Search Results (725)

Introduction: Breast density is a well-recognized factor in breast cancer risk assessment, with higher density linked to increased malignancy risk and reduced sensitivity of conventional mammography. Background parenchymal enhancement (BPE), observed in contrast-enhanced imaging, reflects physiological contrast uptake in non-pathologic breast tissue. While extensively characterized in breast MRI, the role of BPE in contrast-enhanced mammography (CEM) remains uncertain due to inconsistent findings regarding its correlation with breast density and cancer risk. Unlike breast density—standardized through the ACR BI-RADS lexicon—BPE lacks a uniform classification system in CEM, leading to variability in clinical interpretation and research outcomes. To address this gap, we introduce the BPE-CEM Standard Scale (BCSS), a structured four-tiered classification system specifically tailored to the two-dimensional characteristics of CEM, aiming to improve consistency and diagnostic alignment in BPE evaluation. Materials and Methods: In this retrospective single-center study, 213 patients who underwent mammography (MG), ultrasound (US), and contrast-enhanced mammography (CEM) between May 2022 and June 2023 at the “A. Perrino” Hospital in Brindisi were included. Breast density was classified according to ACR BI-RADS (categories A–D). BPE was categorized into four levels: Minimal (< 10% enhancement), Light (10–25%), Moderate (25–50%), and Marked (> 50%). Three radiologists independently assessed BPE in a subset of 50 randomly selected cases to evaluate inter-observer agreement using Cohen’s kappa. Correlations between BPE, breast density, and age were examined through regression analysis. Results: BPE was Minimal in 57% of patients, Light in 31%, Moderate in 10%, and Marked in 2%. A significant positive association was found between higher breast density (BI-RADS C–D) and increased BPE (p < 0.05), whereas lower-density breasts (A–B) were predominantly associated with minimal or light BPE. Regression analysis confirmed a modest but statistically significant association between breast density and BPE (R² = 0.144), while age showed no significant effect. Inter-observer agreement for BPE categorization using the BCSS was excellent (κ = 0.85; 95% CI: 0.78–0.92), supporting its reproducibility. Conclusions: Our findings indicate that breast density is a key determinant of BPE in CEM. The proposed BCSS offers a reproducible, four-level framework for standardized BPE assessment tailored to the imaging characteristics of CEM. By reducing variability in interpretation, the BCSS has the potential to improve diagnostic consistency and facilitate integration of BPE into personalized breast cancer risk models. Further prospective multicenter studies are needed to validate this classification and assess its clinical impact. Full article

Background/Objectives: The extensive use of computed tomography (CT) has led to a significant increase in the detection of small and non-palpable pulmonary nodules, necessitating the use of invasive methods for definitive diagnosis. Video-assisted thoracoscopic surgery (VATS) has become the preferred procedure for nodule resections; however, intraoperative localization remains challenging, especially for deep or subsolid lesions. This study explores whether SPECT/CT improves the technical and clinical outcomes of radio-guided occult lesion localization (ROLL) before uniportal video-assisted thoracoscopic surgery (u-VATS). Methods: This is a retrospective study involving consecutive patients referred for the resection of pulmonary nodules who underwent CT-guided ROLL followed by u-VATS between September 2017 and December 2024. From January 2023, SPECT/CT was systematically added after planar imaging. The cohort was divided into a planar group and a planar + SPECT/CT group. The inclusion criteria involved nodules sized ≤ 2 cm, with ground glass or solid characteristics, located at a depth of <6 cm from the pleural surface. ^99mTc-MAA injected activity, timing, the classification of planar and SPECT/CT image findings (focal uptake, multisite with focal uptake, multisite without focal uptake), spillage, and post-procedure complications were evaluated. Statistical analysis was performed, with continuous data expressed as the median and categorical data as the number. Comparisons were made using chi-square tests for categorical variables and the Mann–Whitney U test for procedural duration. Cohen’s kappa coefficient was calculated to assess agreement between imaging modalities. Results: In total, 125 patients were selected for CT-guided radiotracer injection followed by uniportal-VATS. The planar group and planar + SPECT/CT group comprised 60 and 65 patients, respectively. Focal uptake was detected in 68 (54%), multisite with focal uptake in 46 (36.8%), and multisite without focal uptake in 11 patients (8.8%). In comparative analyses between planar and SPECT/CT imaging in 65 patients, 91% exhibited focal uptake, revealing significant differences in classification for 40% of the patients. SPECT/CT corrected the classification of 23 patients initially categorized as multisite with focal uptake to focal uptake, improving localization accuracy. The mean procedure duration was 39 min with SPECT/CT. Pneumothorax was more frequently detected with SPECT/CT (43% vs. 1.6%). The intraoperative localization success rate was 96%. Conclusions: SPECT/CT imaging in the ROLL procedure for detecting pulmonary nodules before u-VATs demonstrates a significant advantage in reclassifying radiotracer positioning compared to planar imaging. Considering its limited impact on surgical success rates and additional procedural time, SPECT/CT should be reserved for technically challenging cases. Larger sample sizes, multicentric and prospective randomized studies, and formal cost–utility analyses are warranted. Full article

Objective: Physical joint examination is fundamental in rheumatoid arthritis (RA) assessment. This study evaluated the diagnostic accuracy and agreement between standardized and non-standardized physical joint examinations in RA patients using musculoskeletal ultrasound as the reference standard. Methods: We assessed the joints for tenderness and swelling, calculating sensitivity, specificity, and predictive values. Musculoskeletal ultrasound was used as the reference standard, with adjustment for imperfect reference bias. Agreement between the methods was evaluated using the average kappa coefficient. Results: A total of 1496 joints were evaluated. Without adjustment for imperfect reference bias, standardized examination showed higher sensitivity for detecting pain and swelling than non-standardized examination. Specificity was similar for pain but higher for swelling in standardized examination. After bias adjustment, standardized examination sensitivity improved for pain (93.8% vs. 77.3%; 95% CI: 0.14–0.19) and swelling (91.9% vs. 60.0%; 95% CI: 0.29–0.34). Tenderness specificity remained comparable (standardized examination: 75.4%, non-standardized examination: 76.3%), while the non-standardized examination maintained superior swelling specificity (85.7% vs. 77.1%). Standardized joint examination demonstrated significantly higher concordance than non-standardized assessment in evaluating joint tenderness; standardized assessment yielded significantly greater average kappa coefficients under both false-positive-prioritized (0.44 vs. 0.37; p = 0.01) and false-negative-prioritized scenarios (0.59 vs. 0.45; p < 0.0001). For joint swelling, standardized evaluation showed significantly higher concordance when false negatives were considered more critical (0.59 vs. 0.37; p < 0.0001), whereas differences under false-positive prioritization were not statistically significant. Conclusions: Standardization of the physical joint examination significantly improves diagnostic accuracy and agreement in detecting joint tenderness and swelling in patients with rheumatoid arthritis. Implementing a standardized physical examination protocol may enhance disease activity diagnosis and optimize clinical management of RA. Full article

Background: Large Language Models (LLMs) such as ChatGPT, Claude, and Gemini are being increasingly adopted in medicine; however, their reliability in cardiology remains underexplored. Purpose of the study: To compare the performance of three general-purpose LLMs in response to cardiology-related clinical queries. Study design: Seventy clinical prompts stratified by diagnostic phase (pre or post) and user profile (patient or physician) were submitted to ChatGPT, Claude, and Gemini. Three expert cardiologists, who were blinded to the model’s identity, rated each response on scientific accuracy, completeness, clarity, and coherence using a 5-point Likert scale. Statistical analysis included Kruskal–Wallis tests, Dunn’s post hoc comparisons, Kendall’s W, weighted kappa, and sensitivity analyses. Results: ChatGPT outperformed both Claude and Gemini across all criteria (mean scores: 3.7–4.2 vs. 3.4–4.0 and 2.9–3.7, respectively; p < 0.001). The inter-rater agreement was substantial (Kendall’s W: 0.61–0.71). Pre-diagnostic and patient-framed prompts received higher scores than post-diagnostic and physician-framed ones. Results remained robust across sensitivity analyses. Conclusions: Among the evaluated LLMs, ChatGPT demonstrated superior performance in generating clinically relevant cardiology responses. However, none of the models achieved maximal ratings, and the performance varied by context. These findings highlight the need for domain-specific fine-tuning and human oversight to ensure a safe clinical deployment. Full article

Objective: This proof-of-concept study aimed to evaluate and compare the clinical performance of two AI language models (ChatGPT-3.5 and OpenAI o3) in answering clinical pharmacy multiple-choice questions (MCQs), benchmarked against responses from specialist clinical pharmacists in Jordan, including academic preceptors and hospital-based clinicians. Methods: A total of 60 clinical pharmacy MCQs were developed based on current guidelines across four therapeutic areas: cardiovascular, endocrine, infectious, and respiratory diseases. Each item was reviewed by academic and clinical experts and then pilot-tested with five pharmacists to determine clarity and difficulty. Two ChatGPT models—GPT-3.5 and OpenAI o3—were tested using a standardized prompt for each MCQ, entered in separate sessions to avoid memory retention. Their answers were classified as true/false positives or negatives and retested after two weeks to assess reproducibility. Simultaneously, 25 licensed pharmacists (primarily from one academic institution and several hospitals in Amman) completed the same MCQs using validated references (excluding AI tools). Accuracy, sensitivity, specificity, and Cohen’s Kappa were used to compare AI and human performance, with statistical analysis conducted using appropriate tests at a significance level of p ≤ 0.05. Results: OpenAI o3 achieved the highest accuracy (83.3%), sensitivity (90.0%), and specificity (70.0%), outperforming GPT-3.5 (70.0%, 77.5%, 55.0%) and pharmacists (69.7%, 77.0%, 55.0%). AI performance declined significantly with increasing question difficulty. OpenAI o3 showed the highest accuracy in the cardiovascular domain (93.3%), while GPT-3.5 performed best in infectious diseases (80.0%). Reproducibility was higher for GPT-3.5 (81.6%, κ = 0.556) than OpenAI o3 (76.7%, κ = 0.364). Over two test rounds, GPT-3.5’s accuracy remained stable, whereas OpenAI o3’s accuracy decreased from 83.3% to 70.0%, indicating some variability. Conclusions: OpenAI o3 shows strong promise as a clinical decision-support tool in pharmacy, especially for low- to moderate-difficulty questions. However, inconsistencies in reproducibility and limitations in complex cases highlight the importance of cautious, supervised integration alongside human expertise. Full article

This retrospective study aimed to evaluate the prevalence, morphological types, and distribution patterns of palatoradicular grooves (PRGs) in maxillary anterior teeth using cone-beam computed tomography (CBCT) in a Turkish population. CBCT images of 1553 patients from the radiology archive of Ordu University Faculty of Dentistry (2021–2022) were reviewed. A total of 920 patients (4012 teeth) met the inclusion criteria. The presence, type, and localization of PRGs were assessed. Groove types were classified as Type 1, 2, 3A, or 3B; localization was recorded as mesial, distal, or midpalatal. Bilateral and unilateral occurrences were also analyzed. Statistical analysis involved chi-square tests, Tukey’s HSD, and Cohen’s kappa for intra-observer reliability. PRGs were detected in 23.6% of patients and 10.42% of teeth. Lateral incisors were most affected (87.56%). Type 1 grooves were most common (71.53%), with midpalatal localization being most frequent (54.07%). Bilateral grooves were significantly more prevalent than unilateral ones (p < 0.001). No significant association was found between groove type and tooth type or between gender and bilaterality. This study revealed a high prevalence of PRGs, especially in maxillary lateral incisors, with a significant tendency toward bilateral and midpalatal presentation. CBCT proved essential for detecting palatoradicular grooves, aiding diagnosis and treatment. Full article

The thermal Doppler broadening of spectral profiles for particle populations in the absence or presence of potential fields can be described by kappa distributions. The kappa distribution provides a replacement for the Maxwell–Boltzmann distribution, which can be considered as a generalization for describing systems characterized by local correlations among their particles, as found in space and astrophysical plasmas. This paper presents all special cases of kappa distributions as members of a general pathway family of densities introduced by Mathai. The aim of the present paper is to bring to attention the application of various forms of the kappa distribution, its various special cases and its generalizations, which, in scalar-variable and multivariate situations, belong to a general family of distributions known as Mathai’s pathway models, comprising three different families of functions, namely the generalized type-1 beta, type-2 beta and gamma families. Through one parameter, known as the pathway parameter, one will be able to reach all the three families of functions and the stages of transitioning from one family to another. After pointing out the connection of multivariate (vector-variate) kappa distributions to the multivariate pathway model, the multivariate kappa distribution is extended to the real matrix-variate case by working out the various forms and by evaluating the normalizing constants of the various forms of the matrix-variate case explicitly. It is also pointed out that the pathway models are available for the scalar, vector and rectangular matrix-variate cases in the real domain as well as in the complex domain. Full article

Background/Objectives: The aim of this study was to investigate the relationship between the PRIMARY score derived from [⁶⁸Ga]Ga-PSMA PET/CT and key clinical and pathological parameters of prostate cancer aggressiveness, including the PSA level, ISUP Grade Group, and D’Amico risk classification, in patients with biopsy-proven prostate cancer. A secondary aim was to evaluate the interobserver agreement of the PRIMARY score in routine clinical practice. Methods: This retrospective analysis included 51 patients with histopathologically confirmed prostate adenocarcinoma who underwent [⁶⁸Ga]Ga-PSMA PET/CT imaging for staging. PRIMARY scores were determined based on the intraprostatic uptake pattern, intensity, and zonal localization. These scores were compared with PSA levels, ISUP GG, D’Amico risk classification, and histopathological features such as the cribriform pattern, intraductal carcinoma, perineural invasion, extraprostatic extension, and lymphovascular invasion. The PRIMARY scores were independently assigned by a total of three nuclear medicine physicians, and interobserver agreement was calculated using Fleiss’ kappa analysis. Results: Significant associations were found between the PRIMARY scores and the PSA level, ISUP Grade Group, and D’Amico risk classification. The most prevalent score was PRIMARY 5 (54.9%), which was significantly associated with ISUP GG 5 and the high-risk category in D’Amico classification. Among patients with PRIMARY Score 2, a substantial proportion (64.7%) had ISUP GG ≥ 3, and 58.8% were in the high-risk group, highlighting the limitations of binary PRIMARY classification. No statistically significant correlations were found between the PRIMARY scores and specific histopathologic features. Interobserver agreement was excellent (κ = 0.833). Conclusions: The PRIMARY score demonstrates high reproducibility and clinical relevance in stratifying prostate cancer aggressiveness. However, the findings challenge the reliability of binary classifications, particularly for patients with Score 2, who may still harbor high-grade disease. Integrating imaging-based scores with clinical and histopathological data is essential, particularly for accurate staging and decision-making regarding active surveillance. Full article

Introduction: Unilateral cleft lip and palate (CLP) is a severe orofacial birth defect characterized by improper fusion of facial parts and disturbed orofacial functions. The defect manifests as a gap in the orofacial tissues that is accompanied by defective healing patterns and chronic inflammation. The immune system’s defense factors modulate immunity, inflammation, and healing. Angiogenesis factors control blood-vessel formation. Therefore, these factors are vital in the immunological assessment and understanding of CLP morphopathogenesis. The aim of the study is to assess the distribution of vascular endothelial growth factor (VEGF), transforming growth factor beta 1 (TGF- β1), the total macrophage population and the M2 subtype, heat-shock proteins (HSP) 60 and 70, and nuclear factor kappa B (NF-κB) p50 and p65 subtypes in the affected tissue of children with CLP before and during primary dentition. Materials and Methods: Tissue samples were obtained from 15 patients aged from 3 to 8 months during veloplastic surgery. Five controls were used for comparison of data. Immunohistochemistry, light microscopy, semi-quantitative evaluation (from 0 to ++++), and statistics (Mann–Whitney U test and Spearman’s rank correlation) were used to evaluate the data for statistically significant differences and correlations between the groups. Results: Epithelial tissues affected by CLP presented with statistically significant increases in levels of VEGF (p = 0.007), total macrophages (p = 0.007), HSP60 (p = 0.001), NF-κB p65 (p = 0.000), and p50 (p = 0.045), but with a decrease in M2 macrophages (p = 0.025). Blood vessels in CLP-affected tissues showed a statistically significant increase in levels of NF-κB p65 (p = 0.003) and a statistically significant decrease in M2 numbers (p = 0.014). Connective tissue presented with no statistically significant differences. Spearman’s rank correlation revealed multiple statistically significant correlations—26 positive and 5 negative. Conclusions: Statistically significant changes in levels of VEGF and both NF-κB subtypes and numbers of total macrophages and M2 macrophages suggest a possible alteration of variable immune and inflammatory reactions and macrophage functions associated with the initiation and maintenance of the chronic process and the resulting damage. Full article

Background/Objectives: Vertebral discitis osteomyelitis (VDO) is a serious infection involving the vertebral bodies and intervertebral discs, often requiring biopsy for pathogen identification. However, biopsy yields are variable, and guidance on patient selection remains limited. This study aimed to assess how biopsy culture results influence clinical management and to develop imaging-based scoring systems to predict biopsy outcomes. Methods: In this retrospective study, 70 patients who underwent image-guided vertebral biopsy for suspected VDO between 2013 and 2022 were reviewed. Pre-biopsy MRI and CT findings were scored using novel, simplified criteria. MRI was graded based on soft tissue involvement, while CT evaluated the presence or absence of a vacuum phenomenon. Culture results were correlated with imaging scores and subsequent changes in antibiotic management. Statistical analysis included logistic regression, ROC analysis, and interobserver agreement using Cohen’s Kappa. Results: Of the 70 patients, 27 (38.6%) had positive cultures, and 20 (28.5%) experienced changes in management. Among the 48 patients with both MRI and CT imaging, MRI scores indicating soft tissue involvement and absence of the vacuum sign on CT were independent predictors of positive culture (p = 0.022 and p = 0.047, respectively). The combined predictive model showed an AUC of 0.76. Interobserver agreement was excellent (κ = 0.90 for MRI, κ = 0.95 for CT). Conclusions: MRI and CT features can be used to predict biopsy yield and guide clinical decisions in suspected VDO. These scoring systems may help clinicians identify patients most likely to benefit from biopsy, potentially improving outcomes and minimizing unnecessary procedures. Full article

This study used Jilin-1 satellite data and unmanned aerial vehicle (UAV)-collected visible-thermal infrared imagery to calculate twelve spectral indices and evaluate their effectiveness in distinguishing post-fire forest areas and identifying human-altered land-cover changes in Jinyun Mountain, Chongqing. The research goals included mapping wildfire impacts with M-statistic separability, measuring land-cover distinguishability through Jeffries–Matusita (JM) distance analysis, classifying land-cover types using the random forest (RF) algorithm, and verifying classification accuracy. Cumulative human disturbances—such as land clearing, replanting, and road construction—significantly blocked the natural recovery of burn scars, and during long-term human-assisted recovery periods over one year, the Red Green Blue Index (RGBI), Green Leaf Index (GLI), and Excess Green Index (EXG) showed high classification accuracy for six land-cover types: road, bare soil, deadwood, bamboo, broadleaf, and grass. Key accuracy measures showed producer accuracy (PA) > 0.8, user accuracy (UA) > 0.8, overall accuracy (OA) > 90%, and a kappa coefficient > 0.85. Validation results confirmed that visible-spectrum indices are good at distinguishing photosynthetic vegetation, thermal bands help identify artificial surfaces, and combined thermal-visible indices solve spectral confusion in deadwood recognition. Spectral indices provide high-precision quantitative evidence for monitoring post-fire land-cover changes, especially under human intervention, thus offering important data support for time-based modeling of post-fire forest recovery and improvement of ecological restoration plans. Full article

Background: Artificial intelligence (AI) is increasingly integrated into orthodontic workflows, including digital model analysis modules embedded in orthodontic software. While these systems offer efficiency and automation, the accuracy and clinical reliability of AI-generated measurements and diagnostic assessments remain unclear. Therefore, to use AI systems safely and effectively in clinical orthodontics, it is important to check their results by comparing them with those of experienced orthodontists. Methods: Digital models of 48 patients were analyzed by the Orthodontist group and two AI platforms: Titan (full) and SoftSmile (Bolton only). Three orthodontists independently measured all variables using 3Shape OrthoAnalyzer, and group means were used for comparison. A subset of models was reanalyzed after two weeks to assess consistency. Data distribution was evaluated, and appropriate statistical tests were applied. Reliability was assessed using intraclass correlation coefficients (ICC) and Cohen’s kappa. Results: Almost perfect agreement was observed between the orthodontists and Titan AI in molar classification (κ = 0.955 right, κ = 0.900 left; p < 0.001), with perfect agreement reported across all groups—including between the orthodontists themselves—for Angle classification (κ = 1.00). In anterior and overall Bolton analyses, no meaningful agreement was found between the orthodontists and AI platforms. However, in a subset of patients where all three methods identified the tooth size discrepancy in the same arch (either maxilla or mandible), no significant differences were found in anterior (p = 0.226) or overall Bolton values (p = 0.795). Overjet, overbite, and space analysis values showed significant differences between the orthodontist and Titan groups (p < 0.001). ICC analysis indicated good to excellent intra- and inter-rater reliability within the orthodontist group (≥0.77), while both AI systems demonstrated excellent internal consistency, with ICC values exceeding 0.95. Conclusions: AI-based platforms showed high agreement with orthodontists only in Angle classification. While their performance in Bolton analysis was limited, significant differences were observed in other linear measurements, indicating the need for further refinement before clinical use. Full article

Integrating spatial transcriptomic data with immunofluorescence image data is challenging using existing tools due to their differences in spatial resolution. Immunofluorescence provides information about protein expression at the cellular or subcellular level, whereas spatial transcriptomic platforms typically rely on multicellular “spots” for RNA profiling. Our study coupled spatial transcriptomics of irradiated glioblastoma tissues with immunofluorescence for γH2AX, a marker of DNA damage within the nuclei of cells. We then compared gene expression in γH2AX-positive and negative regions within the tissue. There was significant interobserver variability in manual annotation of γH2AX positivity in multicellular spots by three different researchers (Kappa statistic = 0.345), despite all of them being familiar with γH2AX immunofluorescence and having predefined imaging parameters for annotation. This variability led to different researchers nominating different genes as being associated with DNA repair. To overcome this problem, we have developed a new tool using MATLAB. This tool performs “spot”-wise image analysis and uses researcher-defined parameters such as immunofluorescent marker intensity threshold and number of positive cells to annotate the “spots” as γH2AX positive or negative. The tissue with the most variability in manual annotation was annotated reproducibly by our MATLAB tool, leading to reproducible downstream analysis. Full article

Objective: This study aimed to evaluate the reliability and validity of a Food Frequency Questionnaire (FFQ) designed for use in epidemiological studies among populations in Fujian, China. Methods: From September to December 2023, adults aged 18 years and above residing in Fujian Province, southeastern China, were recruited via online survey promotion. Participants completed the FFQ twice with a one-month interval and also completed a 3-day 24 h dietary recall (3d-24HDR), covering two weekdays and one weekend day, during the same period. The reliability of the FFQ was assessed by comparing the average intake of food groups and nutrients between the two FFQs, using Spearman correlation coefficients, intraclass correlation coefficients (ICCs), and weighted Kappa coefficients based on tertile classification. Validity was evaluated by comparing the average intake values from the FFQs and the 3d-24HDR using similar methods, including Spearman correlation, weighted Kappa statistics, and Bland–Altman analysis. Results: A total of 152 participants completed two FFQs (for reliability assessment), and 142 participants completed the 3d-24HDR (for validity assessment). Spearman correlation coefficients for food group intake between the two FFQs ranged from 0.60 to 0.80, with ICCs ranging from 0.53 to 0.91. For energy and nutrient intake, Spearman coefficients ranged from 0.66 to 0.96, and ICCs ranged from 0.57 to 0.97. After tertile classification, less than 15% of participants were misclassified into distant categories. The weighted Kappa coefficients for food groups and nutrients ranged from 0.37 to 0.71 and 0.43 to 0.88, respectively. In comparison with the 3d-24HDR, Spearman correlations for food groups and nutrients ranged from 0.41 to 0.72 and 0.40 to 0.70, respectively. The proportion of participants classified into the same or adjacent tertile was 78.8–95.1%. Weighted Kappa coefficients and Bland–Altman plots indicated acceptable agreement between the FFQ and 3d-24HDR for most nutrients. Conclusions: The FFQ used in this study demonstrated good reliability and moderate-to-good validity. It is suitable for use in dietary assessment in gastric cancer epidemiological studies in Fujian, China. Full article

The petiole nitrate–nitrogen concentration (PNNC) has been an industry standard indicator for in-season potato (Solanum tuberosum L.) nitrogen (N) status diagnosis. Leaf sensors can be used to predict the PNNC and other N status indicators non-destructively. The SPAD meter is a common leaf chlorophyll (Chl) meter, while the Dualex is a newer leaf fluorescence sensor. Limited research has been conducted to compare the two leaf sensors for potato N status assessment. Therefore, the objectives of this study were to (1) compare SPAD and Dualex for predicting potato N status indicators, and (2) evaluate the potential prediction improvement using multi-source data fusion. The plot-scale experiments were conducted in Becker, Minnesota, USA, in 2018, 2019, 2021, and 2023, involving different cultivars, N treatments, and irrigation rates. The results indicated that Dualex’s N balance index (NBI; Chl/Flav) always outperformed Dualex Chl but did not consistently perform better than the SPAD meter. All N status indicators were predicted with significantly higher accuracy with multi-source data fusion using machine learning models. A practical strategy was developed using a linear support vector regression model with SPAD, cultivar information, accumulated growing degree days, accumulated total moisture, and an as-applied N rate to predict the vine or whole-plant N nutrition index (NNI), achieving an R² of 0.80–0.82, accuracy of 0.75–0.77, and Kappa statistic of 0.57–0.58 (near-substantial). Further research is needed to develop an easy-to-use application and corresponding in-season N recommendation strategy to facilitate practical on-farm applications. Full article