. CK and TB cones show similar dosimetric characteristics. The observed differences suggest that the 6MV-FFF beams from TB cones would be slightly "softer" than the 6MV-FFF beams from CK cones; Varian cones may potentially provide better sparing of organs at risk.

Partially as a result of hypoxia-induced radioresistance, rates of treatment failure for head-and-neck cancer patients receiving radiotherapy can be considerable. Clinical trials utilizing positron emission tomography (PET) to image tumor hypoxia and escalate the prescription dose in hypoxic sub-volumes are being pursued in response, with current clinical prescription doses of 70 Gy generally escalated to 77-78 Gy. Instead utilizing magnetic resonance imaging (MRI) for hypoxia-based prescription dose escalation would be associated with a variety of advantages, including not requiring an additional imaging-related radiation dose to be delivered to the patient and allowing for a variety of other functional maps to be extracted from the same patient imaging session, in addition to tumor hypoxia information. The purpose of this study is to investigate the benefits of MRI-informed hypoxia-based radiotherapy dose escalation for head-and-neck cancer patients treated with proton radiotherapy. Ten patients with head-and-neck cancer scheduled to undergo photon therapy underwent a multi-parametric MRI protocol based on which tumor hypoxia maps were computed for every patient using a quantitative blood oxygenation level dependent (BOLD) approach. Four proton therapy treatment plans were then created for each patient, consisting of intensity-modulated proton therapy (IMPT) and proton arc therapy (PAT) treatment planning performed according to current clinical standards (IMPTConv and PATConv) or with a 10% prescription dose escalation to the hypoxic sub-volumes of the low- and high-risk target structures (IMPTEsc and PATEsc). The generated treatment plans were then analyzed with respect to target and organ-at-risk (OAR) doses and normal tissue complication probabilities (NTCPs) as well as tumor control probabilities (TCPs) calculated according to conventional models (TCPConv) or with consideration of hypoxia-induced radioresistance (TCPHyp). Statistical significance (p < 0.05) of different TCP or mean OAR dose distributions was determined using the Wilcoxon signed-rank test. During IMPT, radiotherapy prescription dose escalation increased TCPConv in the nominal scenario by (5.9 &#xb1; 6.3) percentage points (pp) in the normoxic (p < 0.001) and (5.2 &#xb1; 9.0) pp in the hypoxic target volumes (p = 0.006). In the worst-case scenario, TCPConv was increased by (5.6 &#xb1; 4.5) pp (p < 0.001) and (5.3 &#xb1; 6.4) pp (p = 0.003). Dose escalation during PAT improved TCPConv by (3.1 &#xb1; 3.3) pp (p < 0.001) and (2.1 &#xb1; 5.4) pp (p = 0.015) in the nominal scenario and (3.3 &#xb1; 3.1) pp (p < 0.001) and (3.3 &#xb1; 4.4) pp (p < 0.001) in the worst-case scenario. When hypoxia-induced radioresistance was considered, dose escalation elevated TCPHyp in the nominal scenario by (7.6 &#xb1; 4.5) pp (p&#xa0;< 0.001) during IMPT and (6.4 &#xb1; 4.1) pp (p < 0.001) during PAT and TCPHyp in the worst-case scenario by (6.3 &#xb1; 3.8) pp (p < 0.001) during IMPT and (6.3 &#xb1; 3.1) pp (p < 0.001) during PAT. Compared to the patients' clinical photon therapy treatment plans in the nominal scenario, mean OAR doses were reduced by (13.5 &#xb1; 9.3)Gy RBE by IMPTConv, (14.3 &#xb1; 10.5)Gy RBE by PATConv, (9.8 &#xb1; 10.5)Gy RBE by IMPTEsc, and (10.4 &#xb1; 12.8)Gy RBE by PATEsc (all p = 0.002). MRI-based hypoxia-informed radiotherapy prescription dose escalation during both IMPT and PAT significantly increased calculated TCPs while significantly reducing doses delivered to nearby healthy organs compared to the patients' clinical photon therapy treatment plans. MRI-based hypoxia-informed prescription dose escalation is therefore considered feasible and may help partially address hypoxia-induced radioresistance.

Multiple Sclerosis (MS) is a chronic neurological disorder, prevalent in young adults. MS leads in disability accrual, thus affecting overall patients' quality of life. Moreover, the management of MS poses significant burden on health systems worldwide. The present study delves into the impact of different health providing settings (e.g. private office/Clinic vs. specialized MS Center) on the effectiveness of MS management, as well as on patient-reported outcomes related to the quality of life (Axis A). Moreover, the study addresses their relative effectiveness in a health crisis, such as the COVID-19 pandemic (Axis B). Data were collected based on questionnaires administered to people with MS (pwMS). Upon the pandemic and prior to the COVID-19 vaccines being available, all data were collected via online questionnaires. Since March 2021, data were collected both online and in person. Overall, 776 pwMS participated in the study and answered Axis B questionnaire. Of those, 215 additionally answered Axis A questionnaire. Regarding Axis A, disease management by a specialized MS Center was associated with increased access to healthcare professionals (p < 0.001) and/or MRI examinations (p < 0.001) and was also linked to improved time-to-diagnosis following symptom onset, compared to the disease management in a private office/Clinic (p < 0.001). Regarding Axis B, specialized MS centers demonstrated remarkable adaptability during the pandemic, swiftly implementing remote care solutions to ensure continuity of care. These findings suggest that care delivered in specialized MS centers is associated with improved access to healthcare services and better patient-reported outcomes, both under routine care conditions and during healthcare crises.

The use of proton beam therapy (PBT), as a more precision-targeted radiotherapy technique, is increasing in the treatment of head and neck squamous cell carcinoma (HNSCC). PBT benefits from the precise delivery of the radiation dose to the tumour via the Bragg peak. However, challenges still remain in the treatment of HNSCC with radiotherapy, particularly with tumour radioresistance and recurrence, requiring strategies leading to radiosensitisation. There are added complexities with the use of PBT given the increase in linear energy transfer (LET) at and around the Bragg peak, which can cause an altered cellular response compared to low-LET radiation. Nevertheless, targeting the cellular DNA damage response is considered an important strategy to enhance tumour cell killing caused by radiotherapy. Therefore, using specific inhibitors against the protein kinases ataxia telangiectasia mutated (ATM), ataxia telangiectasia and Rad3-related (ATR) and the DNA-dependent protein kinase catalytic subunit (DNA-Pkcs), we investigated their impact in radiosensitising HPV-negative HNSCC cells to PBT of increasing LET. We demonstrate that inhibitors against ATR (AZD6738), and particularly ATM (AZD1390) and DNA-Pkcs (AZD7648), could significantly decrease clonogenic survival of HNSCC cell lines following PBT at both low and relatively high LET (~2 keV/&#xb5;m and ~8 keV/&#xb5;m, respectively). We confirmed that the inhibitors in combination with PBT led to DSB persistence through neutral comet assays and monitoring &#x3b3;H2AX/53BP1 foci. We also show that this strategy can enhance the sensitivity of patient-derived organoids of HNSCC to PBT of both low and high LET, highlighting this as a strategy which should be exploited further.

Objective.This work provides proof-of-concept for the use of real-time ion imaging and treatment gating for lung cancer radiotherapy using 3D range modulators (3DRM).Approach.The accuracy of a fully real-time, plastic scintillator-based portal ion radiography detector was determined by tracking a 3 cm spherical plastic tumour undergoing breathing-style motion in a mock lung geometry. The ability to gate the delivered treatment using a trigger from the ion radiography detector at the desired tumour position was investigated. Finally, an offline simulated study was performed to compare the dosimetric benefits of the advanced image guidance against the standard clinical motion mitigation practice of rescanning.Main Results.The ion radiography detector was shown to track tumour motion in a mock lung phantom setup to 0.1 mm accuracy. The imaging dose was found to be approximately 1/3 of comparable x-ray fluoroscopy methods. While dynamically driving in the 3DRM is not currently possible, preliminary measurements showed modulator positioning to be reproducible to 0.3%. A technical demonstration was provided showing a real-time switch from imaging to treatment using a trigger from the ion-imaging detector, with the mock tumour found in the expected position. The simulated dosimetric study showed that the tracking accuracy in an idealised scenario allows the tumour to be treated quasi-statically, with aD95%of 98% and 99.2% using passive and active treatment beam energy switching respectively.Significance.Lung Ion-fluoroscopy Guided Hadron Therapy (LIGHT) provides a promising approach for (a) mitigating tumour motion using a real-time, in-plane ion imaging device and (b) avoiding interplay effects with scanned beams by using a patient-specific 3D-range modulator to passively scatter a mono-energetic treatment beam. This technique also shows promise for future FLASH treatment delivery methods. Future work will fully determine the tracking performance and dosimetric benefits of LIGHT in more realistic clinical scenarios.

Phantoms are crucial for accurate assessment of new techniques and for simulating clinical conditions safely while reducing the risk to patients. The study explores the efficacy of different tissue equivalent compositions to mimic actual human tissue for their applications in the medical field. The material composition nomenclatures as polymethylmethacrylate (PMMA), solid water, SOT, ST, and resin have been employed by researchers for the study of tissue equivalency through different approaches. The computations to evaluate the useful radiological parameters, i.e., mass energy absorption coefficient, computed tomography number, gamma constant, dose rate, effective electron density for gamma rays and stopping power for charged particle ion beams have been done using established computer codes (XCOM [Photon Cross Sections Database] and SRIM/TRIM [Stopping and Range of Ions in Matter]) as well as through newly developed packages (Phy-X/photon shielding and dosimetry [PSD] and Proton, Alpha, Gamma, Electron and X-radiation interaction parameters [PAGEX]). It is concluded that solid water is the best tissue equivalent phantom material for gamma rays and electrons, while compositions, namely PMMA and ST, are found to be the best tissue equivalents for proton- and alpha particles respectively. The comparison of results obtained from recently developed computer codes (PAGEX and Phy-X/PSD) with established databases confirm their reliability for analysing the phantom materials through a user-friendly approach. Maximum relative difference of Phy-X/PSD versus XCOM results is found to be <1% for gamma rays (0.02-18 MeV), and for protons (0.05-10 MeV), this difference is ~ 2% among results of PAGEX and SRIM/TRIM. Results would be useful for gamma rays and ion beam-based therapies as well as for the development of new phantom compositions.

In patients with repaired tetralogy of Fallot (rTOF), right ventricular (RV) fibrosis has been associated with ventricular tachycardia, heart failure, and sudden cardiac death. Here, we aimed to identify links between diffuse myocardial fibrosis and regional mechanical dysfunction by cardiac magnetic resonance imaging (CMR) in rTOF. Clinical CMR was extended in 17 consecutive rTOF patients (median age 20 years, 4 females) to include biventricular native T1 (nT1) mapping as indicator of diffuse fibrosis, and tissue phase mapping (TPM) for quantification of biventricular segmental, directional systolic/diastolic myocardial velocities. Patients demonstrated smaller peak velocities than healthy volunteers in both ventricles (e.g. LV-base circumferential peak-systolic velocity: median [IQR]&#x2009;-0.9 [1.5] vs. -3.0 [1.2] cm/s, p&#x2009;<&#x2009;0.001). RV peak velocities were reduced even in patients with normal RV ejection fraction (e.g. RV-mid long-axis peak-diastolic velocity: -3.3 [1.2] vs. -6.9 [1.8] cm/s, p&#x2009;=&#x2009;0.001). Global LV nT1 was higher in patients versus controls (1029 [48] vs. 993 [28] ms, p&#x2009;=&#x2009;0.013). Within the rTOF group, fibrosis in lateral LV correlated with shorter time-to-peak-velocity (TTP), whereas fibrosis in inferior RV correlated with longer TTP. Therefore, in rTOF TPM facilitates early diagnosis of impaired systolic and diastolic myocardial function while diffuse fibrosis has differential effects on RV and LV mechanics.

Dopamine (DA) is a critical neurotransmitter whose dysregulation is closely associated with Parkinson's disease; however, its reliable detection in blood remains challenging because of its extremely low concentration and severe matrix interference. Herein, we report a highly sensitive electrochemical sensor based on a hybrid nanocomposite consisting of carbon nitride quantum dot-supported Ru nanoparticle-decorated carbon nanotubes (Ru-CNQD/CNT) integrated with hollow carbon spheres (HCS) for DA detection. Owing to the synergistic combination of the high activity of Ru-CNQD/CNT and the favorable enrichment capability of HCS, the proposed sensor exhibits excellent analytical performance, featuring a wide linear response range from 0.001 to 50.0&#x202f;&#x3bc;M and a low detection limit of 0.13&#x202f;nM. The electrochemical platform was further applied to monitor DA release from PC12&#x202f;cells under high-potassium stimulation, enabling dynamic electrochemical tracking of neurotransmitter secretion. Importantly, quantitative analysis of DA in human serum samples demonstrates that the sensor can effectively differentiate Parkinson's disease patients from healthy individuals. Overall, this work establishes an efficient and ultrasensitive electrochemical strategy for DA detection in complex biological matrices, showing strong potential for neurotransmitter monitoring, DA-related disease assessment, and fundamental neurochemical research.

This review synthesizes current literature on the use of artificial intelligence (AI) to predict knee biomechanics during walking in people with knee osteoarthritis (OA). Four databases were searched from inception to 22/01/2025. Risk of bias was assessed using a modified Newcastle-Ottawa Scale. Study quality was assessed using Grading of Recommendations, Assessment, Development, and Evaluations. Gait spatiotemporal parameters, knee kinematics, knee kinetics, and knee internal biomechanics calculated with both AI and physics-based methods were compared using root mean squared error (RMSE), normalized RMSE (NRMSE), mean absolute error (MAE) with standard deviation (SD), or correlation coefficients (R2), and pooled for reporting. Of 883 studies screened, 8 were included for review, and four provided appropriate data for meta-analysis. Studies ranged from very low to high quality. Limited data were available for spatiotemporal parameters, with few studies including direct physics-based comparators. AI-predicted knee flexion time-series had RMSE ranging from 8.39&#xa0;&#xb1;&#xa0;4.13&#xb0; to 8.81&#xa0;&#xb1;&#xa0;4.25&#xb0; across the gait cycle. Meta-analysis found AI-predicted peak knee adduction moment was highly correlated with its physics-based counterpart (R2: 0.86 and 0.60) with moderate errors (MAE: 0.37 and 0.45) and mean differences 0.03%BW*Ht [95% CI: -0.08 to 0.14]). AI-predicted peak knee contact forces (medial, lateral, and total) had correlations ranging from R2&#xa0;=&#xa0;0.17 to 0.92, and NRMSE varied between 0.21 (0.01) and 0.70 (0.05) relative to physics-based values. Overall, AI approaches have potential to predict specific knee biomechanics, but refinement and validation are needed to improve prediction accuracy across all knee biomechanical variables.

Magnetic resonance spectroscopic imaging (MRSI) provides spatially resolved metabolic information, but its clinical use has been limited by prohibitively long acquisition times. Parallel imaging methods such as GRAPPA have been applied to accelerate MRSI, though conventional implementations often reconstruct each time point independently and fail to exploit spectral correlations across free induction decays. In this work, we present a feasibility study of autocalibration region extending through time (ARTT) GRAPPA, a reconstruction approach that extends the autocalibration region through time (k-t space) to improve coil weight estimation by incorporating both spatial and spectral correlations. Data were acquired at 7&#x2009;Tesla using a two-dimensional FID-MRSI sequence with a 24-channel array. A fully sampled dataset was retrospectively undersampled to simulate varying acceleration patterns, and reconstructions with ARTT GRAPPA were compared with conventional GRAPPA. A homogeneous phantom dataset was also evaluated to assess performance in the absence of spatial-spectral variability. Finally, prospectively undersampled acquisitions were performed in three healthy volunteers with effective acceleration factors of 2.50 and 3.58. ARTT GRAPPA enabled accurate metabolite reconstructions at acceleration factors exceeding 3.5, reducing scan times from over an hour to less than 20&#x2009;min while maintaining normalized errors below 10% for key metabolites. Compared with the conventional method, ARTT GRAPPA consistently achieved more than twice the acceleration at equivalent accuracy. Phantom experiments confirmed that the advantage of ARTT GRAPPA arises from exploiting spatial-spectral correlations rather than intrinsic algorithmic changes. Prospectively undersampled in&#xa0;vivo data further demonstrated feasibility, producing reliable metabolite maps at substantially reduced acquisition times. These findings establish ARTT GRAPPA as a proof-of-principle approach that leverages k-t correlations for practical acceleration of MRSI, suggesting a path toward improved metabolic imaging in clinical research applications.

Persistent infection with high-risk human papillomavirus (HPV) is a major cause of cervical cancer, and improved point-of-care (POC) detection is critical for early intervention. Although PCR-based assays are highly sensitive, their reliance on centralized laboratory infrastructure limits accessibility in decentralized settings. CRISPR-Cas diagnostics combined with lateral flow assays (LFA) offer a rapid alternative; however, visual interpretation of faint test bands remains subjective and variable. Here, we developed a smartphone-based CRISPR-Cas12a LFA platform integrated with an interpretable machine learning (ML) framework for quantitative detection of circulating HPV DNA in plasma. Standardized image acquisition was implemented using a light-controlled enclosure, and radiomics-inspired features were analyzed using a multivariable logistic regression model. The system was trained on 150 plasma samples and validated in an independent cohort of 60 samples. The optimized model achieved 96.7% sensitivity and 100% specificity, outperforming visual interpretation, particularly for low-signal samples. Performance remained stable across different smartphone models, lighting conditions, and operators, with rapid on-device inference enabling consistent and reliable operation. This integrated CRISPR-LFA platform demonstrates accurate and reproducible detection of circulating HPV DNA and supports feasibility for POC applications, pending further validation in broader clinical settings.

This work presents a multi-institutional image comparison between novel O-ring linac cone-beam computed tomography (CBCT) and fan-beam computed tomography simulator (FBCT), among five institutions. A phantom was sent to five institutions with Ethos and Halcyon units equipped with HyperSight CBCT (HS-CBCT). HyperSight used a 125 kVp/176 mAs protocol and an iterative-CBCT reconstruction algorithm with scatter correction. FBCT imaging protocols used 125 kVp and exposure from 176 to 379 mAs. Linear fitting of relative electron density versus Hounsfield unit (RED-vs-HU) curves was determined for RED &#x2264; 1 and RED &#x2265; 1. The contrast was evaluated with regard to solid water. Noise and contrast-to-noise ratio (CNR) were evaluated with and without exposure normalization. The RED-vs-HU curve for RED &#x2264; 1 of FBCT shows a 4% greater slope than HS-CBCT (P = 0.024), while for the RED &#x2265; 1 case, the FBCT slope is 15% larger compared to (P = 0.00004). HS-CBCT has a larger contrast as RED differs from 1, although at RED > 1, the difference is significant, up to 14%. However, noise is larger for HS-CBCT, especially for RED > 1, up to 10 times. Even with exposure correction, noise is more significant for HS-CBCT, which leads to a CNR ten times smaller than FBCT (RED = 1.78). This multi-institutional analysis of HS-CBCT showed reduced slopes of the RED-vs-HU curves compared to FBCT, which results in a greater sensitivity of the HS-CBCT to RED changes. HS-CBCT shows better contrast performance, although the broader beam leads to noise up to 10 times larger noise, even with the scatter correction. The CNR of the FBCT is up to one order of magnitude greater than that for HS-CBCT.