BACKGROUND: Health care technology has the ability to change patient outcomes for the betterment when designed appropriately. Automation is becoming smarter and is increasingly being integrated into health care work systems.
OBJECTIVE: This study focuses on investigating trust between patients and an automated cardiac risk assessment tool (CRAT) in a simulated emergency department setting.
METHODS: A within-subjects experimental study was performed to investigate differences in automation modes for the CRAT: (1) no automation, (2) automation only, and (3) semiautomation. Participants were asked to enter their simulated symptoms for each scenario into the CRAT as instructed by the experimenter, and they would automatically be classified as high, medium, or low risk depending on the symptoms entered. Participants were asked to provide their trust ratings for each combination of risk classification and automation mode on a scale of 1 to 10 (1=absolutely no trust and 10=complete trust).
RESULTS: Results from this study indicate that the participants significantly trusted the semiautomation condition more compared to the automation-only condition (P=.002), and they trusted the no automation condition significantly more than the automation-only condition (P=.03). Additionally, participants significantly trusted the CRAT more in the high-severity scenario compared to the medium-severity scenario (P=.004).
CONCLUSIONS: The findings from this study emphasize the importance of the human component of automation when designing automated technology in health care systems. Automation and artificially intelligent systems are becoming more prevalent in health care systems, and this work emphasizes the need to consider the human element when designing automation into care delivery.

BACKGROUND: Adolescence is a critical developmental period to prevent and treat the emergence of mental health problems. Smartphone-based conversational agents can deliver psychologically driven intervention and support, thus increasing psychological well-being over time.
OBJECTIVE: The objective of the study was to test the potential of an automated conversational agent named Kai.ai to deliver a self-help program based on Acceptance Commitment Therapy tools for adolescents, aimed to increase their well-being.
METHODS: Participants were 10,387 adolescents, aged 14-18 years, who used Kai.ai on one of the top messaging apps (eg, iMessage and WhatsApp). Users\' well-being levels were assessed between 2 and 5 times using the 5-item World Health Organization Well-being Index questionnaire over their engagement with the service.
RESULTS: Users engaged with the conversational agent an average of 45.39 (SD 46.77) days. The average well-being score at time point 1 was 39.28 (SD 18.17), indicating that, on average, users experienced reduced well-being. Latent growth curve modeling indicated that participants\' well-being significantly increased over time (β=2.49; P<.001) and reached a clinically acceptable well-being average score (above 50).
CONCLUSIONS: Mobile-based conversational agents have the potential to deliver engaging and effective Acceptance Commitment Therapy interventions.

UNASSIGNED: The elastic scattering spectroscopy (ESS) device (DermaSensor Inc., Miami, FL) is a noninvasive, painless, adjunctive tool for skin cancer detection.
UNASSIGNED: To investigate the performance of the ESS device in the detection of melanoma.
UNASSIGNED: A prospective, investigator-blinded, multicenter study was conducted at 8 United States (US) and 2 Australian sites. All eligible skin lesions were clinically concerning for melanoma, examined with the ESS device, subsequently biopsied according to dermatologists\' standard of care, and evaluated with histopathology. A total of 311 participants with 440 lesions were enrolled, including 44 melanomas (63.6% in situ and 36.4% invasive) and 44 severely dysplastic nevi.
UNASSIGNED: The observed sensitivity of the ESS device for melanoma detection was 95.5% (95% CI, 84.5% to 98.8%, 42 of 44 melanomas), and the observed specificity was 32.5% (95% CI, 27.2% to 38.3%). The positive and negative predictive values were 16.0% and 98.1%, respectively.
UNASSIGNED: The device was tested in a high-risk population with lesions selected for biopsy based on clinical and dermoscopic assessments of board-certified dermatologists. Most enrolled lesions were pigmented.
UNASSIGNED: The ESS device\'s high sensitivity and NPV for the detection of melanoma suggest the device may be a useful adjunctive, point-of-care tool for melanoma detection.

To evaluate deep learning-based calcium segmentation and quantification on ECG-gated cardiac CT scans compared with manual evaluation. Automated calcium quantification was performed using a neural network based on mask regions with convolutional neural networks (R-CNNs) for multi-organ segmentation. Manual evaluation of calcium was carried out using proprietary software. This is a retrospective study of archived data. This study used 40 patients to train the segmentation model and 110 patients were used for the validation of the algorithm. The Pearson correlation coefficient between the reference actual and the computed predictive scores shows high level of correlation (0.84; P < .001) and high limits of agreement (±1.96 SD; -2000, 2000) in Bland-Altman plot analysis. The proposed method correctly classifies the risk group in 75.2% and classifies the subjects in the same group. In total, 81% of the predictive scores lie in the same categories and only seven patients out of 110 were more than one category off. For the presence/absence of coronary artery calcifications, the deep learning model achieved a sensitivity of 90% and a specificity of 94%. Fully automated model shows good correlation compared with reference standards. Automating process reduces evaluation time and optimizes clinical calcium scoring without additional resources.

BACKGROUND: Smart bathroom technology offers unrivaled opportunities for the automated measurement of a range of biomarkers and other data. Unfortunately, efforts in this area are mostly driven by a technology push rather than market pull approach, which decreases the chances of successful adoption. As yet, little is known about the use cases, barriers, and desires that potential users of smart bathrooms perceive.
OBJECTIVE: This study aimed to investigate how participants from the general population experience using a smart sensor-equipped toilet seat installed in their home. The study contributes to answering the following questions: What use cases do citizens see for this innovation? and What are the limitations and barriers to its everyday use that they see, including concerns regarding privacy, the lack of fit with everyday practices, and unmet expectations for user experience?
METHODS: Overall, 31 participants from 30 households participated in a study consisting of 3 (partially overlapping) stages: sensitizing, in which participants filled out questionnaires to trigger their thoughts about smart bathroom use and personal health; provotyping, in which participants received a gentle provocation in the form of a smart toilet seat, which they used for 2 weeks; and discussion, in which participants took part in a web-based focus group session to discuss their experiences.
RESULTS: Participants mostly found the everyday use of the toilet, including installation and dismantling when necessary, to be relatively easy and free of complications. Where complications occurred, participants mentioned issues related to the design of the prototype, technology, or mismatches with normal practices in using toilets and hygiene. A broad range of use cases were mentioned, ranging from signaling potentially detrimental health conditions or exacerbations of existing conditions to documenting physical data to measuring biomarkers to inform a diagnosis and behavioral change. Participants differed greatly in whether they let others use, or even know about, the seat. Ownership and control over their own data were essential for most participants.
CONCLUSIONS: This study showed that participants felt that a smart toilet seat could be acceptable and effective, as long as it fits everyday practices concerning toilet use and hygiene. The range of potential uses for a smart toilet seat is broad, as long as privacy and control over disclosure and data are warranted.

BACKGROUND: The surveillance of hospital-acquired infections in Germany is usually conducted via manual chart review; this, however, proves resource intensive and is prone to a certain degree of subjectivity. Documentation based on electronic routine data may present an alternative to manual methods. We compared the data derived via manual chart review to that which was derived from electronic routine data.
METHODS: Data used for the analyses was obtained from five of the University of Leipzig Medical Center\'s (ULMC) ICUs. Clinical data was collected according to the Protection against Infection Act (IfSG); documentation thereof was carried out in hospital information systems (HIS) as well as in the ICU-KISS module provided by the National Reference Center for the Surveillance of Nosocomial Infections (NRZ). Algorithmically derived data was generated via an algorithm developed in the EFFECT study; ward-movement data was linked with microbiological test results, generating a data set that allows for evaluation as to whether or not an infection was ICU-acquired.
RESULTS: Approximately 75% of MDRO cases and 85% of cases of sepsis/primary bacteremia were classified as ICU-acquired by both manual chart review and EFFECT. Most discrepancies between the manual and algorithmic approaches were due to differentiating definitions regarding the patients\' time at risk for acquiring MDRO/bacteremia.
CONCLUSIONS: The concordance between manual chart review and algorithmically generated data was considerable. This study shows that hospital infection surveillance based on electronically generated routine data may be a worthwhile and sustainable alternative to manual chart review.

OBJECTIVE: Image fusion merges preoperative computed tomography angiography (CTA) with live fluoroscopy during endovascular procedures to function as an overlay 3D roadmap. However, in most current systems, the registration between imaging modalities is performed manually by vertebral column matching which can be subjective, inaccurate and time consuming depending on experience. Our objective was to evaluate feasibility and accuracy of image-based automated 2D-3D image fusion between preoperative CTA and intraoperative fluoroscopy based on vertebral column matching.
METHODS: A single-center study with offline procedure data was conducted in 10 consecutive patients which had endovascular aortic repair in which we evaluated unreleased automated fusion software provided by Philips (Best, the Netherlands). Fluoroscopy and digital subtraction angiography images were collected after the procedures and the vertebral column was fused fully automatically. Primary endpoints were feasibility and accuracy of bone alignment (mm). Secondary endpoint was vascular alignment (mm) between the lowest renal artery orifices. Clinical non-inferiority was defined at a mismatch of < 1 mm.
RESULTS: In total, 87 automated measurements and 40 manual measurements were performed on vertebrae T12-L5 in all 10 patients. Manual correction was needed in 3 of the 10 patients due to incomplete visibility of the vertebral edges in the fluoroscopy image. Median difference between automated fusion and manual fusion was 0.1 mm for bone alignment (p = 0.94). The vascular alignment was 4.9 mm (0.7-17.5 mm) for manual and 5.5 mm (1.0-14.0 mm) for automated fusion. This did not improve, due to the presence of stiff wires and stent graft.
CONCLUSIONS: Automated image fusion was feasible when all vertebral edges were visible. Accuracy was non-inferior to manual image fusion regarding bone alignment. Future developments should focus on intraoperative image-based correction of vascular alignment.

Imaging-based total kidney volume (TKV) and total liver volume (TLV) are major prognostic factors in autosomal dominant polycystic kidney disease (ADPKD) and end points for clinical trials. However, volumetry is time consuming and reader dependent in clinical practice. Our aim was to develop a fully automated method for joint kidney and liver segmentation in magnetic resonance imaging (MRI) and to evaluate its performance in a multisequence, multicenter setting.
The convolutional neural network was trained on a large multicenter dataset consisting of 992 MRI scans of 327 patients. Manual segmentation delivered ground-truth labels. The model\'s performance was evaluated in a separate test dataset of 93 patients (350 MRI scans) as well as a heterogeneous external dataset of 831 MRI scans from 323 patients.
The segmentation model yielded excellent performance, achieving a median per study Dice coefficient of 0.92-0.97 for the kidneys and 0.96 for the liver. Automatically computed TKV correlated highly with manual measurements (intraclass correlation coefficient [ICC]: 0.996-0.999) with low bias and high precision (-0.2%±4% for axial images and 0.5%±4% for coronal images). TLV estimation showed an ICC of 0.999 and bias/precision of -0.5%±3%. For the external dataset, the automated TKV demonstrated bias and precision of -1%±7%.
Our deep learning model enabled accurate segmentation of kidneys and liver and objective assessment of TKV and TLV. Importantly, this approach was validated with axial and coronal MRI scans from 40 different scanners, making implementation in clinical routine care feasible.Clinical Trial registry name and registration number: The German ADPKD Tolvaptan Treatment Registry (AD[H]PKD), NCT02497521.

Visually-guided laser balloon ablation (VGLA) currently requires careful manual rotation of the laser to create overlapping lesions. A novel semi-automated VGLA may reduce ablation times and lesion gaps. We aimed to compare semi-automated (SA) VGLA to that of manual (MN) VGLA.
Acute: Nine swine underwent right superior pulmonary vein isolation (PVI) using either SA (n = 3, 13-18 W), MN (n = 3, 8.5-12 W), or radiofrequency (RF, n = 3, 25-40 W) and were killed acutely. Chronic: 16 swine, underwent PVI using either SA (n = 8, 15 W) or MN (n = 8, 10 W), and were survived for 1 month before being killed. All hearts were then submitted for pathological evaluation.
Acute: PVI was successful in all 9/9 swine with lesion counts significantly lower in the SA arm (5.3 ± 5.9, 33.7 ± 10.0, and 28.0 ± 4.4 in SA, MN, and RF arms; p = .007 for SA and MN). At necropsy, circumferentiality and transmurality were 98% and 94% in SA, 98% and 80% in MN, and 100% and 100% in RF arms. A single steam pop was noted on sectioning in the SA arm swine and occurred in the high dose (18 W) strategy. Chronic: PVI was acutely successful in 16/16 swine with no difference in PVI durability rates (62.5% vs. 75.0%), lesion transmurality (95.8 ± 17.4% vs.91.9 ± 25.9%), and circumferentiality (95.8 ± 6.6% vs. 94.8 ± 6.3%) between SA and MN arms. Catheter use time and lesion counts were lower in the SA arm compared to the MN arm (11.5 ± 12.7 vs. 21.8 ± 3.8 min, p = .046 and 4.8 ± 3.83 vs. 35.4 ± 4.4, p < .001).
Motor-assisted semi-automated laser balloon ablation can improve upon procedural efficiency by reducing ablation time.

Access to evidence-based psychological treatment is a challenge worldwide. We assessed the effectiveness of a fully automated aviophobia smartphone app treatment delivered in combination with a $5 virtual reality (VR) viewer.
In total, 153 participants from the Dutch general population with aviophobia symptoms and smartphone access were randomized in a single-blind randomized controlled trial to either an automated VR cognitive behavior therapy (VR-CBT) app treatment condition (n = 77) or a wait-list control condition (n = 76). The VR-CBT app was delivered over a 6-week period in the participants\' natural environment. Online self-report assessments were completed at baseline, post-treatment, at 3-month and at 12-month follow-up. The primary outcome measure was the Flight Anxiety Situations Questionnaire (FAS). Analyses were based on intent-to-treat.
A significant reduction of aviophobia symptoms at post-test for the VR-CBT app compared with the control condition [p < 0.001; d = 0. 98 (95% CI 0.65-1.32)] was demonstrated. The dropout rate was 21%. Results were maintained at 3-month follow-up [within-group d = 1.14 (95% CI 0.46-1.81)] and at 12-month follow-up [within-group d = 1.12 (95% CI 0.46-1.79)]. Six participants reported adverse effects of cyber sickness symptoms.
This study is the first to show that fully automated mobile VR-CBT therapy delivered in a natural setting can maintain long-term effectiveness in reducing aviophobia symptoms. In doing so, it offers an accessible and scalable evidence-based treatment solution that can be applied globally at a fraction of the cost of current treatment alternatives.