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Professional UAV thermal imaging systems are widely used for inspection, environmental monitoring, search and rescue, agriculture, and technical diagnostics. However, their cost limits their use in education, preliminary field screening, rapid prototyping, and low-resource applications. This study evaluates a minimum-cost indirect UAV thermal sensing workflow based on a DJI Mini 4K consumer drone, a lightweight Servo King9000 smartphone, and a UTi260M smartphone-connected infrared thermal camera. In the proposed configuration, the smartphone displayed and recorded the thermal stream, while the onboard RGB camera of the UAV recorded the smartphone-displayed infrared video during flight. The aim was not to develop a radiometric UAV thermal imaging platform, but to determine whether such a low-cost configuration can provide qualitative presence/absence indication of clear thermal hotspots and to identify its operational limits. The system was experimentally assessed under no-payload and payload conditions, daylight and nighttime illumination, and several low-altitude operating heights. Additional motor-region thermal observations were performed using a UTi260T handheld thermal camera under loaded and unloaded operating conditions. The complete UAV-payload configuration had a measured mass of approximately 340 g, corresponding to an effective added payload of 91 g and a payload-to-UAV mass ratio of 36.5%. Payload operation reduced near-ground flight endurance from approximately 25 min to 14 min 40 s. The maximum observed motor-region temperature increased from 24.9 °C under unloaded operation to 42.0 °C under loaded operation, while motor thermal asymmetry increased from 4.8 °C to 7.6 °C. Nighttime and low-glare operation improved the readability of the smartphone-displayed thermal stream, with the most practical usability observed at approximately 10-20 m. The results show that the proposed workflow is feasible only for short-range qualitative thermal screening and clear hotspot presence/absence indication. The UAV-recorded video should not be interpreted as direct thermal data, but as an RGB recording of a smartphone display showing thermal information. Therefore, the workflow is not suitable for quantitative temperature measurement, radiometric thermal mapping, or accurate thermal shape delineation. The main operational limits are payload mass, suspended-load oscillation, display readability, reduced endurance, motor-region thermal loading, sensitivity to payload alignment, and the absence of raw radiometric data. Direct UTi260M smartphone-recorded thermal frames were additionally used for pixel-size-assisted qualitative verification of practical reference thermal targets, including a human-sized target and a vehicle-sized target, at selected low-altitude operating heights.

This cross-sectional study examined the association between smartphone dependence and sport participation among Chinese adolescents and assessed whether self-control, health beliefs, and interpersonal support were statistically related to this association. A total of 1,610 students in Grades 5-9 completed self-report measures of smartphone dependence, sport participation, self-control, health beliefs, and interpersonal support. Structural equation modeling with bootstrapping was used to test a theoretically specified chain mediation model involving self-control and health beliefs. A separate simple moderation analysis using PROCESS Model 1 examined whether interpersonal support moderated the direct association between smartphone dependence and sport participation. Smartphone dependence was negatively associated with sport participation. Bootstrapped structural equation modeling indicated three significant cross-sectional indirect associations: through self-control, through health beliefs, and through the theoretically specified sequence of self-control and health beliefs. In the separate moderation analysis, interpersonal support moderated the direct association between smartphone dependence and sport participation. The negative association was more pronounced among adolescents reporting higher interpersonal support, suggesting that general perceived support may not necessarily function as sport-specific support. The findings identify self-regulatory, health-cognitive, and social-contextual correlates of sport participation in the context of smartphone dependence. Given the cross-sectional design, the results should be interpreted as statistical associations rather than causal or temporal processes. They may inform future longitudinal and intervention studies examining whether self-regulatory skills, sport-related health beliefs, and action-oriented support can promote adolescent sport participation.

The automated classification artificial intelligence (AI) for anterior segment corneal diseases that we developed is capable of classifying images into nine categories, including vision-threatening corneal diseases, such as infectious keratitis; however, it has been trained exclusively on slit-lamp images. We aimed to develop an AI model adaptable to smartphone images by applying transfer learning using smartphone images to the existing AI model. AI trained with transfer learning on smartphone images will be referred to as "Phone-tuned AI." This study included 2530 images captured using smartphones, collected from multiple collaborating facilities between October 2021 and March 2024. Transfer learning was applied to two existing AI models (You Only Look Once version 5 [YOLOv5] and YOLOX) using these smartphone images, and the accuracy of the Phone-tuned AI was evaluated. The average accuracy for each classification using smartphone images was 93.5% for Phone-tuned AI (YOLOv5) and 67.0% for Original AI (YOLOv5), showing a statistically significant improvement (P = 0.0033). Similarly, the accuracy was 84.2% for Phone-tuned AI (YOLOX) and 78.4% for Original AI (YOLOX), with no significant difference (P = 0.36). When diseases were categorized by urgency, the Phone-tuned AI (YOLOv5) achieved 94.8% for urgent, 89.7% for semi-urgent, 89.4% for routine, and 98.7% for observation-level cases. Phone-tuned AI has the potential to assist in diagnosis and triage in regions with a shortage of ophthalmologists, such as rural areas. Transfer learning using smartphone images showed a particularly good fit with YOLOv5, resulting in high diagnostic accuracy and demonstrating strong potential for clinical application.

Academic burnout is a prevalent issue among college students, yet research on its relationship with physical exercise from a person-centered perspective remains limited. This study aimed to identify latent profiles of physical exercise among college students and to examine the mediating roles of self-control and problematic smartphone use in the association between these exercise profiles and academic burnout. A questionnaire survey was conducted among 722 Chinese college students. Latent profile analysis and bootstrap mediation analysis were employed to analyze the data. The results revealed three distinct exercise profiles: occasional exercisers (27.9%), developing exercisers (31.7%), and regular exercisers (40.3%). Significant differences were found across the three profiles in self-control, problematic smartphone use, and academic burnout. Specifically, regular exercisers reported the highest self-control and the lowest levels of problematic smartphone use and academic burnout, followed by developing exercisers, with occasional exercisers showing the least favorable outcomes. Mediation analyses indicated that self-control significantly mediated the relationship between both the developing and regular exercise profiles (compared to occasional exercisers) and academic burnout. Moreover, self-control and problematic smartphone use acted as sequential mediators in these relationships. However, problematic smartphone use alone did not show a significant mediating effect. These findings highlight the heterogeneity in college students' physical exercise patterns and suggest that interventions aimed at reducing academic burnout should consider promoting regular physical exercise, which appears to be associated with higher self-control and subsequently lower problematic smartphone use. Tailored strategies targeting different exercise profiles may be more effective in addressing academic burnout.

This study aimed to validate the Smartphone Addiction Scale-Short Version (SAS-SV) and the Perceived Academic Underachievement Scale (PAUS) for Bangladeshi adolescents and young adults. In addition, the study investigated whether classroom mindful attention has an indirect association with the relationship between smartphone addiction and academic underachievement in the target population. A cross-sectional survey using convenience sampling was conducted with 712 participants recruited from different educational institutions in Bangladesh. Bangla versions of SAS-SV, Classroom Mindful Attention Regulation Scale, and PAUS were administered in collecting data. Factor structure, reliability, and validity of the measures were assessed, and statistical mediation to determine the indirect association was conducted to achieve the study aims. Both SAS-SV and PAUS showed excellent internal consistency (Cronbach's α = 0.95 and 0.91, respectively) and confirmed the predefined factor structures with good model fits (CFI = 0.99, TLI = 0.99, RMSEA = 0.05, SRMR = 0.03; PAUS: CFI = 0.99, TLI = 0.99, RMSEA = 0.04, SRMR = 0.03). While determining the convergent validity of the measures, both smartphone addiction and perceived academic underachievement were associated with lower GPAs and reduced classroom mindful attention. Moreover, the SAS-SV and PAUS were found to be invariably applicable across age, gender, and data collection mode. Mediation analyses indicate that the self-awareness component of classroom mindful attention had an indirect association with the relationship between smartphone addiction and perceived academic underachievement in young adults (B = 0.014, 95% CI [0.001, 0.028], p = .033) and the combined sample (B = 0.020, 95% CI [0.005, 0.036], p = .009), whereas no significant indirect association was observed among adolescents. These findings highlight the associative role of classroom self-awareness in the relationship between smartphone addiction and perceived academic underachievement and underscore the potential role of mindfulness-based interventions in educational settings.

This study aimed to determine whether flagship smartphones can approach the performance of professional digital single-lens reflex (DSLR) cameras using a standardized workflow incorporating color calibration and optical zoom. Three DSLR cameras (Canon EOS 5D Mark IV, Canon EOS 80D, Nikon D610) and two smartphones (iPhone 17 Pro Max, Galaxy S24 Ultra) were used to capture nine standardized extraoral and intraoral views for each of 25 volunteers. Images were evaluated for color accuracy, dimensional accuracy, and image quality. Statistical analyses were conducted using one-way repeated-measures analysis of variance and paired t-tests, with Bonferroni correction applied for multiple comparisons (&#x3b1;&#x2009;=&#x2009;0.05). Gray-card calibration significantly reduced smartphone image &#x394;E values (P&#x2009;<&#x2009;0.001), resulting in lower &#x394;E values than those of the DSLR group with standardized white balance (P&#x2009;<&#x2009;0.001). Regarding dimensional accuracy, images captured with the iPhone 17 Pro Max at 4&#xd7; optical zoom showed no significant difference from DSLR cameras (P&#x2009;=&#x2009;0.178), whereas the Samsung device significantly underestimated arch width (P&#x2009;=&#x2009;0.041). Samsung achieved the most favorable BRISQUE score. Under a standardized workflow incorporating color calibration and appropriate optical zoom, smartphone photography achieved gray-card-based color accuracy and relative dimensional consistency comparable to those of DSLR cameras, providing a more convenient and feasible imaging option. However, DSLR cameras still demonstrated advantages in clinically demanding aesthetic cases. Using a standardized workflow that includes appropriate optical zoom, professional dental lighting, and gray-card-based color calibration, smartphone photography can achieve relatively satisfactory reproduction of dental color and dimensional consistency, representing a potentially reliable and cost-effective option for clinical documentation.

This study aimed to investigate the sex-specific differences in smartphone addiction, physical activity levels, and cognitive functions among university students. A cross-sectional survey was conducted with 256 university students aged 18-25. Participants completed questionnaires using the Smartphone Addiction Scale Short Version to assess their level of smartphone addiction. The working memory and selective attention domains of cognitive function were evaluated, and the International Physical Activity Questionnaire was used to determine participants' self-reported physical activity levels. The results revealed significant sex differences, with male students exhibiting higher levels of smartphone addiction (male = 46.42 &#xb1; 9.37; female = 38.27 &#xb1; 7.63) and greater physical activity (male = 3752 &#xb1; 1876; female = 3447 &#xb1; 1748) than their female counterparts. Additionally, female students demonstrated superior performance on selective attention tasks, including reaction time (female = 463.00 &#xb1; 50.53; male = 457.34 &#xb1; 59.31) and accuracy (female = 92.26 &#xb1; 6.53; male = 89.60 &#xb1; 8.39) across varied conditions, whereas no significant sex differences were observed in working memory or overall reaction time. These findings suggest that sex-specific factors may influence differences between male and female participants in smartphone use, cognitive function, and physical activity.

Equine sports medicine is increasingly oriented toward objective, field-based monitoring systems that support both performance optimization and welfare assessment. In this context, smartphone-based technologies have emerged as accessible tools capable of integrating data acquisition, processing, and interpretation within a single platform. This narrative review aims to examine the role of smartphones in equine sports medicine, focusing on their function as standalone sensing devices and as gateways for wearable and external sensor systems. The analysis is based on a structured synthesis of current literature addressing technological foundations, including embedded sensors, connectivity architectures, and artificial intelligence-driven data processing, as well as their clinical applications across locomotor, cardiovascular, respiratory, behavioural, and thermoregulatory domains. Evidence indicates that smartphone-based systems improve the feasibility of longitudinal monitoring and facilitate real-time decision-making in field conditions, while enhancing communication between veterinarians, trainers, and owners. However, their performance remains influenced by acquisition conditions, system variability, and algorithmic constraints, requiring careful validation and contextual interpretation. In addition, challenges related to data governance, privacy, and ethical use remain insufficiently addressed. Overall, smartphone-based technologies represent enabling tools that support a transition toward more integrated, data-driven, and welfare-oriented management of the equine athlete, while highlighting the need for standardisation and regulatory development.

Smartphone global navigation satellite system (GNSS) positioning is degraded by low-cost antennas, limited receiver hardware, multipath propagation, and noisy code pseudorange observations. Existing correction methods often improve stochastic weighting, estimate coordinate-domain corrections, or smooth receiver trajectories, but they rarely estimate how each satellite contributes to the horizontal position error while preserving line-of-sight (LOS) geometry. This study presents a random-forest-assisted geometry-aware correction method that combines satellite-wise LOS projection error estimation with exponential temporal weighted least squares (Temporal WLS). The horizontal error between the smartphone National Marine Electronics Association (NMEA) solution and the F9P reference position is projected onto each satellite LOS direction and used as the learning target. A random forest model is trained using 26 smartphone GNSS features, including geometry, signal strength, code-derived variation, uncertainty, automatic gain control, and state flags. The predicted LOS errors are fused with satellite geometry through epoch-wise WLS and Temporal WLS. In same-session front-70/back-30 validation, the horizontal root mean square (RMS) error decreased from 2.747 m to 1.033 m. Excluding one suspected non-co-located reference session further reduced the RMS error from 2.867 m to 0.362 m.

To ensure continuous rehabilitation for patients with hemiparesis due to brain disorders, there is a growing need for simple, accessible systems that enable unsupervised self-training. The occurrence of compensatory trunk movements during the unsupervised exercise may prevent true functional recovery. This study proposes a smartphone-based visual-auditory feedback system designed to detect and suppress compensatory trunk movements in real-time without wearable sensors. A pilot cohort (n&#x2009;=&#x2009;16) was first used to calibrate detection thresholds for compensatory trunk movements. Subsequently, a total of 55 hemiparetic patients were enrolled in a randomized controlled trial and allocated to a Feedback (FB) group (n&#x2009;=&#x2009;27) or a Non-Feedback (NFB) group (n&#x2009;=&#x2009;28). Participants performed standardised upper limb rehabilitation tasks using the Rapael Smart Board&#x2122;, a planar upper limb rehabilitation device. The proposed system utilised a smartphone camera with MediaPipe-based pose estimation to track trunk movements and provided real-time traffic-light feedback based on calibrated thresholds. Outcome measures included trunk path length, trunk deviation, task efficiency, and spatial occupancy, which were evaluated using 3D coordinates reconstructed from depth camera data. The FB group demonstrated significantly improved postural stability compared to the NFB group, with a 37.9% reduction in trunk path length (p&#x2009;=&#x2009;0.014) and a 35.3% decrease in spatial occupancy (p&#x2009;=&#x2009;0.003). Kinematic analysis revealed that the NFB group's shorter hand path lengths were achieved through kinematic redundancy-specifically, the recruitment of trunk degrees of freedom-rather than through selective upper limb motor control. In contrast, the FB group maintained a stable posture near the neutral position, ensuring true upper limb engagement. The usability assessment demonstrated that the system was well received by users and was reliable (Cronbach's &#x3b1;= 0.775). The proposed system, which integrates mobile technology, effectively suppresses compensatory trunk movements and promotes selective motor control, and ensures that rehabilitation outcomes reflect true upper limb joint engagement rather than kinematic redundancy through compensatory trunk recruitment. While certain design considerations remain, particularly related to dynamic recalibration and the use of a fixed auditory feedback window, the system retains strong potential as an automated feedback solution, offering a scalable and accessible pathway for high-quality unsupervised home rehabilitation.

Web-based and mobile phone-based apps have become widely available for dietary self-monitoring; however, their use may increase the risk of disordered eating. College students frequently demonstrate poor nutrient intake despite consumption of sufficient calories. One way to improve diet quality may be via the use of a smartphone app that encourages intuitive eating. The purpose of this study was to improve diet quality among college students through the use of a novel smartphone app that promotes intuitive eating rather than calorie counting and weight loss. The In2Eat iOS mobile app was developed in SwiftUI and stored user data in a Firebase database. A total of 45 college students completed assessments of intuitive eating, diet quality, and disordered eating before and after 4 weeks of using the In2Eat app. Users evaluated the usability of the app with the System Usability Scale (SUS). Engagement with the app was recorded as the total number of days a meal was logged, the total number of meals logged, and the average number of meals logged per day. After our 4-week intervention, dietary qualities that protect against chronic disease increased by 28%, fruit consumption increased by 63%, and skin antioxidant levels increased by 6.1% (Hedges g=0.16; mean difference 0.33, 95% bias corrected and accelerated [BCa] CI 0.04-0.61; P=.03). Global intuitive eating did not change during the user study; however, the unconditional permission to eat subscale increased (Hedges g=-0.28; mean difference 0.28, 95% BCa CI 0.07-0.49; P=.01, adjusted P=.07). Overall, disordered eating also did not change with app use, although dietary restraint decreased (Hedges g=-0.23; mean difference 0.30, 95% BCa CI -0.61 to -0.04; P=.04, adjusted P=.22). The average SUS score for the In2Eat app was 67.2 (SD 15.5). The number of days a meal was logged was positively correlated with SUS scores (r=0.28; P=.06), and the total number of meals logged had a monotonic association with app usability (&#x3c1;=0.31; P=.04). When divided according to the low (mean 10.2, SD 5.3), medium (mean 26.3, SD 2.8), and high (mean 33.6, SD 3.8) number of days logging meals, participants with higher days of logging reported the app as more usable (H=6.75; P=.03). A regression analysis showed that 8% of the variance in system usability (R2=0.080; P=.31) was explained by app use; however, none of the individual predictors contributed substantially to the variance. An intuitive eating smartphone app can improve diet quality without increasing disordered eating. Results suggest that participants who logged more meals tended to rate the app as more usable. Further research is needed with a greater sample size after incorporating features to improve the app's usability.

Intermittent and non-invasive glucose monitoring offers remarkable benefits for personalized diabetes management. We present a novel smartphone-integrated near-infrared (NIR) fluorescent microfluidic sensor for efficient sweat collection and fast, reliable detection of glucose. The microfluidic sensor contains four microchambers, each equipped with a bilayer membrane: the upper is sensing layer consisting of polyurethane matrix and non-enzymatic glucose probe PY-POFs, while the lower is light-converting layer consisting of PMMA matrix and up-conversion nanoparticles (UCNPs). Upon 808-nm NIR excitation, the light-converting layer excited the sensing layer via upconversion luminescence, and the glucose-sensitive fluorescence peaking at 470&#xa0;nm is captured with a smartphone. The sensor exhibits a rapid response time of 15&#xa0;s, excellent selectivity and stability due to the non-enzymatic nature, as well as a low detection limit of 20 &#xb5;M because of low-background interference. Moreover, the practical applicability of our device is verified by tracking glucose fluctuations in sweat samples from human volunteers under pre- and post-prandial conditions, with results well correlated to those acquired by commercial blood glucose meters. The prototype demonstrated holds significant promise as a reliable, portable, and user-friendly sensing platform, suitable for rapid intermittent point-of-care non-invasive metabolic monitoring.

Accurate and reliable detection of ascorbic acid (AA) is of paramount significance for clinical diagnosis and health surveillance. Consequently, the rational design of advanced functional materials and the exploration of their potential in AA sensing have emerged as a pivotal research frontier. Herein, a heterometallic EuIII-BiIII cluster-encapsulated antimonotungstate (HDMEA)9 Na18H3{[Bi10Eu2(H2O)4(DMEA)2(H2OA)4W10O36][B-&#x3b1;-SbW9O33]6}&#xb7;124H2O (1, DMEA = N,N-dimethylethanolamine, H2OA = oxalic acid) was successfully synthesized via a multicomponent co-assembly strategy. Notably, 1 exhibits intense fluorescence emission in aqueous media and functions as a highly sensitive fluorescent sensor for AA, achieving a low detection limit of 0.038 &#x3bc;M in a linear range of 40-200 &#x3bc;M. Intriguingly, the aqueous solution of 1 undergoes a distinct color transition from colorless to orange upon the addition of AA. Capitalizing on this chromogenic response, a portable sensing platform was further constructed, which transduces visual color signals into digital readouts through a smartphone-based colorimetric application, realizing quantitative detection of AA with an LOD of 0.45 &#x3bc;M in the linear range of 0-400 &#x3bc;M. Both the 1-based fluorescent sensor and the smartphone-assisted colorimetric assay were successfully validated for the detection of AA in real samples. This work elucidates the unique synergistic interactions between Eu3+ and Bi3+ ions in the fabrication of multicomponent polyoxometalates and highlights the immense potential of polyoxometalate-based materials for applications in environmental monitoring and biomedical sensing.

A new cholic acid-salicylaldehyde conjugate, probe CASA, was facilely constructed through one-step esterification. The probe sensitively and selectively detected both N2H4 and pH, with the salicylaldehyde group as the sensing site. Based on the combined AIE/ESIPT mechanism, the probe exhibited a yellow-green turn-on fluorescent response to N2H4 with a low detection limit of 26&#x2009;nM and a rapid detection time of 10&#x2009;min. Through the ICT process, the probe displayed a light blue turn-on fluorescence signal for neutral pH variations in a linear range of 5.0-7.8 with a pKa value of 6.26. The microstructural attributes of the probe during assembly could elucidate the recognition processes. Probe CASA enabled quantitative analysis of N2H4 and pH in real environmental samples. A smartphone-assisted sensing system and probe-coated portable test strips were developed to facilitate convenient and on-site testing. Additionally, the probe possessed excellent biocompatibility and low cytotoxicity, making it possible to monitor the changes of N2H4 and pH within living cells. This dual-channel N2H4-pH independent detection can prevent spectral crosstalk, facilitate instrument simplification, and provide a more intelligent and reliable analytical toolkit for complex systems. The findings are anticipated to provide more innovative strategies of highly efficient fluorescent probes for multi-analyte detection.

A ratiometric fluorescence platform for sensing arsenate with high-performance has been developed with Cu2+-functionalized Zr metal organic framework (Cu@Zr-MOF) fluorescent nanozyme. The doping of Cu2+ provides the catalytic active site to decompose O2 into &#x2022;OH and 1O2, which oxidizes non-fluorescent substrate o-phenylenediamine (OPD) into product (oxOPD) with yellow fluorescence. When As(V) is present, the intrinsic blue fluorescence of Zr-MOF enhances remarkably by weakening the ligand-to-metal charge transfer (LMCT) accompanied by the fluorescence decrease of oxOPD owing to inner filter effect (IFE). Furthermore, the solutions present a distinguishable color tonality from yellow to blue, which is converted to digital value by smartphone, achieving visual detection of As(V) with a low LOD of 0.67&#xa0;&#x3bc;M. The specific recognition of Zr-O clusters toward As(V) enables this sensor high selectivity. Compared to other MOF-based ratiometric fluorescence assays for As(V), this method is portable and cost-effective owing to the direct coordination of Cu@Zr-MOF nanozyme instead of fragile and high-cost natural enzyme with As(V). This work not only develops a portable method for sensing As(V) but also expands the potential application of functionalized MOF in on-site monitoring.

Motor disturbances are common in neurologic and neurodegenerative syndromes. A standard motor speed and dexterity measure is the finger tapping test (FTT). The FTT has traditionally been administered in clinic using a mechanical FTT, limiting accessibility and early motor change quantification. This study assessed the validity of a smartphone app-based FTT, which may expand access and enable more frequent testing. The cohort was diagnostically diverse, including participants with frontotemporal dementia (FTD), progressive supranuclear palsy (PSP), corticobasal syndrome, primary progressive aphasia, multiple sclerosis, and clinically unimpaired controls. Participants completed a 20-second ALLFTD Mobile App (mApp)-FTT with each hand. Tapping speed metrics were extracted. Participants completed the gold-standard mechanical FTT, a neurologist-administered finger tapping exam, the PSP Rating Scale (PSPRS) and the Unified Parkinson's Disease Rating Scale (UPDRS). Correlations assessed mApp-FTT and mechanical FTT relationships; regressions evaluated associations with neurologist-rated finger tapping impairment, PSPRS and UPDRS, adjusting for age and sex. The mApp-FTT showed moderate-to-strong correlations with the mechanical FTT (dominant: r=0.63, p<0.001; non-dominant: r=0.55, p<0.001). Taps per second were associated with PSPRS motor severity (dominant hand: std. &#x3b2;=-0.59, 95% CI [-0.91, -0.27], p<0.001) and the UPDRS (dominant hand: std. &#x3b2;=-0.41, 95% CI [-0.82, 0.00], p=0.049). Flight time was modestly associated with neurologist-rated finger tapping impairment (dominant hand: std. &#x3b2;=0.15, 95% CI [0.00, 0.29], p=0.044). These findings support mApp-FTT validity as a measure of motor function across neurodegenerative conditions. Validation in longitudinal and unsupervised remote settings is warranted to understand scalability and evaluate change over time.

This research represents an advancement in smartphone-based image acquisition methodology, building upon a previous study to estimate the essential oil content of bergamot fruits in situ using a deep learning approach. To overcome an operational constraint due to a bulky portable dark box to standardise illumination, this study proposes a more versatile solution: a mobile application based on a colour card reference. By replacing physical shielding with digital compensation, the app functions as a local colourimetric sensor, enabling real-time correction of images acquired directly in the orchard, regardless of environmental variables such as direct sunlight or shadows. Workflow relies on an automated calibration procedure. Upon image acquisition, the application utilises ArUco Markers to autonomously detect and extract both the colour card and the fruit surface. The core of the innovation lies in the colour calibration algorithm based on RGB histogram matching logic, which calculates the precise chromatic transformation required to align the field data with the reference card data (acquired under controlled conditions). These calculated parameters are then dynamically mapped onto the fruit's image. The final output is a normalised high-fidelity image, ready for the calculation of chromatic indices, such as the citrus colour index, or for seamless integration into predictive models. The results show that the application is a valid tool for colour calibration, thanks to the good agreement with the values obtained using the inspection chamber. The latter can therefore be replaced by the app, which allows reliable results to be obtained even when used on its own.

Smartphone applications for dermatology are widely available across Europe, yet evidence on their characteristics, validation, and regulatory compliance remains limited. This review, conducted by the European Academy of Dermatology and Venereology (EADV) Artificial Intelligence (AI) Task Force, systematically searched app stores in 44 European countries, identifying 1746 apps in 2024, of which 420 met inclusion criteria. Specifically for AI-based skin cancer screening apps, the search was updated in June 2026. Apps were categorized by function, with metadata extracted on cost, target audience, AI presence, General Data Protection Regulation (GDPR) compliance, and CE (Conformit&#xe9; Europ&#xe9;enne) marking. Most apps (61%) were available on both Google Play and Apple App Store; 94% were free, and 60% targeted laypersons. Twelve percent reported AI functionality in 2024, 41% of these focused on skin cancer diagnosis. Only 2% reported CE marking and 10% GDPR compliance. Scientific validation was limited. Twenty percent of apps linked to peer-reviewed publications, 24 apps had clinical trial evaluations, 5 underwent randomized controlled trials (RCTs), and 6 had real-world post-deployment studies. The 2026 search identified 38 AI-based skin cancer screening apps. Adherence to EADV recommendations was poor. Key principles including explainability, inclusivity, and data sharing were met by &#x2264;&#x2009;37% of apps. Most dermatology apps target laypersons for teledermatology, education, and self-diagnosis. Major gaps exist in regulatory compliance, clinical evidence, and transparency, raising patient safety concerns. These shortcomings raise concerns about reliability and safety, underscoring the need for stronger quality assurance, real-world validation, and inclusion of diverse skin types, in line with EADV AI Task Force recommendations.