To describe a novel, low-cost, and high-quality intraoperative video recording setup for oculoplastic surgery using a magnetic neck-mounted smartphone holder and iPhone with the Final Cut Camera app. A magnetic neck mount (Tianzhu Insta 360, China) and an iPhone 16 Pro Max were adapted for surgical recording by securing the holder around the binocular base of the operating microscope. The magnetically attached phone provided a stable and adjustable platform, allowing intraoperative view adjustments via the microscope's handles or foot pedal without compromising sterility. The Final Cut Camera app enabled 4K video capture with manual control of focus, exposure, and white balance. Its Live Multicam feature allowed simultaneous multi-angle recording and real-time monitoring via a compatible iPad, enabling an assistant to control framing and clarity intraoperatively. A multi-port adapter was used to support continuous power and external memory. Over six months, more than 50 oculoplastic procedures, including dacryocystorhinostomy, orbitotomy, and eyelid surgeries, were successfully recorded using this setup. All recordings were stable, centered, and of high image quality. Screenshots captured during surgery clearly depicted anatomical structures under standard operating room lighting. This smartphone-based recording method offers a simple, cost-effective, and ergonomically practical alternative to traditional surgical video systems in oculoplastic procedures. Its adaptability, ease of use, and compatibility with sterile environments make it an ideal solution for surgical documentation and teaching, especially in resource-limited settings.
To validate a custom smartphone application for at-home visual acuity (VA) measurement in children. A total of 452 children aged 3-17.5 years participated. Certified examiners measured in-office test-retest VA (logMAR) using gold-standard Amblyopia Treatment Study HOTV (3-to-6-year-olds, younger cohort) or electronic Early Treatment of Diabetic Retinopathy Study (7-to-17.5-year-olds, older cohort) protocols at 3-4.5 m and app-based VA at 1.5 m. Caregivers measured at-home app-based VA at 1.5 m. Comparing at-home app-based with gold-standard VA, in eyes 20/40 or better, 95% (143/151) and 93% (91/98) of the younger and older cohorts were within 2 lines, respectively (mean differences: younger = -0.03, older = -0.04; 95% limits-of-agreement half-width (LOA): younger = ±0.26, older = ±0.22). In eyes 20/50 or worse, 66% (42/64) and 75% (76/101) of the younger and older cohorts were within 2 lines, respectively (mean differences: younger = 0.11, older = 0.13, LOA: younger = ±0.50, older = ±0.51). Comparing in-office app-based VA with gold-standard VA, in eyes 20/40 or better, 98% (160/164) and 94% (99/105) of the younger and older cohorts were within 2 lines, respectively (mean differences: younger = -0.03, older = -0.03; LOA: younger = ±0.22; older = ±0.24). In eyes 20/50 or worse, 85% (60/71) and 91% (101/111) of the younger and older cohorts were within 2 lines, respectively (mean differences: younger = 0.04; older = 0.04; LOA: younger = ±0.39; older = ±0.24). For gold-standard test-retest, in eyes 20/40 or better, 99% (163/164) and 99% (104/105) of the younger and older cohorts had retest within 2 lines, respectively (mean differences: younger = 0.00; older = 0.01; LOA: younger = ±0.17; older = ±0.11). For 20/50 or worse, 92% (66/72) and 100% (111/111) in the younger and older cohorts were within 2 lines, respectively (mean differences: younger = 0.01; older = 0.02; LOA: younger = ±0.35; older = ±0.15). Our app demonstrated good concordance with the gold standard at home and in the office for eyes with VA of 20/40 or better. However, concordance decreased considerably for eyes with VA 20/50 or worse, particularly at home.
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To evaluate the accuracy, consistency, and efficiency of a novel digital measurement method for blepharoptosis grading utilizing iPhone photography and built-in markup tools, compared to the traditional ruler method. In this prospective, blinded methodological comparison study, 145 patients (185 eyes) with blepharoptosis were enrolled. Each patient underwent marginal reflex distance 1 (MRD1) measurement via both the traditional ruler method (control) and the iPhone method (experimental). The iPhone method involved capturing standardized photographs and performing measurements using the device's native markup feature. Agreement between methods was assessed using the Intraclass Correlation Coefficient (ICC) and Bland-Altman analysis. Grading consistency was evaluated using the Kappa statistic, and operational efficiency was compared via independent t-tests. The mean MRD1 values were 2.18 ± 0.94 mm (ruler) and 2.22 ± 0.89 mm (iPhone), with no statistically significant difference (P = .188). The 2 methods demonstrated excellent agreement, with an ICC of 0.968 (95% CI: 0.956-0.977). Bland-Altman analysis showed a mean bias of -0.04 mm, with 95% limits of agreement from -0.51 mm to 0.43 mm. Clinical grading consistency was almost perfect (Kappa = 0.892). The iPhone method was significantly more efficient, requiring less time than the traditional method (64.8 ± 18.7 s vs 86.5 ± 22.3 s, P < .001). The smartphone-based digital measurement method proves to be an accurate, reliable, and efficient alternative to the traditional ruler method for quantifying MRD1 and grading blepharoptosis. It demonstrates high agreement with conventional techniques while offering advantages in standardization, traceability, and operational workflow. This approach holds significant potential for enhancing diagnostic precision in clinical practice, particularly in outpatient and primary care settings.
The risk of genitourinary toxicity during radiotherapy for prostate cancer was found to be lower for bladder volumes ≥200 ml. An app that reminds patients daily to drink water might be helpful. Before being investigated in patients, an app should be tested in healthy volunteers. Thirty healthy volunteers were included in this prospective study and asked to test the app and affirm (=satisfaction) or negate nine statements. These statements belonged to the sections 'Download and installation' (two statements), 'Navigation' (two statements), or 'Content/functions' (five statements). If a satisfaction rate was <60%, the app was to be considered not useful. If it was <80%, the app needed optimization. iPhone users (n=18) were compared to Android users (n=12). Satisfaction rates (participants affirming a statement) were 90.0% (27 out of 30 participants) and 86.7% (26 out of 30 participants) regarding the two statements belonging to the Download and installation section. Regarding the two statements of the Navigation section, satisfaction rates were 100% (28 out of 28) and 96.6% (28 out of 29), respectively. For the Content/functions section, satisfaction rates were 79.3% (23 out of 29 participants) for the statement: "The app reminded me at the selected times". For the other four statements, satisfaction rates were each 100% (25 out of 25, 27 out of 27, 28 out of 28, and 29 out of 29 participants). Significant differences between iPhone and Android users were not observed. When looking at the subgroups of iPhone and Android users, two additional aspects were identified that needed modifications. Although the new reminder app was mainly rated usable, some modifications were required. Our findings highlight that a pre-study in healthy volunteers is important.
We present a low-cost, fully reproducible software and hardware protocol for smartphone sensor battery cost tests. Our pipeline combines a rigorous hardware checklist and light-sealed enclosure, a software checklist for iOS devices, and a BatteryTest app to control sensor configurations and log battery state during tests. Methodologically, we applied this standardized protocol in 30 independent analyzed test runs using six iPhone 14 Pro and three iPhone 13 Pro devices, and compared battery-life outcomes across predefined sensor conditions (idle, TrueDepth, GPS, accelerometer, pedometer, gyroscope, and rear camera), sampling rates, and sensor-specific settings. Key findings include: (i) Baseline battery life was approximately 10% higher on the 14 Pro versus the 13 Pro models under idle conditions. Sensor activation substantially reduced battery life, with GPS and camera usage exhibiting the strongest impact. (ii) Software parameters matter: the sampling rate change from 27 s to 3 s led to significantly decreased battery life in several scenarios, while reducing the location accuracy in GPS tests increased battery life by up to 20 h on the 13 Pro devices. (iii) Cross-device-generation consistency is heterogeneous. The iPhone 14 Pro lasts up to 50% longer on GPS tests, yet drains about an hour faster than the 13 Pro in camera tests. This work introduces the first standardized, and fully reproducible protocol for quantifying sensor-specific battery consumption on iPhones, enabling consistent, comparable, and low-cost energy benchmarking across device generations.
Gram staining provides rapid microbiological information that may assist in empirical antimicrobial selection; however, the results are often interpreted by microbiological specialists who are not always available. Therefore, we developed a computer-aided diagnosis system using artificial intelligence trained on microscopic images of Gram-stained urine, captured with an iPhone, using the Bartholomew and Mittwer method. The system interprets Gram-stained urine samples and classifies bacterial morphology (Class 1: 7 predefined morphology categories) and 17 predefined species-level categories (Class 2). In this retrospective observational study, five imaging devices and two staining methods (Bartholomew and Mittwer, Favor) were compared. Urine specimens were collected from two hospitals between 1 April and 31 December 2022. Validation images were generated using five devices (four smartphones and one microscopic camera). We used a micrometer with microscopy with all smartphones; some iPhone images were taken without a micrometer. Favor staining was only imaged using an iPhone without the micrometer. Image data sets were generated from 433 clinical and 17 spiked samples. The overall accuracy was 0.804 for Class 1 and 0.640 for Class 2. Images taken by the microscopic camera had the highest accuracy and kappa coefficient, whereas the AQUOS smartphone had the lowest accuracy and kappa coefficient. The accuracy of images created without a micrometer was 0.885 for Class 1 and 0.666 for Class 2. The Bartholomew and Mittwer method had better accuracy and a better kappa coefficient. Overall, accuracy depended on the staining method used in the training data, not on the imaging device.IMPORTANCEGram staining provides rapid information on both the site of infection and likely pathogens, guiding empirical antimicrobial selection. However, interpretation requires infectious disease expertise, which is not always available. We developed an artificial intelligence-based diagnostic support system trained on iPhone images of Gram-stained urine using the Bartholomew and Mittwer method to classify bacterial morphology (Class 1) and inferred species (Class 2). To provide essential baseline data on factors influencing accuracy and reliability, we compared Gram-stained urine images from two hospitals obtained with five imaging devices and two staining methods. Microscopic camera images showed the highest accuracy, whereas an AQUOS smartphone showed the lowest. Images without a micrometer performed better, and the Bartholomew and Mittwer method outperformed the Favor method. Accuracy increased when confidence levels were higher. Our findings suggest that using the same staining method as the training data and avoiding micrometer noise are critical, while device differences are less influential.
Cardiac auscultation is an essential component of clinical examination but is often challenging to achieve proficiency in. Self-contained, multisensory learning resources that incorporate simultaneous visual and haptic stimuli offer a unique approach to supporting learners in acquiring this core skill. This pilot study of both medical students and clinical educators evaluated the utility of a novel iPhone app, Haptic Heart, which generates haptic vibrations to simulate heart sounds and murmurs. We aimed to explore the perceptions of students and educators when using haptics as a learning resource and the underlying reasons behind these perceptions and to gather lessons that would inform future development of the resource. Clinical-year medical students from the Lincoln Medical School with access to an iPhone were invited to trial Haptic Heart between October 2023 and December 2024. Cardiology specialists involved in clinical education were also invited to take part. After using the app, participants were asked to complete a modified version of the 12-item Evaluation of Technology-Enhanced Learning Materials: Learner Perceptions questionnaire that included additional free-text items. Educators were also asked to comment on the resource's authenticity and perceived usefulness. Quantitative responses were analyzed using descriptive statistics; free-text responses were analyzed for common themes. A total of 21 students and 18 educators completed the evaluation. Both cohorts returned positive responses across nearly all questionnaire items, with students showing near universal agreement that the app was of excellent quality (21/21, 100%), supported their learning needs (21/21, 100%), and would change their clinical practice (20/21, 95.2%). Educators similarly rated the resource highly for learning utility (16/18, 88.9%) and authenticity (13/18, 72.2%). Reported technical difficulties were minimal for students (1/21, 4.8%) and educators (2/18, 11.1%). Analysis of free-text responses suggested that learners valued the ability to "feel" murmurs and to vary heart rate. Educators highlighted the resource's novelty and innovation, although some noted concerns about audio quality when using a stethoscope to auscultate haptic vibrations directly. This pilot evaluation demonstrates the potential of smartphone-based haptic technology as a tool for medical education. Haptic Heart was perceived by both students and educators as an innovative educational tool for cardiac auscultation. Further work should focus on expanding the range of haptic patterns provided and exploring the effectiveness of these resources on learning.
Lymphedema, a chronic and incurable condition with limited therapeutic options, has limited options to quantitatively assess functional changes during its development; as a result, a deeper understanding of its pathophysiology remains hindered. To characterize lymphatic alterations and their association with disease pathology in a clinically relevant model in the rat, we developed a longitudinal iPhone-based volumetry method combined with non-invasive NIR analysis of lymphatic function. Secondary lymphedema was induced by surgery and single-dose irradiation. iPhone volumetry provided longitudinal measurements of hindlimb volume, while NIR imaging quantified the pumping function of major lymphatic vessels in the popliteal area. Among 30 rats, lymphedema developed in 80%, defined as interlimb volume differences exceeding 5% and persisting through 14 days. In all rats with lymphedema, disease persisted until the end of the study at postoperative day 42 (P = 0.0015). NIR imaging revealed lymphatic dilation, dye extravasation, and lymphangiogenesis in affected limbs. Lymphatics in limbs with lymphedema exhibited increased contraction frequency, reduced amplitude, and diminished transport compared to baseline and contralateral controls (all P < 0.05). In contrast, rats that did no develop lymphedema showed no postoperative functional changes, although at baseline they displayed higher frequency and lower amplitude and transport compared with LE rats (all P < 0.001). Baseline transport values correlated negatively with swelling (r = -0.44, P = 0.002), as determined by ROC analysis, which yielded an AUC of 0.83, a sensitivity of 83.3%, and a specificity of 82.6%. Histopathology at day 42 confirmed significant dermal thickening and fat deposition in LE limbs (P < 0.001 and P = 0.002, respectively). Longitudinal volumetry and NIR imaging applied to a clinically relevant animal model suggest a strong association between swelling and lymphatic function, which could provide deeper insight into lymphedema pathophysiology and represent valuable tools for future research and therapeutic development.
This study aimed to assess the diagnostic accuracy of digital intraoral photographs obtained using smartphones and a macro camera in evaluating oral health among adults. A total of 200 adult patients underwent clinical and radiographic examinations using the Decayed, Filled Teeth (DFT) Index, Caries Assessment Spectrum and Treatment (CAST) Index, Plaque Index (PI), and Modified Gingival Index (MGI). Intraoral photographs were taken using three devices: Samsung S23 Ultra, iPhone 14 Pro, and Canon EOS 400D with macro lens. Following the clinical recording of DFT, CAST, PI, and MGI scores by two calibrated examiners as the reference standard, intraoral photographs were captured by a third dentist and independently evaluated by two separate blinded examiners to compare the diagnostic accuracy of the devices against the clinical findings. Non-parametric analyses were conducted using the Friedman test with Dunn's post hoc test, Wilcoxon test and agreement between clinical and photographic methods was evaluated via the Bland-Altman method (p < 0.05). The macro camera demonstrated the highest inter-rater reliability for FT scores (ICC = 0.886), while iPhone-derived MGI scores showed the lowest reliability (ICC = 0.624). Statistically significant differences were found among all imaging devices for all indices (p < 0.001), except for MGI. Bland-Altman analysis showed that most values fell within the 95% limits of agreement, indicating good concordance with clinical data. Smartphone and macro camera photographs provided comparable diagnostic results for caries and restorations. However, limitations remain in the assessment of periodontal parameters via photographic methods. Smartphone-based intraoral photography can serve as a practical diagnostic tool in teledentistry.
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 (α = 0.05). Gray-card calibration significantly reduced smartphone image ΔE values (P < 0.001), resulting in lower ΔE values than those of the DSLR group with standardized white balance (P < 0.001). Regarding dimensional accuracy, images captured with the iPhone 17 Pro Max at 4× optical zoom showed no significant difference from DSLR cameras (P = 0.178), whereas the Samsung device significantly underestimated arch width (P = 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.
BACKGROUND: Tinnitus is a complex condition with significant heterogeneity in its presentation, and its risk factors remain poorly characterized, posing challenges for prevention, diagnosis, and treatment. This study aimed to assess the prevalence, characteristics, and risk factors of tinnitus in the United States (U.S.) using large-scale survey data. METHODS: We conducted a cross-sectional analysis of 125,252 volunteer adults (≥ 18 years) enrolled in the Apple Hearing Study, a nationwide app-based cohort of iPhone users in the U.S. (November 2019–November 2022). The outcomes were the weighted prevalence of any tinnitus and bothersome tinnitus, measured using self-reported tinnitus frequency, duration, awareness, loudness, and interference with hearing. Age-adjusted and multivariable logistic regression models were applied to analyze the odds ratios of self-reported potential risk factors on tinnitus, and a weighted decision tree identified the strongest predictors of bothersome tinnitus. RESULTS: The estimated weighted national prevalence of any tinnitus was 30.8% (95% Confidence Interval [CI]: [30.3%, 31.2%]) and bothersome tinnitus was 11.6% (95% CI: [11.3%, 11.9%]). Controlling for age, sex, race/ethnicity, and other sociodemographic characteristics, self-rated hearing ability was the strongest risk factor for any tinnitus (odds ratios of 4.52 [95% CI: 4.03–5.06] and bothersome tinnitus 8.88 [95% CI: 7.52–10.49], comparing poor to excellent hearing). The odds of both types of tinnitus increased with age, peaking in the 60–64 age group (2.01 [95% CI: 1.77–2.28] for any tinnitus and 2.72 [95% CI: 2.24–3.92] for bothersome tinnitus) after adjusting for the same set of variables. Non-Hispanic Whites had higher odds of any and bothersome tinnitus compared to other race/ethnicities. A reported history of occupational noise exposure was associated with higher odds of any and bothersome tinnitus. CONCLUSIONS: Approximately 3 in 10 U.S. adults are estimated to experience any tinnitus, and about 1 in 10 affected by bothersome tinnitus. Tinnitus is associated with worse self-rated hearing ability, age, race/ethnicity, and a history of workplace noise. These results align with prior epidemiological estimates and demonstrate the feasibility of using app-based platforms to collect large-scale, high-quality hearing health data.
Decentralized clinical trials using direct-to-participant recruitment can potentially engage large, representative participant pools. The objective of the study was to share insights on multichannel strategies for participant recruitment in the decentralized Heartline Study, a randomized trial testing the impact of a mobile application-based heart health program with the electrocardiogram and Irregular Rhythm Notification features on an Apple Watch for early diagnosis, treatment, and outcomes of atrial fibrillation. Eligible participants were U.S. adults aged ≥65 years with an iPhone and Medicare coverage. Multiple pathways for broad outreach were explored, including digital (eg, email, social media) and traditional channels (eg, direct mail, community outreach). Recruitment efforts were assessed and refined throughout the study to maximize reach. Across multiple channels, 321,272 Heartline Study applications were installed, with 34,244 participants (11%) completing enrollment (February 2020-December 2022) and 82% (28,155/34,244) completing baseline demographic assessments. Women accounted for 54.2% (15,258/28,155) of study participants; 93.0% identified as White (26,192/28,155), 2.8% Asian (781/28,155), 2.7% Black (747/28,155), and 2.5% Hispanic (699/28,155). Broad geographic representation throughout the United States was achieved. The Heartline Study demonstrated the ability to recruit large numbers of participants aged ≥65 years. A direct-to-participant approach across multiple channels achieved excellent gender and geographic diversity, enrolling a higher percentage of women than typical cardiology trials and participation from rural areas. However, less racial and ethnic representation was achieved, highlighting the need for additional strategies to meet this goal. Future trials may consider such multichannel recruitment approaches to support decentralized clinical trials. (A Study to Investigate if Early Atrial Fibrillation [AF] Diagnosis Reduces Risk of Events Like Stroke in the Real-World; NCT04276441).
Establishing an appropriate occlusal vertical dimension (OVD) is a critical and challenging step in complete mouth rehabilitation, particularly in patients with severe tooth wear or edentulism. Although craniometric equations have been proposed to estimate OVD from facial measurements, their applicability to 3-dimensional (3D) facial scans within digital workflows remains unclear. The purpose of this retrospective clinical study was to determine whether OVD estimated from 3D facial scans using a craniometric equation is clinically equivalent to OVD measured directly. Facial anthropometric measurements and OVD were obtained both clinically and from 3D facial scans acquired with 3 different devices (Artec Spider scanner; Artec 3D, Einstar; Shining 3D, Qlone on iPhone 13 Pro; Apple Inc) in 23 dentate participants. Based on these facial measurements, OVD was also estimated for each modality (clinical and scanned) using a craniometric equation. Equivalence was assessed with the two 1-sided t test (TOST) procedure using a predefined clinical equivalence margin of ±2 mm. Equivalence was not observed between equation-based OVD derived from scan measurements and clinically measured OVD for any device. Scan-derived equation-based OVD values systematically differed from clinical measurements by -3.1 to -3.6 mm, exceeding the predefined equivalence margin. Estimation of OVD from 3D facial scans using a craniometric equation cannot be considered clinically precise and should not replace conventional clinical methods for determining OVD.
Communication applications offer healthcare professionals a simple, instantaneous and direct form of connection within a healthcare team. This cross-sectional survey aimed to examine current usage patterns among Australian medical practitioners and assess their understanding of data security and ethical considerations. Among 151 respondents, most of whom were anaesthetists (77%) practising in metropolitan centres (82%) across South Australia, Queensland, and New South Wales, the Apple iPhone was the most popular device type (77%) while WhatsApp was the preferred application for facilitating patient-based discussions (75%). Doctors sent an average of one to 10 patient-related messages per week, although junior doctors were significantly more likely to exceed this number. The majority (66%) of doctors felt comfortable sharing non-identifying patient information, in comparison to 32% who felt comfortable sharing identifying patient information. Participants demonstrated inconsistent understanding of consent and documentation requirements when transmitting patient data. These findings highlight the routine use of communication applications in Australian hospitals and emphasise the need for greater clinician education on privacy obligations and the development of clear, ethical guidelines for their use in healthcare.
This study aimed to evaluate the diagnostic accuracy of the smartphone Weber test compared with the traditional tuning fork Weber test (TFWT) in patients with unilateral conductive and sensorineural hearing loss, and to determine whether the smartphone-based method can serve as a practical screening tool in clinical and remote settings. This prospective study was conducted at the Otorhinolaryngology Clinic of Ankara Bilkent City Hospital between April 2023 and March 2024. A total of 150 participants were enrolled, including patients with conductive hearing loss (n = 50), sensorineural hearing loss (n = 50), and healthy controls (n = 50). Both the TFWT and a smartphone vibration test (iPhone 13, 512 Hz equivalent) were performed. The smartphone vibration was validated for functional comparability, rather than acoustic identity, using accelerometer measurements. Diagnostic performance was evaluated by accuracy, sensitivity, specificity, Cohen's Kappa coefficient, and receiver operating characteristic analysis with 95% confidence intervals (CIs). A binary logistic regression identified independent predictors of test accuracy. The smartphone Weber test demonstrated an overall accuracy of 83.3% (95% CI: 76.8-89.7%), comparable to the TFWT (84.0% [95% CI: 78.1-90.2%]). Agreement between tests was "moderate" (κ = 0.488, 95% CI: 0.39-0.57, p < 0.001). The smartphone test showed an area under the curve of 0.74 (95% CI: 0.67-0.81), while the TFWT had 0.78 (95% CI: 0.71-0.84), with no significant difference between them (p = 0.34). Logistic regression identified interaural difference as the only independent predictor of correct smartphone test results (p = 0.009). The smartphone Weber test shows moderate diagnostic accuracy and comparable, but not statistically superior, performance to the traditional tuning fork. While not a replacement for audiometry, it may serve as a convenient preliminary screening tool in primary care, emergency, and telemedicine settings. Further multicenter research should standardize calibration, improve specificity, and validate its clinical utility across devices and populations.
High-fidelity 3D reconstruction and precise phenotypic parameter extraction of banana plants are critical for crop growth monitoring and yield estimation in precision agriculture. However, traditional methods encounter significant bottlenecks: LiDAR systems are cost-prohibitive for widespread adoption, while traditional photogrammetry often fails to handle the complex canopy structures, severe occlusions, and weak texture features characteristic of banana leaves. To address these limitations, this article proposes a novel framework for 3D reconstruction and automatic phenotyping based on multi-view images captured by mobile phones. We introduce BN-NeRF, an enhanced Neural Radiance Field method built upon Instant-NGP. Specifically, we integrate three key technical improvements: (1) frame-level geometric calibration to correct camera pose drift caused by handheld motion; (2) sparse geometric anchoring to explicitly constrain depth and scale using sparse point clouds; and (3) thin-leaf prior regularization to suppress artifacts and improve the geometric accuracy of leaf surfaces. Building on this reconstruction, we establish a complete pipeline to recover explicit metric geometry from implicit radiance fields. By combining mesh topological analysis with geodesic algorithms, we achieve automated and precise extraction of key morphological parameters. Extensive experiments were conducted on a dataset of 90 banana plants in a real-world orchard. The results demonstrate that BN-NeRF achieves superior rendering quality (PSNR of 32.4 dB, SSIM of 0.951, and LPIPS of 0.152) while maintaining inference speeds comparable to Instant-NGP. Furthermore, the extracted phenotypic parameters showed strong agreement with manual ground truth across both leaf-level and structural traits. In addition to trait-specific regression performance, the evaluation also includes normalized completeness analysis, calibration-cube-based scale validation, and Bland-Altman agreement analysis, supporting the measurement reliability of BN-NeRF for field phenotyping. This study demonstrates that low-cost smartphone-based acquisition, combined with BN-NeRF, can support accurate field phenotyping of banana plants. In addition, an implemented mobile-cloud system was functionally validated through repeated end-to-end runs on an iPhone 13 client and a cloud workstation.
Difficulty understanding speech in noisy environments is a primary challenge of hearing impairment, inadequately addressed by hearing aids alone. While auditory training can enhance selective attention and speech perception, current digital programs face poor user adherence and lack realistic 3D spatial audio. This pilot study evaluated the feasibility, usability, and preliminary efficacy of ARIA (Augmented Reality Immersive Auditory training), a handheld mobile intervention that provides gamified at-home auditory training to middle-aged adults via earbud-delivered spatial audio. In this single-arm, pre-post-follow-up pilot study, 11 adults (mean age 53.0, SD 3.0 y) with functional hearing not requiring amplification completed a 4-week at-home training program using ARIA on provided devices (iPhone 14 Pro, AirPods Pro 2). Speech-in-noise perception was assessed via the Korean Matrix Sentence Test at baseline, 4 weeks, and 8 weeks at 3 signal-to-noise ratios (SNRs; 0 dB, -6 dB, and -9 dB, respectively). Feasibility, usability (System Usability Scale), user experience (Player Experience of Need Satisfaction), in-game performance, and qualitative feedback were collected. Protocol completion was 100% (11/11), demonstrating technical feasibility. Exploratory efficacy analyses revealed statistically significant speech-in-noise improvements posttraining across all conditions (0 dB: t10=3.43, P=.02; -6 dB: t10=5.34, P<.001; -9 dB: t10=4.34, P=.004). Gains were maintained at the 8-week follow-up. In-game localization improvements correlated significantly with speech perception gains at -6 dB SNR (ρ=0.639; P=.03) and -9 dB SNR (ρ=0.612; P=.045). User experience showed mixed results: the mean System Usability Scale score was 70.2 (SD 19.6; range 47.5-92.5), reflecting substantial individual differences in usability perception. While 72% (n=8) reported difficulties with the augmented reality (AR) environmental setup, 63% reported genuine mastery-driven engagement with core gameplay. Thematic analysis revealed a dissociation between peripheral usability challenges (setup friction, "homework" characterization due to protocol structure) and successful engagement with the training paradigm itself. This pilot demonstrated the feasibility of AR-based audio-motor training for at-home delivery and revealed encouraging preliminary efficacy signals, warranting progression to controlled efficacy trials. Formative findings identified specific usability refinements needed for broader implementation, particularly streamlining AR setup while preserving the core gameplay elements that successfully fostered competence and engagement. These insights provide clear guidance for platform optimization and randomized controlled trial design.
This paper describes ASTER, a novel procedure for collecting Screen Time data from iOS, iPadOS, watchOS, and devices using their built-in Apple Screen Time features. Traditional methods of studying digital behavior often rely on self-reported data, which are prone to inaccuracies (i.e., recall bias), limiting their validity and granularity. While Android devices have long facilitated real-time and granular behavior tracking through third-party applications, similar tools are not available on Apple devices due to Apple's restrictions. This is problematic because there are significant differences between the user populations of iOS and Android. To address this gap, this study developed a data donation procedure that leverages the synchronization of screentime, enabling the extraction of comprehensive usage data of iPhones, iPads, Apple Watches, and Macs linked to the same Apple ID. The process involves donating system-level files used to generate Screen Time metrics on Mac, containing anonymized use data of all linked devices. We developed a tool that enables researchers to process these files into a usable dataset (e.g., JSON). This dataset provides granular insights into app usage without requiring substantial technical expertise or financial investment. While this approach enables the integration of Apple cross-device behavior into digital media research, it is limited to users with a Mac and can only capture data from the previous 4 weeks. Additionally, the method is vulnerable to changes in Apple's software structure, echoing the moving target problem. Nonetheless, this method marks an important step forward in current approaches to the passive sensing of smartphone behavior.
Aim and objectives Accurate shade selection is critical for achieving optimal esthetic outcomes in restorative dentistry. Digital photography has emerged as an alternative to conventional visual shade matching; however, the accuracy of smartphone (SP) cameras compared with digital single-lens reflex (DSLR) cameras remains uncertain. This study aimed to compare Commission Internationale de l'Éclairage L*a*b* (CIELAB) color coordinates obtained from SP and DSLR images with manufacturer-provided values of the VITA 3D-Master shade guide (VITA Zahnfabrik, Bad Säckingen, Germany) and to evaluate the reliability of smartphone photography for shade selection. Methods An in vitro study was conducted using a commercial VITA 3D-Master shade guide. Images of 26 shade tabs were captured using an iPhone 13 smartphone (Apple Inc., Cupertino, CA, USA) and a Canon EOS 700D DSLR (Canon Inc., Tokyo, Japan). Two images per shade tab were obtained under standardized daylight conditions (4000-5000 K) at a fixed distance of 18 cm against a neutral gray background, yielding a total of 104 images. Image selection was standardized based on predefined criteria of focus and exposure consistency. CIELAB color values (L*, a*, b*) were extracted using digital image analysis software. Color differences (ΔE) between photographic values and manufacturer reference values were calculated using the CIEDE2000 formula. Statistical analysis was performed using the Wilcoxon signed-rank test, with significance set at P < 0.05. Results Statistically significant differences were observed between SP and DSLR images in deviations of L*, a*, and b* values (P < 0.05). DSLR images demonstrated greater color accuracy, achieving 75% agreement with manufacturer reference values, whereas SP images showed 55% agreement. Mean ΔE values were lower for DSLR images, indicating improved color fidelity. Conclusions Within the limitations of this in vitro study, DSLR photography demonstrated greater accuracy in shade selection compared to smartphone photography. Although smartphones may serve as an accessible adjunct, they currently exhibit lower color accuracy than DSLR systems for shade determination.