Transcranial photoacoustic computed tomography (PACT) holds significant potential as a neuroimaging modality. However, compensating for skull-induced aberrations in reconstructed images remains a challenge. Although optimization-based image reconstruction methods (OBRMs) can account for the relevant wave physics, they are computationally demanding and generally require accurate estimates of the skull's viscoelastic parameters. To circumvent these issues, a learning-based image reconstruction method was investigated for three-dimensional (3D) transcranial PACT. The method was systematically assessed in virtual imaging studies that involved stochastic 3D numerical head phantoms and applied to experimental data acquired by use of a physical head phantom that involved a human skull. The results demonstrated that the learning-based method yielded accurate images and exhibited robustness to errors in the assumed skull properties, while substantially reducing computational times compared to an OBRM. To the best of our knowledge, this is the first demonstration of a learned image reconstruction method for 3D transcranial PACT.

Comprehending the biomechanical characteristics of the human lumbar spine is crucial for managing and preventing spinal disorders. Precise material properties derived from patient-specific CT scans are essential for simulations to accurately mimic real-life scenarios, which is invaluable in creating effective surgical plans. The integration of Finite Element Analysis (FEA) with Physics-Informed Neural Networks (PINNs) offers significant clinical benefits by automating lumbar spine segmentation and meshing. We developed a FEA model of the lumbar spine incorporating detailed anatomical and material properties derived from high-quality CT and MRI scans. The model includes vertebrae and intervertebral discs, segmented and meshed using advanced imaging and computational techniques. PINNs were implemented to integrate physical laws directly into the neural network training process, ensuring that the predictions of material properties adhered to the governing equations of mechanics. The model achieved an accuracy of 94.30% in predicting material properties such as Young's modulus (14.88 GPa for cortical bone and 1.23 MPa for intervertebral discs), Poisson's ratio (0.25 and 0.47, respectively), bulk modulus (9.87 GPa and 6.56 MPa, respectively), and shear modulus (5.96 GPa and 0.42 MPa, respectively). We developed a lumbar spine FEA model using anatomical and material properties from CT and MRI scans. Vertebrae and discs were segmented and meshed with advanced imaging techniques, while PINNs ensured material predictions followed mechanical laws. The integration of FEA and PINNs allows for accurate, automated prediction of material properties and mechanical behaviors of the lumbar spine, significantly reducing manual input and enhancing reliability. This approach ensures dependable biomechanical simulations and supports the development of personalized treatment plans and surgical strategies, ultimately improving clinical outcomes for spinal disorders. This method improves surgical planning and outcomes, contributing to better patient care and recovery in spinal disorders.

Focal cortical dysplasia (FCD) is a common cause of pharmacoresistant epilepsy. However, it can be challenging to detect FCD using MRI alone. This study aimed to review and analyze studies that used machine learning and artificial neural networks (ANN) methods as an additional tool to enhance MRI findings in FCD patients. A systematic search was conducted in four databases (Embase, PubMed, Scopus, and Web of Science). The quality of the studies was assessed using QUADAS-AI, and a bivariate random-effects model was used for analysis. The main outcome analyzed was the sensitivity and specificity of patient-wise outcomes. Heterogeneity among studies was assessed using I². A total of 41 studies met the inclusion criteria, including 24 ANN-based studies and 17 machine learning studies. Meta-analysis of internal validation datasets showed a pooled sensitivity of 0.81 and specificity of 0.92 for AI-based models in detecting FCD lesions. Meta-analysis of external validation datasets yielded a pooled sensitivity of 0.73 and specificity of 0.66. There was moderate heterogeneity among studies in the external validation dataset, but no significant publication bias was found. Although there is an increasing number of machine learning and ANN-based models for FCD detection, their clinical applicability remains limited. Further refinement and optimization, along with longitudinal studies, are needed to ensure their integration into clinical practice. Addressing the identified limitations and intensifying research efforts will improve their relevance and reliability in real medical scenarios.

AI has emerged as a transformative force in clinical medicine, changing the diagnosis, treatment, and management of patients. Tools have been derived for working with ML, DL, and NLP algorithms to analyze large complex medical datasets with unprecedented accuracy and speed, thereby improving diagnostic precision, treatment personalization, and patient care outcomes. For example, CNNs have dramatically improved the accuracy of medical imaging diagnoses, and NLP algorithms have greatly helped extract insights from unstructured data, including EHRs. However, there are still numerous challenges that face AI integration into clinical workflows, including data privacy, algorithmic bias, ethical dilemmas, and problems with the interpretability of "black-box" AI models. These barriers have thus far prevented the widespread application of AI in health care, and its possible trends, obstacles, and future implications are necessary to be systematically explored. The purpose of this paper is, therefore, to assess the current trends in AI applications in clinical medicine, identify those obstacles that are hindering adoption, and identify possible future directions. This research hopes to synthesize evidence from other peer-reviewed articles to provide a more comprehensive understanding of the role that AI plays to advance clinical practices, improve patient outcomes, or enhance decision-making. A systematic review was done according to the PRISMA guidelines to explore the integration of Artificial Intelligence in clinical medicine, including trends, challenges, and future directions. PubMed, Cochrane Library, Web of Science, and Scopus databases were searched for peer-reviewed articles from 2014 to 2024 with keywords such as "Artificial Intelligence in Medicine," "AI in Clinical Practice," "Machine Learning in Healthcare," and "Ethical Implications of AI in Medicine." Studies focusing on AI application in diagnostics, treatment planning, and patient care reporting measurable clinical outcomes were included. Non-clinical AI applications and articles published before 2014 were excluded. Selected studies were screened for relevance, and then their quality was critically appraised to synthesize data reliably and rigorously. This systematic review includes the findings of 8 studies that pointed out the transformational role of AI in clinical medicine. AI tools, such as CNNs, had diagnostic accuracy more than the traditional methods, particularly in radiology and pathology. Predictive models efficiently supported risk stratification, early disease detection, and personalized medicine. Despite these improvements, significant hurdles, including data privacy, algorithmic bias, and resistance from clinicians regarding the "black-box" nature of AI, had yet to be surmounted. XAI has emerged as an attractive solution that offers the promise to enhance interpretability and trust. As a whole, AI appeared promising in enhancing diagnostics, treatment personalization, and clinical workflows by dealing with systemic inefficiencies. The transformation potential of AI in clinical medicine can transform diagnostics, treatment strategies, and efficiency. Overcoming obstacles such as concerns about data privacy, the danger of algorithmic bias, and difficulties with interpretability may pave the way for broader use and facilitate improvement in patient outcomes while transforming clinical workflows to bring sustainability into healthcare delivery.

Global aging raises concerns about cognitive health, metabolic disorders, and sarcopenia. Prevention of reversible decline and diseases in middle-aged individuals is essential for promoting healthy aging. We hypothesize that changes in body composition, specifically muscle mass and visceral fat, and metabolic indices are associated with accelerated brain aging. To explore these relationships, we employed a brain age model to investigate the links between the brain age gap (BAG), body composition, and metabolic markers. Using T1-weighted anatomical brain MRIs, we developed a machine learning model to predict brain age from gray matter features, trained on 2,675 healthy individuals aged 18-92 years. This model was then applied to a separate cohort of 458 Taiwanese adults (57.8 years ± 11.6; 280 men) to assess associations between BAG, body composition quantified by MRI, and metabolic markers. Our model demonstrated reliable generalizability for predicting individual age in the clinical dataset (MAE = 6.11 years, r = 0.900). Key findings included significant correlations between larger BAG and reduced total abdominal muscle area (r = -0.146, p = 0.018), lower BMI-adjusted skeletal muscle indices, (r = -0.134, p = 0.030), increased systemic inflammation, as indicated by high-sensitivity C-reactive protein levels (r = 0.121, p = 0.048), and elevated fasting glucose levels (r = 0.149, p = 0.020). Our findings confirm that muscle mass and metabolic health decline are associated with accelerated brain aging. Interventions to improve muscle health and metabolic control may mitigate adverse effects of brain aging, supporting healthier aging trajectories.

Artificial intelligence (AI) may extract prognostic information from MRI for localized prostate cancer. We evaluate whether AI-derived prostate and gross tumor volume (GTV) are associated with toxicity and oncologic outcomes after radiotherapy. We conducted a retrospective study of patients, who underwent radiotherapy between 2010 and 2017. We trained an AI segmentation algorithm to contour the prostate and GTV from patients treated with external-beam RT, and applied the algorithm to those treated with brachytherapy. AI prostate and GTV volumes were calculated from segmentation results. We evaluated whether AI GTV volume was associated with biochemical failure (BF) and metastasis. We evaluated whether AI prostate volume was associated with acute and late grade 2+ genitourinary toxicity, and International Prostate Symptom Score (IPSS) resolution for monotherapy and combination sets, separately. We identified 187 patients who received brachytherapy (monotherapy (N = 154) or combination therapy (N = 33)). AI GTV volume was associated with BF (hazard ratio (HR):1.28[1.14,1.44];p < 0.001) and metastasis (HR:1.34[1.18,1.53;p < 0.001). For the monotherapy subset, AI prostate volume was associated with both acute (adjusted odds ratio:1.16[1.07,1.25];p < 0.001) and late grade 2 + genitourinary toxicity (adjusted HR:1.04[1.01,1.07];p = 0.01), but not IPSS resolution (0.99[0.97,1.00];p = 0.13). For the combination therapy subset, AI prostate volume was not associated with either acute (p = 0.72) or late (p = 0.75) grade 2 + urinary toxicity. However, AI prostate volume was associated with IPSS resolution (0.96[0.93, 0.99];p = 0.01). AI-derived prostate and GTV volumes may be prognostic for toxicity and oncologic outcomes after RT. Such information may aid in treatment decision-making, given differences in outcomes among RT treatment modalities.

To investigate the impacts of a deep learning-based iterative reconstruction algorithm on image quality and measuring accuracy of bone mineral density (BMD) in low-dose chest CT. Phantom and patient studies were separately conducted in this study. The same low-dose protocol was used for phantoms and patients. All images were reconstructed with filtered back projection, hybrid iterative reconstruction (HIR) (KARL®, level of 3,5,7), and deep learning-based iterative reconstruction (artificial intelligence iterative reconstruction [AIIR], low, medium, and high strength). The noise power spectrum (NPS) and the task-based transfer function (TTF) were evaluated using phantom. The accuracy and the relative error (RE) of BMD were evaluated using a European spine phantom. The subjective evaluation was performed by 2 experienced radiologists. BMD was measured using quantitative CT (QCT). Image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), BMD values, and subjective scores were compared with Wilcoxon signed-rank test. The Cohen's kappa test was used to evaluate the inter-reader and inter-group agreement. AIIR reduced noise and improved resolution on phantom images significantly. There were no significant differences among BMD values in all groups of images (all P > 0.05). RE of BMD measured using AIIR images was smaller. In objective evaluation, all strengths of AIIR achieved less image noise and higher SNR and CNR (all P < 0.05). AIIR-H showed the lowest noise and highest SNR and CNR (P < 0.05). The increase in AIIR algorithm strengths did not affect BMD values significantly (all P > 0.05). The deep learning-based iterative reconstruction did not affect the accuracy of BMD measurement in low-dose chest CT while reducing image noise and improving spatial resolution. The BMD values could be measured accurately in low-dose chest CT with deep learning-based iterative reconstruction while reducing image noise and improving spatial resolution.

This study aimed to develop an ultrasound image-based radiomics model for diagnosing cervical lymph node (LN) metastasis in patients with head and neck squamous cell carcinoma (HNSCC) that shows higher accuracy than previous models. A total of 537 LN (260 metastatic and 277 nonmetastatic) from 126 patients (78 men, 48 women, average age 63 years) were enrolled. The multivariate analysis software Prediction One (Sony Network Communications Corporation) was used to create the diagnostic models. Furthermore, three machine learning methods were adopted as comparison approaches. Based on a combination of texture analysis results, clinical information, and ultrasound findings interpretated by specialists, a total of 12 models were created, three for each machine learning method, and their diagnostic performance was compared. The three best models had area under the curve of 0.98. Parameters related to ultrasound findings, such as presence of a hilum, echogenicity, and granular parenchymal echoes, showed particularly high contributions. Other significant contributors were those from texture analysis that indicated the minimum pixel value, number of contiguous pixels with the same echogenicity, and uniformity of gray levels. The radiomics model developed was able to accurately diagnose cervical LN metastasis in HNSCC.

Cone Beam Computed Tomography (CBCT) has become a crucial imaging tool in modern dentistry. At present, a review does not exist to provide comprehensive understanding of the technological advancements and the entities driving the innovations of CBCT. This study aimed to analyse the patent records associated with CBCT technology, to gain valuable insights into the trends and breakthroughs, and identify key areas of focus from manufacturers. The online patent database called The Lens was accessed on 3 January 2025 to identify relevant patent records. A total of 706 patent records were identified and analysed. The majority of over 700 CBCT patents was contributed by CBCT manufacturers. The United States was the jurisdiction with most patent records, followed by Europe and China. Some manufacturers hold patent records for common features of CBCT systems, such as motion artifact correction, metal artifact reduction, reconstruction of panoramic image based on 3D data, and incorporation of artificial intelligence. Patent analysis can offer valuable insights into the development and advancement of CBCT technology, and foster collaboration between manufacturers, researchers, and clinicians. The advancements in CBCT technology, as reflected by patent trends, enhance diagnostic accuracy and treatment planning. Understanding these technological innovations can aid clinicians in selecting the most effective imaging tools for patient care.

In the HECKTOR 2022 challenge set [1], several state-of-the-art (SOTA, achieving best performance) deep learning models were introduced for predicting recurrence-free period (RFP) in head and neck cancer patients using PET and CT images. This study investigates whether a conventional DenseNet architecture, with optimized numbers of layers and image-fusion strategies, could achieve comparable performance as SOTA models. The HECKTOR 2022 dataset comprises 489 oropharyngeal cancer (OPC) patients from seven distinct centers. It was randomly divided into a training set (n = 369) and an independent test set (n = 120). Furthermore, an additional dataset of 400 OPC patients, who underwent chemo(radiotherapy) at our center, was employed for external testing. Each patients' data included pre-treatment CT- and PET-scans, manually generated GTV (Gross tumour volume) contours for primary tumors and lymph nodes, and RFP information. The present study compared the performance of DenseNet against three SOTA models developed on the HECKTOR 2022 dataset. When inputting CT, PET and GTV using the early fusion (considering them as different channels of input) approach, DenseNet81 (with 81 layers) obtained an internal test C-index of 0.69, a performance metric comparable with SOTA models. Notably, the removal of GTV from the input data yielded the same internal test C-index of 0.69 while improving the external test C-index from 0.59 to 0.63. Furthermore, compared to PET-only models, when utilizing the late fusion (concatenation of extracted features) with CT and PET, DenseNet81 demonstrated superior C-index values of 0.68 and 0.66 in both internal and external test sets, while using early fusion was better in only the internal test set. The basic DenseNet architecture with 81 layers demonstrated a predictive performance on par with SOTA models featuring more intricate architectures in the internal test set, and better performance in the external test. The late fusion of CT and PET imaging data yielded superior performance in the external test.