Category: Nevin Manimala

JMIR Res Protoc. 2025 Dec 22;14:e68160. doi: 10.2196/68160.

ABSTRACT

BACKGROUND: Accurate shade matching in dentistry is crucial for achieving aesthetic outcomes, with increasing patient expectations driving advancements in shade selection technologies. Color perception is influenced by multiple factors such as incident light, reflection, absorption, observer variability, and environmental conditions. The evolution of shade-matching tools now includes digital and artificial intelligence (Al)-assisted appliances aimed at improving accuracy and ease of use.

OBJECTIVE: This study aims to compare and evaluate the efficacy of AI-assisted appliances, namely, smartphone cameras, digital single-lens reflex (DSLR) cameras, and intraoral scanners in selecting tooth shades in clinical practice.

METHODS: This observational study conducted at the Department of Prosthodontics and Crown & Bridge aims to evaluate shade selection methods in 221 participants recruited from the outpatient department based on specific inclusion and exclusion criteria, including age, oral health status, and informed consent. Three devices will be used for shade selection: a smartphone camera (iPhone 12, iOS 12.5.2) for quick, noninvasive, and accessible image capture; a DSLR camera (Canon EOS 200D) to obtain high-resolution images under standardized lighting for enhanced color accuracy; and an intraoral scanner (CEREC Primescan, Dentsply Sirona) offering precise 3D mapping and digital shade analysis. This multidevice approach allows for a comparative evaluation of conventional and advanced digital tools in clinical shade matching. The primary objective is to assess the effectiveness of commonly available digital teeth in accurately selecting tooth shades. Our study anticipates the following outcomes: validation of smartphone cameras as simple, economical, and efficient tools for basic shade matching; demonstration of DSLR cameras’ superiority in resolution and lighting control for improved accuracy; and confirmation of intraoral scanners as precise, customizable devices that offer a high level of digital integration. Statistical analyses will include sensitivity, specificity, and subgroup evaluations to compare the performance of each device. The findings are expected to show that both DSLR and smartphone cameras can match the effectiveness of intraoral scanners, offering viable alternatives for clinical use.

RESULTS: This study was intramurally funded in December 2024. Data collection is scheduled to commence following the publication of this study protocol. As of submission, no participants have been recruited, and data analysis is yet to begin. Results are expected to be completed and published in early January 2026.

CONCLUSIONS: This study aims to establish a standard protocol for the use of Al-assisted, easily accessible tools such as smartphones and DSLRs for dental shade selection. These devices, being user-friendly and nontechnical, could democratize the process of shade matching, benefiting both clinicians and patients by improving restoration outcomes while reducing costs and complexity. Our results will contribute to the growing body of digital dentistry literature and support the integration of practical Al tools in everyday clinical practice.

TRIAL REGISTRATION: Clinical Trials Registry-India CTRI/2024/07/070002; https://tinyurl.com/4pt5eutb.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/68160.

PMID:41428389 | DOI:10.2196/68160

Clin Nucl Med. 2025 Dec 16. doi: 10.1097/RLU.0000000000006270. Online ahead of print.

ABSTRACT

BACKGROUND: To investigate newly diagnosed multiple myeloma (MM) patients and determine whether a combination of baseline [18F]FDG-PET-derived parameters and clinical parameters would improve patient prognostication.

PATIENTS AND METHODS: In this IRB-approved study, patients who underwent [18F]FDG-PET/CT as part of their initial diagnostic workup for MM in our centre between 2018 and 2024 were included. Various [18F]FDG-PET/CT parameters were extracted, including bone marrow, focal lesion, and total body measurements. Also, clinical parameters were gathered. The Cox proportional model was employed to estimate hazard ratios (HRs) for each parameter. A P-value <0.05 was considered statistically significant.

RESULTS: A total of 42 patients (mean age =67 y) entered this study. The median follow-up was 24 months. From continuous [18F]FDG-PET/CT-derived parameters, total-body metabolic tumor volume (TMTV), total-body total lesion glycolysis (TTLG), and bone marrow SUVmax were found to be significantly correlated with patient survival. Following dichotomization, TMTV and TTLG lost their statistical significance, while bone marrow SUVmax retained its significance, showing an HR of 8.5 (P = 0.039). Moreover, the presence of extramedullary disease was the other significant predictor of survival, with an HR of 5.5 (P = 0.002). Among the continuous clinical parameters, serum free light chain ratio, β2-microglobulin, LDH, and creatinine levels significantly correlated with patient survival. Only serum β2-microglobulin retained its significance following dichotomization, showing an HR of 4.0 (P = 0.015).

CONCLUSIONS: [18F]FDG-PET/CT-derived parameters, particularly high bone marrow SUVmax and the presence of extramedullary disease, as well as their combination with clinical parameters, particularly high serum β2-microglobulin level, have the potential to enhance MM prognostication at the time of baseline staging.

PMID:41428382 | DOI:10.1097/RLU.0000000000006270

J Psychol. 2025 Dec 22:1-28. doi: 10.1080/00223980.2025.2601591. Online ahead of print.

ABSTRACT

This study aimed to investigate the underlying mechanism of the association between emotional intelligence (EI) and psychological well-being (PWB) among international students in China. We specifically examined the mediating effects of psychological capital (PsyCap) and academic engagement, as well as the moderating effect of social support. A moderated mediation model was evaluated using a sample of 443 participants. The results suggested that there was a significant positive relationship between students’ EI and their PWB. The serial multiple mediation model revealed that the indirect associations of EI with PWB through PsyCap and academic engagement were statistically significant. Moreover, the findings revealed that social support moderated all correlations between study variables, with the exception of EI and academic engagement, and that the strength of these connections increased with greater levels of social support. The present findings provide important insights into how to enhance the overall well-being of international students.

PMID:41428376 | DOI:10.1080/00223980.2025.2601591

J Racial Ethn Health Disparities. 2025 Dec 22. doi: 10.1007/s40615-025-02805-y. Online ahead of print.

ABSTRACT

INTRODUCTION: Chronic pain disproportionately affects older adults, especially Black/African American older adults with musculoskeletal disorders like osteoarthritis. Given that osteoarthritis and chronic musculoskeletal conditions are the leading causes of pain and disability worldwide, self-management of chronic pain is essential. One of the goals in the U.S. National Pain Strategy was to develop nationwide pain self-management programs. This paper describes a multi-method approach used to design a new pain self-management intervention for Black/African American older adults living with chronic musculoskeletal pain.

METHODS: Four mixed-methods studies investigated the lived experiences, cultural beliefs, and social factors that affect how older adults perceive, manage, and cope with osteoarthritis and joint pain. Research participants were adults, ranging in age from 50 to 94 years of age with chronic musculoskeletal pain-primarily osteoarthritis. Other key stakeholders, such as a community advisory board and subject matter experts, were included at different stages of each study. Descriptive statistics, thematic analysis, and content analysis were applied across studies to identify recurring patterns.

RESULTS: Across studies, older Black/African American individuals revealed how they manage chronic musculoskeletal pain in ways consistent with their culture, spiritual beliefs, and accessible resources. While older adults were engaged in varying levels of self-management, there were clear differences in pain experiences and disability impact, types of self-management strategies utilized, and opportunities to tailor or customize self-management interventions. Data from research participants, community advisory board, and experts in the field helped (1) create and evaluate a new educational resource for Black/African Americans and (2) shape the structure of the PROACTIVE intervention, which combines culturally congruent pain education, spiritual coping, and financial navigation to help older Black/African Americans manage chronic musculoskeletal pain.

CONCLUSION: This new pain intervention offers B/AA older individuals the skills, motivation, and resources to manage chronic pain in a culturally sensitive and practical way. It is our hope that this intervention will improve pain outcomes and physical and emotional quality of life in this population.

PMID:41428325 | DOI:10.1007/s40615-025-02805-y

Ophthalmol Ther. 2025 Dec 22. doi: 10.1007/s40123-025-01292-y. Online ahead of print.

ABSTRACT

INTRODUCTION: Patients with retinitis pigmentosa (RP) frequently exhibit zonular weakness, which poses challenges for intraocular lens (IOL) stability and refractive prediction. This study aimed to evaluate the impact of capsular tension ring (CTR) implantation on the predictive accuracy of 12 IOL power calculation formulas in patients with RP receiving cataract surgery.

METHODS: We conducted a retrospective cohort study where the predictive accuracy of 12 IOL formulas was assessed using refractive prediction error (PE), mean absolute error (MAE), root-mean-square absolute error (RMSAE), and the percentage of eyes achieving target refraction within ± 0.25 D to ± 1.00 D. These metrics were compared between eyes with (n = 23) and without (n = 30) CTR implantation. The influence of lens thickness (LT) on formula accuracy was also evaluated.

RESULTS: In the overall cohort of 53 eyes from 38 patients with RP, the Barrett Universal II (BUII) formula yielded the lowest numerical MAE (0.45 D) and RMSAE (0.56 D), though none of the formulas were statistically superior in RMSAE. In the CTR group, significantly lower MAE was observed for Cooke K6 (P = 0.024), Kane (P = 0.016), Emmetropia Verifying Optical (EVO) 2.0 (P = 0.040), and PEARL-DGS (P = 0.021) compared to the non-CTR group. The CTR group also exhibited a significantly higher percentage of eyes within ± 0.50 D (P = 0.016), ± 0.75 D (P < 0.001), and ± 1.00 D (P < 0.001) of target refraction. Increasing LT correlated with a hyperopic shift for all formulas; however, BUII demonstrated relatively stable MAE across LT quartiles.

CONCLUSIONS: Implantation of a CTR was associated with significantly improved predictive accuracy for several modern IOL formulas in patients with RP. The BUII formula showed the highest overall predictive accuracy.

PMID:41428313 | DOI:10.1007/s40123-025-01292-y

Brain Topogr. 2025 Dec 22;39(1):14. doi: 10.1007/s10548-025-01169-0.

ABSTRACT

Infrared neural stimulation (INS) represents an invasive technique for modulating brain activity in animals, particularly primates, which serve as effective models for human brain research. Noninvasive approaches, such as transcranial laser stimulation, are safer but have lower spatial and temporal resolution, primarily altering metabolic processes rather than directly stimulating specific neurons. Invasive techniques provide better resolution by targeting neurons with focused laser beams but require intricate surgeries that damage the meninges, limiting studies to short-term experiments conducted mostly on anesthetized animals. We present a minimally invasive approach for long-term, high-resolution laser INS that does not disrupt brain tissue integrity and minimizes the risk of inflammation. Laser radiation is delivered through contact between a flexible optical fiber and the outer surface of the dura mater, allowing for chronic experiments on non-anesthetized primates who maintain their cognitive functions and physical activities. This method has enabled us to conduct a multi-day INS experiment and collect statistically reliable data on neurophysiological responses in a cognitively intact primate subjected to targeted high-resolution INS. We analyzed electrocorticogram and evoked potentials in various cortical areas while applying infrared laser stimulation directed at a selected point on the primary visual cortex of a rhesus macaque. Results indicated that even low-intensity laser stimulation (below conscious perception thresholds) caused synchronous biopotential changes not only at the stimulation site but also in certain distant cortical regions, suggesting a more complex brain response mechanism to INS than merely the activation of stimulated neurons. We believe the presented method will significantly facilitate chronic INS studies, further contributing to fundamental and clinical outcomes.

PMID:41428267 | DOI:10.1007/s10548-025-01169-0

Eur J Pediatr. 2025 Dec 22;185(1):33. doi: 10.1007/s00431-025-06693-6.

ABSTRACT

PURPOSE: Differentiating among exanthematous diseases is frequently challenging due to their overlapping symptomatology. We, therefore, aimed to evaluate the diagnostic accuracy of a consultant physician, a resident physician, and various AI models (ChatGPT-5, Gemini, Copilot) in this context.

METHODS: We prospectively enrolled 291 patients treated for exanthematous diseases at our clinic between January 2024 and July 2025. The AI models were first tasked with making a diagnosis based solely on cutaneous images and subsequently with both images and accompanying clinical findings. The diagnoses rendered by the consultant, the resident, and the AI models were then compared against the definitive diagnosis.

RESULTS: When benchmarked against the definitive diagnosis, the consultant achieved the highest diagnostic accuracy (96.6%), followed by ChatGPT (with clinical data, 86.9%), Copilot (with clinical data, 81.4%), Gemini (with clinical data, 78.7%), and the resident physician (72.5%). In contrast, models without clinical data performed poorly, with the lowest accuracy recorded at 30.6% by Copilot. In ROC analysis against the consultant, the resident (AUC: .875) and AI models with clinical data-ChatGPT (AUC: .898), Gemini (AUC: .856), and Copilot (AUC: .818)-all demonstrated good diagnostic power (p < .001). The ChatGPT model without clinical data showed moderate diagnostic power, whereas the Copilot and Gemini models without data were not statistically significant. Performance metrics (sensitivity, specificity) were: ChatGPT (with data) (89.7%, 90.0%); Copilot (with data) (83.6%, 80.0%); Gemini (with data) (81.1%, 90.0%); the resident (75.1%, 100.0%); ChatGPT (no data) (51.6%, 90.0%); Gemini (no data) (33.5%, 100.0%); and Copilot (no data) (31.7%, 100.0%). The consultant’s diagnostic performance was significantly superior to all other interpreters and models (p < .001 for all comparisons).

CONCLUSION: This study establishes the diagnostic utility of AI models in pediatric exanthematous diseases, with ChatGPT-5 demonstrating the greatest accuracy when augmented with clinical data. The findings position these models as powerful assistive tools for clinicians but affirm that they do not yet supplant the indispensable expertise of a consultant physician, who remains the gold standard for diagnosis.

WHAT IS KNOWN: • Overlapping clinical features of exanthematous diseases often lead to diagnostic uncertainty. • Rash-focused artificial intelligence models frequently perform better when supplemented with clinical context rather than image data alone.

WHAT IS NEW: • This study provides the first large-scale, multimodal comparison of three next-generation artificial intelligence models (ChatGPT-5, Gemini, Copilot) specifically in pediatric exanthematous diseases. • The study uniquely demonstrates the diagnostic performance gap between image-only and image-plus-clinical-data modes across multiple artificial intelligence models, quantifying the exact improvement provided by clinical context. • By benchmarking artificial intelligence performance simultaneously against both a consultant and a resident physician, this work introduces a novel dual-reference standard, offering more nuanced insight into real-world clinical use cases.

PMID:41428260 | DOI:10.1007/s00431-025-06693-6

J Robot Surg. 2025 Dec 22;20(1):112. doi: 10.1007/s11701-025-03029-2.

ABSTRACT

To evaluate the effect of a multidisciplinary team (MDT) continuing care model led by diabetes specialist nurses on patients with benign adrenal tumors and type 2 diabetes mellitus undergoing robotic daytime surgery. This retrospective cohort study enrolled 60 type 2 diabetes patients undergoing robot-assisted adrenal tumor resection at the Day Surgery Department of Shanxi Bethune Hospital between October 2024 and May 2025. Patients were divided into two groups based on recorded nursing patterns: the observation group (n = 30) received structured multidisciplinary team (MDT) extended care in addition to standard nursing, while the control group (n = 30) received routine follow-up management. The study compared blood glucose control parameters (fasting glucose, 2-hour postprandial glucose, and glycated hemoglobin), 8-day postoperative wound healing outcomes, diabetes self-management behavior scores, patient satisfaction, and 48-hour delayed discharge rates under different surgical management models. Before the intervention, there was no statistically significant difference in various indicators between the two groups (P > 0.05). After the intervention, the fasting blood glucose, 2-hour postprandial blood glucose, and glycated hemoglobin indicators in the observation group were better than those in the control group. The postoperative wound healing and patient self-management behavior results were superior to the control group. Patient satisfaction was higher in the observation group, and the 48-hour delayed discharge rate under the daytime surgery model was lower than that in the control group. These differences were statistically significant (P < 0.05). This retrospective analysis indicates that, in clinical practice, implementing a diabetes-specialist-nurse-led MDT transitional care model for diabetic patients with adrenal tumors undergoing robotic ambulatory surgery is significantly associated with better glycemic control, enhanced postoperative wound healing, improved patient self-management ability and satisfaction, and reduced delayed discharge rates. This model can serve as a beneficial strategy to optimize the management of ambulatory surgical patients.

PMID:41428247 | DOI:10.1007/s11701-025-03029-2

Biometrics. 2025 Oct 8;81(4):ujaf165. doi: 10.1093/biomtc/ujaf165.

ABSTRACT

In many genomic studies, gene co-expression graphs are influenced by subject-level covariates like single nucleotide polymorphisms. Traditional Gaussian graphical models ignore these covariates and estimate only population-level networks, potentially masking important heterogeneity. Covariate-dependent Gaussian graphical regressions address this limitation by regressing the precision matrix on covariates, thereby modeling how graph structures vary with high-dimensional subject-specific covariates. To fit the model, we adopt a multi-task learning approach that achieves lower error rates than node-wise regressions. Yet, the important problem of statistical inference in this setting remains largely unexplored. We propose a class of debiased estimators based on multi-task learners, which can be computed quickly and separately. In a key step, we introduce a novel projection technique for estimating the inverse covariance matrix, reducing optimization costs to scale with the sample size n. Our debiased estimators achieve fast convergence and asymptotic normality, enabling valid inference. Simulations demonstrate the utility of the method, and an application to a brain cancer gene-expression dataset reveals meaningful biological relationships.

PMID:41428236 | DOI:10.1093/biomtc/ujaf165

Biometrics. 2025 Oct 8;81(4):ujaf164. doi: 10.1093/biomtc/ujaf164.

ABSTRACT

Multi-state models are widely used to study complex interrelated life events. In resource-limited settings, nested case-control (NCC) sampling may be employed to extract subsamples from a cohort for an event of interest, followed by a conditional likelihood analysis. However, conditioning restricts the reuse of NCC data for studying additional events. An alternative approach constructs pseudolikelihoods using inverse probability weighting (IPW) for inference with NCC data. Existing IPW-based pseudolikelihood methods focus primarily on estimating relative risks for multiple outcomes or secondary endpoints. In this work, we extend these methods to predict transition probabilities under general multi-state models and evaluate their efficiency. As the standard IPW methods for the prediction of transition probabilities may suffer from inefficiency, we propose two novel approaches for more efficient prediction and derive explicit variance estimates for these methods. The first approach calibrates the design weights using cohort-level information, while the second jointly models transitions originating from the same state. A simulation study demonstrates that either approach substantially improves efficiency and that their combined application yields further gains. We illustrate these methods with real data from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial.

PMID:41428235 | DOI:10.1093/biomtc/ujaf164