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.
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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.
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.
Due to academic pressures and irregular schedules, university students often face challenges in maintaining healthy movement behaviours (including sleep, physical activity, and screen time), which are interrelated and influence both physical and mental health. Smartwatch- and smartphone-based ecological momentary assessments (EMAs) and ecological momentary interventions (EMIs) offer real-time, context-aware strategies to promote movement behaviours. This pilot study aims to assess the feasibility and preliminary effectiveness of a hybrid approach that combines continuous digital monitoring of movement behaviours with sequentially embedded randomised controlled trials (RCTs) evaluating EMIs. MOVE@NUS pilot study employed a five-month hybrid design that combines continuous passive monitoring (primarily via Apple Watches, supplemented by iPhones) with three embedded RCTs targeting sleep (RCT-1), physical activity (RCT-2), and screen time (RCT-3). For each RCT, participants are randomised on a 1:1:1 schedule (control, intervention 1, intervention 2). Eligible participants are first-year undergraduates at the National University of Singapore, aged 18-25 years, who own or regularly use an iPhone and an Apple Watch. EMIs, delivered via the study app, comprise standard health messages or personalised reminders based on HealthKit data or participants' self-reported behaviours and preferences. Self-reported measures include eight EMA bursts (three-day periods every two weeks) and online questionnaires at baseline, midway (2.5 months), and endpoint (5 months). All EMIs and EMAs are text-based and can be completed in under two minutes. Feasibility outcomes include recruitment, engagement, and user experience assessed through quantitative surveys and semi-structured interviews. Preliminary effectiveness will be explored separately for each RCT, comparing movement behaviours between intervention and control groups. Findings from this study will inform the development of scalable and longer-term digital intervention cohorts for promoting healthier lifestyles among university students. Furthermore, as university students soon transition to the workforce, insights gained will inform scalable digital health interventions for broader populations. ClinicalTrials.gov ID NCT06597890 First Posted: 19 September 2024.
Dental photography plays a key role in diagnosis, documentation, and communication in the field of dentistry. The digital single-lens reflex (DSLR) cameras are the gold standard for image quality, but advances in modern smartphone technology necessitate evaluating their performance in resolution, distortion, magnification, color accuracy, and overall image quality. The purpose of this study was to compare DSLR cameras and smartphone cameras regarding resolution, distortion, magnification, color accuracy, and overall image quality in intra- and extraoral dental photography. A Nikon Z5 full-frame DSLR with a 105 mm macro lens and twin flash (control) was compared with three flagship smartphones: iPhone 15 Pro, Google Pixel 8, and Samsung S24. Ten participants were photographed in five standardized dental views: maximum intercuspation, right lateral occlusion, maxillary anterior with black contrastor, mandibular arch (mirror view), and front profile. Images were analyzed using the GNU Image Manipulation Program (GIMP 2.1); statistical analysis was performed with Statistical Package for the Social Sciences (SPSS) 27. The Samsung S24 closely matched the DSLR in shade accuracy and distortion. Google Pixel 8 produced an acceptable resolution (<300 dots per inch). The iPhone 15 Pro demonstrated superior performance in reducing distortion and maintaining clarity (P < 0.05). Despite the convenience and affordability of smartphone cameras, DSLR systems demonstrate superior performance in terms of magnification, image resolution, and color accuracy. These advantages render DSLRs more suitable for clinical applications that demand high precision and diagnostic reliability in dental photography.
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.
Clinical studies have shown that aortic arch pulse-wave velocity (PWVaa), a measure of local aortic stiffness, is a strong independent predictor of subsequent white matter hyperintensity volume and white matter integrity, both associated with cognitive decline, elevated stroke risk, vascular dementia, and neurodegenerative diseases. Total arterial compliance (TAC), a measure of global arterial stiffness, has been recognized as a marker of preclinical vascular disease. This study introduces a smartphone-based method for the noninvasive measurement of PWVaa and TAC using carotid pressure waveforms acquired via smartphone. This method uses intrinsic frequency analysis of smartphone-acquired (iPhone) carotid pressure waveforms to assess PWVaa and TAC. The method was trained, validated, and blind-tested on a cohort of 132 participants aged 20 to 90 years, including both healthy individuals and those with cardiovascular disease, all of whom underwent cardiac magnetic resonance imaging, tonometry, and iPhone waveform measurements. In the blind test set, our method achieved Pearson correlations of 0.81 and 0.80 for PWVaa and TAC, with biases of -0.20 m/s and -0.06 mL/mm Hg and limits of agreement of -4.09 to 3.68 m/s and -0.52 to 0.40 mL/mm Hg, respectively. In the heart failure population, correlations were 0.81 for both, with a PWVaa a bias of -1.07 m/s and TAC bias of -0.06 mL/mm Hg. Our smartphone-based method enables accurate assessment of local and global arterial stiffness metrics (PWVaa and TAC). It offers easy-to-use monitoring of vascular aging and arterial health, with important implications for identifying patients at higher risk of neurodegenerative and cardiovascular diseases. URL: clinicaltrials.org; Unique Identifier: NCT02240979.
In aesthetic clinical trials, image self-capture using mobile devices may help reduce burden on clinic resources, increase data quality, and lower barriers to study participation. This study aimed to develop a mobile device app to help participants self-capture clinically usable images. The Allergan Aesthetic (an AbbVie Company) mobile image app was designed to auto-capture images while directing study participants on distance, head position, and expression to capture a high-quality clinical image. To assess resolution and optimal lighting conditions, images captured using the app in office, at home, and in outdoor settings were compared with those from a Canfield VISIA-CR system (Canfield Scientific). Objective image quality assessment of facial images captured using the app with an iPhone XR (Apple Inc) and iPhone 12 (Apple Inc), compared with images captured using the Canfield VISIA-CR with a digital single-lens reflex camera and the Canfield mobile image capture app with a variety of Android (Google) and iOS (Apple Inc) devices, was conducted using the Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE). Clinical utility was assessed by calculating inter- and intrarater variability for severity ratings of participants' lateral canthal lines (LCL) or forehead lines (FHL) obtained from app-captured images compared with ratings based on in-person evaluations performed by a physician. Usability was assessed according to the ISO (International Organization for Standardization)/IEC (International Electrotechnical Commission) 250101 standard. The Allergan Aesthetic mobile image app was found to perform best under natural light and had image resolution insufficient for assessing minor facial structures, but appropriate for larger structures (eg, FHL). A total of 3968 images were assessed using BRISQUE. Images captured with the Allergan Aesthetic mobile image app had better image quality than those captured using other modalities, as indicated by lower mean BRISQUE scores of 14.05-19.81 compared with Canfield VISIA-CR with a DSLR (34.47) and the Canfield mobile image capture app (23.43). LCL and FHL were rated both in person and digitally in 68 and 71 participants, respectively (median age 52-56 y; 48% to 52% female; 75% to 78% White). Interrater reliability between clinician live evaluations and independent photo review of self-captured photos based on intraclass correlation coefficients (ICCs) was substantial (0.61-0.80) to almost perfect (0.81-1.00) for all raters (LCL: ICC 0.75-0.91 at rest and 0.79-0.89 at maximum contraction; FHL: ICC 0.77-0.93 at rest and 0.70-0.89 at maximum contraction). After 2 iterations of improvements, mean usability ratings of the app experience (out of 5) were as follows: easy to complete=3.2, enjoyable=3.1, satisfied with the level of guidance provided=3.2, and likely to complete a full session without exiting=4.1. The Allergan Aesthetic mobile image app delivers consistent, high-quality images that allow for assessment of LCL and FHL in good agreement with in-person evaluation. Image self-capture using mobile devices may help reduce clinic costs and remove barriers to participation in aesthetic clinical trials.
Poor sleep is common and detrimental to health. Smartphone use is often noted as a sleep disruptor, but evidence remains limited and inconsistent. This necessitates research focused on objective, longitudinal designs, as well as analytical approaches that can reveal lagged and reciprocal relationships that capture within- and between-person effects. To address these gaps, the current study investigated within- and between-person lagged and reciprocal effects of sleep duration and smartphone use of 68 participants through longitudinal and objective data donated from iPhones and Apple Watches across 14 consecutive days. Apple Watches objectively measured total sleep and sleep stage durations (REM, core, and deep sleep), while iPhones assessed total smartphone use duration and in-bed smartphone use. Two Dynamic Structural Equation Models (DSEMs), one with total sleep and one with sleep broken down into three sleep stages, were conducted. At the within-person level, more total smartphone use increased same-day in-bed smartphone use, β = .25 (95% CI .20, .31), which in turn led to more same-day overall sleep, β = .08 (95% CI .02, .14). Additionally, results indicated stable between-person habits, with strong day-to-day associations for each variable with its own next-day value, β = .53-.82 (95% CI .47, .88). Findings contradict the perspective of smartphones as sleep disruptors, despite leaving open whether this added sleep means poorer rest or a real benefit of in-bed smartphone use. Furthermore, the strength of the between-person results emphasizes the importance of habits in this relationship. In studying day-to-day smartphone use and sleep, these findings provide nuanced empirical insights supporting health and policy recommendations regarding smartphone use and sleep hygiene.
This study provides the first independent evaluation of the Apple Over-the-Counter Hearing Aid Feature (OTC-HAF), examining its usability and laboratory performance among adults with self-perceived mild-to-moderate hearing loss. A cross-sectional evaluation was conducted at a university audiology clinic. Digitally literate iPhone users (n = 25, ages 20-72 years) independently used AirPods Pro 2 to complete the Apple Hearing Test Feature and activate the OTC-HAF. The sample size aligns with usability model recommendations for moderately complex systems. Outcomes were assessed immediately after setup. Usability was high, with a mean mHealth App Usability Questionnaire score of 6.7/7 (SD = 0.3) and a mean Hearing Aid Skills and Knowledge Inventory-Clinical score of 93.4% (SD = 9.0%). Study-specific questionnaire responses showed high satisfaction, good sound quality, and ease of use. Qualitative feedback highlighted affordability, convenience, and dual-purpose design, with some noting occlusion and difficulty locating settings. Some participants reported they would only use the device situationally. The audiogram import feature showed limited accuracy: 71% of thresholds were within 5 dB of the reference when both ears were scanned together and 73% when each ear was scanned separately, with 12% and 8% of thresholds missing for these methods, respectively. Objective performance measures showed nonsignificant speech-in-noise benefit (Quick Speech-in-Noise Test mean benefit of 0.1 dB SNR, SD = 1.9), and real-ear measurements showed gain levels generally below National Acoustic Laboratories-Non-Linear 2 targets. The Apple OTC-HAF showed high usability and satisfaction among digitally literate iPhone users, but nonsignificant speech-in-noise benefit and gain levels generally lower than prescriptive targets. Further research should explore broader applicability, long-term outcomes, and strategies to support uptake and consistent use. https://doi.org/10.23641/asha.31366123.
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.
The analysis of electronic evidence from smartphones is essential in modern criminal investigations. This study evaluates the performance of three widely used mobile forensic tools, Cellebrite UFED, MSAB XRY, and Magnet AXIOM, using logical extraction on two iOS and two Android devices, and assesses examiner-perceived usability through the System Usability Scale. On the iPhone 11 Pro, UFED (8539 artifacts) extracted more artifacts than XRY (6542 artifacts) and AXIOM (4220 artifacts). On the iPhone 13 Mini, total artifact counts were UFED (135,024 artifacts), XRY (173,140 artifacts), and AXIOM (355,671 artifacts), with the higher AXIOM volume largely influenced by cache-based and WebKit-related data, including web history (32,614 entries) and SMS artifacts (9101 entries). On the Xiaomi Redmi A3, extraction results were comparable across UFED (285 artifacts), XRY (280 artifacts), and AXIOM (329 artifacts). Greater variation was observed on the Samsung Galaxy A32, with UFED (28,214 artifacts), XRY (15,007 artifacts), and AXIOM (79,088 artifacts), primarily due to differences in artifact classification and provenance rather than disparities in access to core communication data. Usability evaluation showed mean System Usability Scale scores of AXIOM (71.0), UFED (69.2), and XRY (59.7). These findings indicate that artifact volume alone does not necessarily reflect evidentiary value and that forensic tool selection should balance decoding capability, artifact provenance, and usability to ensure reliable and defensible digital evidence analysis.
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.
Limited access to cadavers necessitates the availability of digital resources for anatomy education. Smartphone-based photogrammetry offers a promising solution for creating three-dimensional (3D) and augmented reality (AR) models. This study compared two mobile photogrammetry applications (Qlone and Polycam) that have been used in modern anatomical education. Human cadaveric specimens were prepared and scanned using an iPhone 12 equipped with each application. Initially, a structured qualitative assessment of the applications and their outputs was performed by three experts using a Likert scale, considering image quality, medical utility, and technical factors. After selecting the superior application, diverse anatomical specimens were reconstructed into 3D/AR models. Nine clinical anatomy experts used a Likert scale to rate 20 selected models in four areas: realism, clarity, completeness, and educational value. The comparative analysis indicated that Polycam is significantly superior to Qlone in 3D realism, resolution, shape fidelity, and educational value, despite Qlone's strengths in cost and processing speed. Polycam was then used to create high-fidelity 3D models of complex structures, which were refined and uploaded to a web-based platform. Experts scored the models as "good" to "excellent" in all four evaluation domains, with particularly high scores for anatomical realism in bones and solid organs. In conclusion, the Polycam application is useful for creating high-quality 3D/AR models of human anatomy. These digital resources maintain anatomical accuracy and enable immersive learning, making them an invaluable supplement to traditional dissection in medical schools.
Equine lameness diagnosis largely relies on subjective visual assessments, which can be biased. Although marker-based methods, force plates and inertial measurement units (IMUs) provide objective measurements, they require specialized setups. Vision-based algorithms offer a portable, markerless alternative, but their accuracy needs thorough testing. To evaluate a custom vision-based algorithm for estimating the groundline across multiple camera angles, including handheld use in horses trotting on a treadmill. Experimental comparative study. Eight Standardbred trotter mares were recorded trotting on a high-speed treadmill using seven iPhones positioned at various heights and angles, including a handheld device. A trained deep neural network algorithm placed 2D keypoints on each video frame. Vertical Displacement Signals (VDS) for the eye, withers and croup (tuber sacrale) were computed relative to either an algorithm-estimated or a fixed treadmill groundline. Maximum (Maxdiff) and minimum (Mindiff) stride values were compared using Bland-Altman analysis, scatter plots and histograms. The effect of handheld use on variability and accuracy was assessed by comparing results from a handheld camera to those from a static camera. Groundline estimation closely matched the fixed reference, exhibiting near-zero mean angle error and low mean average error (MAE = 0.45°; n = 242.192). Maxdiff and Mindiff stride-level (n = 36.981) MAE were 0.5 mm, with clinically acceptable additional variability introduced by handheld use at the trial level (Maxdiff and Mindiff MAE < 1.8 mm; n = 357). Treadmill-based data and a single breed/coat colour may limit generalizability to other settings. The vision-based algorithm accurately estimates the groundline and stride VDS parameters from various camera setups, including handheld. Further validation in diverse environments and against other objective gait analysis systems is recommended.
Atrial fibrillation (AF) can lead to significant cardiovascular events and health care utilization. Continuous monitoring via insertable cardiac monitors (ICMs) and data analytics have the potential to improve care delivery. The DEFINE Atrial Fibrillation (DEFINE AFib) study was designed to develop and evaluate novel algorithms using ICM data to predict AF-associated clinical actions (AFCAs) and guide AF management. DEFINE AFib enrolled patients with an ICM (Reveal LINQ/LINQ II; Medtronic) and a history of AF. An Apple iPhone application collected AF-related quality of life (AFEQT) and EQ-5D data. ICM daily AF burden and Apple Watch (AW) irregular rhythm notification (IRN) data were also collected. AFCA was defined as an AF-related procedure or initiation of rate/rhythm control medication. Mutivariable logistic regression was used to identify ICM features in the last 30 days associated with first occurrence of AFCA in the next 30 days in a train and test approach (70%/30%). Among 864 patients (mean 69 ± 10 years; 56% male) meeting inclusion criteria, there were 8963 30-day evaluation windows that included 151 AFCAs. Area under the receiver operating characteristic curve (AUC) was 76% (train) and 70% (test). The model placed participants into high- vs low-risk AFCA groups. At the patient-level, 21% of participants crossing the high-risk threshold for their first time experienced an AFCA at a mean time of 195 ± 164 days compared with 5% in the low-risk group (AUC 65%). Increasing daily mean AF burden was associated with lower quality of life: <6 minutes (reference), 6 minutes to 5.5 hours (AFEQT -7.69; P < .001), and 5.5-12 hours (AFETQ -13.97, P < .001; ≥5.5 hours EQ-5D -0.03, P = .007). In a subanalysis of individuals with smart watch data (n = 53), the ICM model signaled high risk before AFCAs 85% of the time (AUC 57%) compared with 23% for AW IRN (P = .005; AUC 55%). DEFINE AFib transformed ICM diagnostic data to predict risk of AFCA with good discrimination, particularly compared with wearable data. These results highlight the potential advantages of ICM-based continuous monitoring for AF management and the utility of ICM prediction models that could help inform pre-emptive therapeutic strategies.
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.
Accurate evaluation of gingival health requires understanding inflammation as a sign of disease activity or healing. Only a few studies have examined the gingiva in depth using non-invasive imaging techniques. Therefore, this study assessed the accuracy of evaluating gingival parameters using professional (Canon EOS 1300D, Canon Inc., Japan) and smartphone (iPhone 15 Pro, Apple Inc., USA) cameras, with pre- and post-treatment photographs. Thirty-four patients with gingivitis were selected, and photographs were captured using professional and smartphone cameras. Gingival parameters were examined using pictures of the maxillary anterior region taken at distances of 24, 28, and 32 cm from the examination site. All the images were evaluated using the free ImageJ software (US National Institutes of Health, Bethesda, Maryland, USA) with an accuracy of 0.01 mm. The paired t-test was used to compare gingival color values and clinical measurements. A P value of<0.05 was considered statistically significant. There were no significant differences in gingival parameters between images taken using either professional or smartphone photography. The results showed no significant differences in gingival color assessment at 24-, 28-, and 32-cm distances between the two images. Digital images obtained with DSLR and smartphone cameras showed comparable accuracy for gingival parameter measurements at the tested distances.
To evaluate and compare the accuracy, defined as trueness and precision, and the acquisition time of digital impressions for edentulous full-arch implant position transfer obtained using conventional scan bodies and a novel smartphone-based photogrammetry system. A maxillary edentulous model with four multi-unit abutment replicas was scanned using two acquisition methods: (1) an intraoral scanner with conventional scan bodies and (2) a smartphone-based photogrammetry system (PIC APP, iPhone 16 Pro). Each workflow was repeated 20 times. A high-accuracy industrial scanner served as the reference dataset. Accuracy was assessed using centroid root mean square deviation (CRMS), body root mean square deviation (BRMS), and angular deviation. Acquisition time was also recorded. Statistical comparisons were performed using the Mann-Whitney U test (α = 0.05). Smartphone-based photogrammetry demonstrated significantly lower CRMS trueness (54.8 ± 6.57 µm) compared with conventional scan body-based intraoral scanning (244.13 ± 162.66 µm, p < 0.001). BRMS values were also significantly lower for the photogrammetry workflow (31.70 ± 14.15 µm vs. 67.62 ± 40.52 µm, p < 0.001). Angular deviation was slightly greater with photogrammetry (0.54° ± 0.03 vs. 0.39° ± 0.13, p = 0.001), although both remained within clinically acceptable limits. Acquisition time was longer for the smartphone-based workflow (97 vs. 50 seconds, p < 0.001). Within the limitations of this in vitro study and with reference to the intraoral scanning workflow evaluated, the smartphone-based photogrammetry system demonstrated trueness and precision values compatible with the requirements for full-arch implant position transfer accuracy. Compared with conventional scan body-based intraoral scanning, the system showed reduced linear deviations and greater consistency in implant position transfer under controlled experimental conditions. Smartphone-based photogrammetry may represent a promising digital approach for implant position acquisition in full-arch rehabilitations. Its use of widely available mobile hardware and reduced system complexity suggests potential advantages in accessibility compared with conventional photogrammetry systems. However, the clinical performance, economic impact, and workflow efficiency of this technology must be confirmed with prospective clinical trials and dedicated cost-benefit analyses before definitive clinical recommendations can be made.