Oral mucosal lesions triage via YOLOv7 models.

BACKGROUND/PURPOSE: The global incidence of lip and oral cavity cancer continues to rise, necessitating improved early detection methods. This study leverages the capabilities of computer vision and deep learning to enhance the early detection and classification of oral mucosal lesions. METHODS: A dataset initially consisting of 6903 white-light macroscopic images collected from 2006 to 2013 was expanded to over 50,000 images to train the YOLOv7 deep learning model. Lesions were categorized into three referral grades: benign (green), potentially malignant (yellow), and malignant (red), facilitating efficient triage. RESULTS: The YOLOv7 models, particularly the YOLOv7-E6, demonstrated high precision and recall across all lesion categories. The YOLOv7-D6 model excelled at identifying malignant lesions with notable precision, recall, and F1 scores. Enhancements, including the integration of coordinate attention in the YOLOv7-D6-CA model, significantly improved the accuracy of lesion classification. CONCLUSION: The study underscores the robust comparison of various YOLOv7 model configurations in the classification to triage oral lesions. The overall results highlight the potential of deep learning models to contribute to the early detection of oral cancers, offering valuable tools for both clinical settings and remote screening applications.

Integrating factors associated with complex wound healing into a mobile application: Findings from a cohort study.

Complex, chronic or hard-to-heal wounds are a prevalent health problem worldwide, with significant physical, psychological and social consequences. This study aims to identify factors associated with the healing process of these wounds and develop a mobile application for wound care that incorporates these factors. A prospective multicentre cohort study was conducted in nine health units in Portugal, involving data collection through a mobile application by nurses from April to October 2022. The study followed 46 patients with 57 wounds for up to 5 weeks, conducting six evaluations. Healing time was the main outcome measure, analysed using the Mann-Whitney test and three Cox regression models to calculate risk ratios. The study sample comprised various wound types, with pressure ulcers being the most common (61.4%), followed by venous leg ulcers (17.5%) and diabetic foot ulcers (8.8%). Factors that were found to impair the wound healing process included chronic kidney disease (U = 13.50; p = 0.046), obesity (U = 18.0; p = 0.021), non-adherence to treatment (U = 1.0; p = 0.029) and interference of the wound with daily routines (U = 11.0; p = 0.028). Risk factors for delayed healing over time were identified as bone involvement (RR 3.91; p < 0.001), presence of odour (RR 3.36; p = 0.007), presence of neuropathy (RR 2.49; p = 0.002), use of anti-inflammatory drugs (RR 2.45; p = 0.011), stalled wound (RR 2.26; p = 0.022), greater width (RR 2.03; p = 0.002), greater depth (RR 1.72; p = 0.036) and a high score on the healing scale (RR 1.21; p = 0.001). Integrating the identified risk factors for delayed healing into the assessment of patients and incorporating them into a mobile application can enhance decision-making in wound care.

Development and testing of "Is Lung Cancer Screening for You?" A computer-based decision aid.

PURPOSE: To reduce lung cancer mortality, individuals at high-risk should receive a low-dose computed tomography screening annually. To increase the likelihood of screening, interventions that promote shared decision-making are needed. The goal of this study was to investigate the feasibility, acceptability, usability, and preliminary effectiveness of a computer-based decision aid. METHODS: Thirty-three participants were recruited through primary-care clinics in a small southeastern-US city. Participants used a computer-based decision aid ("Is Lung Cancer Screening for You?") during a clinic appointment. Paper surveys collected self-reported feasibility, acceptability, and usability data. A research coordinator was present to observe each patient's and health-care provider's interactions, and to assess the fidelity of shared decision-making. RESULTS: The decision aid was feasible, acceptable for use in a clinic setting, and easy for participants to use. Patients had low decisional conflict following use of the decision aid and had high screening intention and actual screening rates. Shared decision-making discussions using the decision aid were nearly 6 min on average. CONCLUSION: Computer-based decision aids are feasible for promoting shared lung cancer-screening decisions. A more robust study is warranted to measure the added value of a computer-based version of this aid versus a paper-based aid.

Computer-Assisted Clinical Diagnosis and Treatment.

PURPOSE OF REVIEW: Computer-assisted diagnosis and treatment (CAD/CAT) is a rapidly growing field of medicine that uses computer technology and telehealth to aid in the diagnosis and treatment of various diseases. The purpose of this paper is to provide a review on computer-assisted diagnosis and treatment. This technology gives providers access to diagnostic tools and treatment options so that they can make more informed decisions leading to improved patient outcomes. RECENT FINDINGS: CAD/CAT has expanded in allergy and immunology in the form of digital tools that enable remote patient monitoring such as digital inhalers, pulmonary function tests, and E-diaries. By incorporating this information into electronic medical records (EMRs), providers can use this information to make the best, evidence-based diagnosis and to recommend treatment that is likely to be most effective. A major benefit of CAD/CAT is that by analyzing large amounts of data, tailored recommendations can be made to improve patient outcomes and reduce the risk of adverse events. Machine learning can assist with medical data acquisition, feature extraction, interpretation, and decision support. It is important to note that this technology is not meant to replace human professionals. Instead, it is designed to assist healthcare professionals to better diagnose and treat patients.

Nanoencapsulation of unsaturated omega-3 fatty acids as protection against oxidation: A systematic review and data-mining.

The chemical structure of unsaturated fatty acids makes them highly prone to oxidation, which decreases their nutritional properties. Nanocarriers have the ability to protect unstable nutraceuticals and take them to their specific targets. Thus, the aim is to determine the effectiveness of nanoencapsulation of omega-3 unsaturated fatty acids as protection against oxidation, as well as to apply data-mining approach to identify nanoencapsulation profiles. Three databases were used to search for studies focused on comparing omega-3 encapsulation to the active compound in its raw form. Studies without oxidation test or no use omega 3-rich oil as active ingredient in nanoformulations were excluded. Twenty-three studies were included in the systematic review. The qualitative analysis indicated that the main evaluated parameters were encapsulation efficiency (%), physical-chemical parameters and oxidation (analyzed at different storage temperatures), oil type, and whether the formulation was added to food. With regard to quantitative analysis, studies that did not perform oxidation tests focused on comparing free oil to the encapsulated one were excluded. Data-mining indicated that encapsulation efficiency and particle size were the main characteristic defining nanocarrier's effectiveness in protecting the oil against oxidation. Nevertheless, it is important to note the main characteristics associated with oil protection in nanocarriers.

Assisted decision-making and interprofessional collaboration in the care of older people: a qualitative study exploring perceptions of barriers and facilitators in the acute hospital setting.

In recent years, there has been a move toward a more human rights-based approach to the issue of supported and assisted decision-making (ADM) with legislative changes strengthening the formal right for older people to participate in care planning and decision-making. Ireland's Assisted Decision-Making (Capacity) Act, 2015 breaks from traditional views of capacity to consider the uniqueness of each decision in relation to topic, time and place for those with impaired or fluctuating capacity. This study set out to explore experiences of assisted decision making (ADM) in acute care hospitals in Ireland and to identify the barriers and enablers to ADM for older people and people with dementia from the perspective of different Health and Social Care Professionals (HSCPs) involved in their care. We carried out 26 semi-structured audio-recorded interviews with a convenience sample of HSCPs working in two acute hospitals and subsequently confirmed the results. HSCPs identified several barriers to, and enablers of, ADM in acute hospitals that were categorized into three key themes: Building meaningful engagement with older people and their family carers; barriers and enablers associated with interprofessional collaboration and barriers and enablers associated with the environment. Our findings suggest that despite concrete policy and legislative underpinnings to ADM, this was not always evident in practice and suggests the need for specialized education and training on ADM in practice settings.

SLIC-supervoxels-based response evaluation of osteosarcoma treated with neoadjuvant chemotherapy using multi-parametric MR imaging.

OBJECTIVE: Histopathological examination (HPE) is the current gold standard for assessing chemotherapy response to tumor, but it is possible only after surgery. The purpose of the study was to develop a noninvasive, imaging-based robust method to delineate, visualize, and quantify the proportions of necrosis and viable tissue present within the tumor along with peritumoral edema before and after neoadjuvant chemotherapy (NACT) and to evaluate treatment response with correlation to HPE necrosis after surgery. METHODS: The MRI dataset of 30 patients (N = 30; male:female = 24:6; age = 17.6 ± 2.7 years) with osteosarcoma was acquired using 1.5 T Philips Achieva MRI scanner before (baseline) and after 3 cycles of NACT (follow-up). After NACT, all patients underwent surgical resection followed by HPE. Simple linear iterative clustering supervoxels and Otsu multithresholding were combined to develop the proposed method-SLICs+MTh-to subsegment and quantify viable and nonviable regions within tumor using multiparametric MRI. Manually drawn ground-truth ROIs and SLICs+MTh-based segmentation of tumor, edema, and necrosis were compared using Jacquard index (JI), Dice coefficient (DC), precision (P), and recall (R). Postcontrast T1W images (PC-T1W) were used to validate the SLICs+MTh-based necrosis. SLICs+MTh-based necrosis volume at follow-up was compared with HPE necrosis using paired t test (p ≤ 0.05). RESULTS: Active tumor, necrosis, and edema were segmented with moderate to satisfactory accuracy (JI = 62-78%; DC = 72-87%; P = 67-87%; R = 63-88%). Qualitatively and quantitatively (DC = 74 ± 9%), the SLICs+MTh-based necrosis area correlated well with the hypointense necrosis areas in PC-T1W. No significant difference (paired t test, p = 0.26; Bland-Altman plot, bias = 2.47) between SLICs+MTh-based necrosis at follow-up and HPE necrosis was observed. CONCLUSION: The proposed multiparametric MRI-based SLICs+MTh method performs noninvasive assessment of NACT response in osteosarcoma that may improve cancer treatment monitoring, planning, and overall prognosis. KEY POINTS: • The simple linear iterative clustering supervoxels and Otsu multithresholding-based technique (SLICs+MTh) successfully estimates the proportion of necrosis, viable tumor, and edema in osteosarcoma in the course of chemotherapy. • The proposed technique is noninvasive and uses multiparametric MRI to measure necrosis as an indication of anticancer treatment response. • SLICs+MTh-based necrosis was in satisfactory agreement with histological necrosis after surgery.

Tracking cognitive impairment in multiple sclerosis using the Brain on Track test: a validation study.

BACKGROUND: The accurate and regular monitoring cognitive performance in multiple sclerosis (MS) patients is critical to develop new prevention and management strategies for cognitive impairment (CI). The Brain on Track (BoT) test is a self-administered web-based tool developed for cognitive screening and monitoring. The objective of this study was to validate the use of the BoT in MS, by assessing its ability to distinguish between MS patients and matched controls, as well as detect CI among MS patients, by analysing its correlation with standard cognitive tests and its reliability and learning effects in repeatable use. METHODS: The BoT was applied in 30 patients with MS consecutively selected and 30 age- and education-matched controls, first in a hospital clinic, under supervision, and then 1 week later from home. After these first two trials, MS patients repeated the test from home every 4 weeks for 3 months. A standard neuropsychological battery was also applied to MS patients at baseline. RESULTS: The Cronbach's alpha was 0.89. Test scores were significantly different between MS patients and controls (Cohen's d = 0.87; p < 0.01). Among MS patients, scores were significantly lower in those with CI documented in the standard neuropsychological battery than in their cognitively preserved counterparts (Cohen's d = 2.0; p < 0.001). The BoT scores presented a good correlation with standard neuropsychological tests, particularly for information processing speed. Regarding test-retest reliability, 10/11 subtests presented two-way mixed single intraclass consistency correlation coefficients > 0.70. CONCLUSION: The BoT showed good neuropsychological parameters in MS patients, endorsing the use of self-administered computerized tests in this setting.

Application of an Artificial Intelligence Trilogy to Accelerate Processing of Suspected Patients With SARS-CoV-2 at a Smart Quarantine Station: Observational Study.

BACKGROUND: As the COVID-19 epidemic increases in severity, the burden of quarantine stations outside emergency departments (EDs) at hospitals is increasing daily. To address the high screening workload at quarantine stations, all staff members with medical licenses are required to work shifts in these stations. Therefore, it is necessary to simplify the workflow and decision-making process for physicians and surgeons from all subspecialties. OBJECTIVE: The aim of this paper is to demonstrate how the National Cheng Kung University Hospital artificial intelligence (AI) trilogy of diversion to a smart quarantine station, AI-assisted image interpretation, and a built-in clinical decision-making algorithm improves medical care and reduces quarantine processing times. METHODS: This observational study on the emerging COVID-19 pandemic included 643 patients. An "AI trilogy" of diversion to a smart quarantine station, AI-assisted image interpretation, and a built-in clinical decision-making algorithm on a tablet computer was applied to shorten the quarantine survey process and reduce processing time during the COVID-19 pandemic. RESULTS: The use of the AI trilogy facilitated the processing of suspected cases of COVID-19 with or without symptoms; also, travel, occupation, contact, and clustering histories were obtained with the tablet computer device. A separate AI-mode function that could quickly recognize pulmonary infiltrates on chest x-rays was merged into the smart clinical assisting system (SCAS), and this model was subsequently trained with COVID-19 pneumonia cases from the GitHub open source data set. The detection rates for posteroanterior and anteroposterior chest x-rays were 55/59 (93%) and 5/11 (45%), respectively. The SCAS algorithm was continuously adjusted based on updates to the Taiwan Centers for Disease Control public safety guidelines for faster clinical decision making. Our ex vivo study demonstrated the efficiency of disinfecting the tablet computer surface by wiping it twice with 75% alcohol sanitizer. To further analyze the impact of the AI application in the quarantine station, we subdivided the station group into groups with or without AI. Compared with the conventional ED (n=281), the survey time at the quarantine station (n=1520) was significantly shortened; the median survey time at the ED was 153 minutes (95% CI 108.5-205.0), vs 35 minutes at the quarantine station (95% CI 24-56; P<.001). Furthermore, the use of the AI application in the quarantine station reduced the survey time in the quarantine station; the median survey time without AI was 101 minutes (95% CI 40-153), vs 34 minutes (95% CI 24-53) with AI in the quarantine station (P<.001). CONCLUSIONS: The AI trilogy improved our medical care workflow by shortening the quarantine survey process and reducing the processing time, which is especially important during an emerging infectious disease epidemic.

Development and initial psychometric evaluation of the computer-based prostate Cancer screening decision aid acceptance scale for African-American men.

BACKGROUND: To reliably evaluate the acceptance and use of computer-based prostate cancer decision aids (CBDAs) for African-American men, culturally relevant measures are needed. This study describes the development and initial psychometric evaluation of the 24-item Computer-Based Prostate Cancer Screening Decision Aid Acceptance Scale among 357 African-American men. METHODS: Exploratory factor analysis (EFA) with maximum likelihood estimation and polychoric correlations followed by Promax and Varimax rotations. RESULTS: EFA yielded three factors: Technology Use Expectancy and Intention (16 items), Technology Use Anxiety (5 items), and Technology Use Self-Efficacy (3 items) with good to excellent internal consistency reliability at .95, .90, and .85, respectively. The standardized root mean square residual (0.035) indicated the factor structure explained most of the correlations. CONCLUSIONS: Findings suggest the three-factor, 24-item Computer-Based Prostate Cancer Screening Decision Aid Acceptance Scale has utility in determining the acceptance and use of CBDAs among African-American men at risk for prostate cancer. Future research is needed to confirm this factor structure among socio-demographically diverse African-Americans.

[A computerised clinical decision-support system for the management of depression in Primary Care]. / Un sistema informatizado de apoyo a las decisiones clínicas para el manejo de la depresión en atención primaria.

Despite its clinical relevance and its importance as a public health problem, there are major gaps in the management of depression. Evidence-based clinical guidelines are useful to improve processes and clinical outcomes. In order to make their implementation easier these guidelines have been transformed into computerised clinical decision support systems. In this article, a description is presented on the basics and characteristics of a new computerised clinical guideline for the management of major depression, developed in the public health system in Catalonia. This tool helps the clinician to establish reliable and accurate diagnoses of depression, to choose the best treatment a priori according to the disease and the patient characteristics. It also emphasises the importance of systematic monitoring to assess the clinical course, and to adjust therapeutic interventions to the patient's needs at all times.

An architecture for a continuous, user-driven, and data-driven application of clinical guidelines and its evaluation.

OBJECTIVES: Design, implement, and evaluate a new architecture for realistic continuous guideline (GL)-based decision support, based on a series of requirements that we have identified, such as support for continuous care, for multiple task types, and for data-driven and user-driven modes. METHODS: We designed and implemented a new continuous GL-based support architecture, PICARD, which accesses a temporal reasoning engine, and provides several different types of application interfaces. We present the new architecture in detail in the current paper. To evaluate the architecture, we first performed a technical evaluation of the PICARD architecture, using 19 simulated scenarios in the preeclampsia/toxemia domain. We then performed a functional evaluation with the help of two domain experts, by generating patient records that simulate 60 decision points from six clinical guideline-based scenarios, lasting from two days to four weeks. Finally, 36 clinicians made manual decisions in half of the scenarios, and had access to the automated GL-based support in the other half. The measures used in all three experiments were correctness and completeness of the decisions relative to the GL. RESULTS: Mean correctness and completeness in the technical evaluation were 1±0.0 and 0.96±0.03 respectively. The functional evaluation produced only several minor comments from the two experts, mostly regarding the output's style; otherwise the system's recommendations were validated. In the clinically oriented evaluation, the 36 clinicians applied manually approximately 41% of the GL's recommended actions. Completeness increased to approximately 93% when using PICARD. Manual correctness was approximately 94.5%, and remained similar when using PICARD; but while 68% of the manual decisions included correct but redundant actions, only 3% of the actions included in decisions made when using PICARD were redundant. CONCLUSIONS: The PICARD architecture is technically feasible and is functionally valid, and addresses the realistic continuous GL-based application requirements that we have defined; in particular, the requirement for care over significant time frames. The use of the PICARD architecture in the domain we examined resulted in enhanced completeness and in reduction of redundancies, and is potentially beneficial for general GL-based management of chronic patients.

How to improve vital sign data quality for use in clinical decision support systems? A qualitative study in nine Swedish emergency departments.

BACKGROUND: Vital sign data are important for clinical decision making in emergency care. Clinical Decision Support Systems (CDSS) have been advocated to increase patient safety and quality of care. However, the efficiency of CDSS depends on the quality of the underlying vital sign data. Therefore, possible factors affecting vital sign data quality need to be understood. This study aims to explore the factors affecting vital sign data quality in Swedish emergency departments and to determine in how far clinicians perceive vital sign data to be fit for use in clinical decision support systems. A further aim of the study is to provide recommendations on how to improve vital sign data quality in emergency departments. METHODS: Semi-structured interviews were conducted with sixteen physicians and nurses from nine hospitals and vital sign documentation templates were collected and analysed. Follow-up interviews and process observations were done at three of the hospitals to verify the results. Content analysis with constant comparison of the data was used to analyse and categorize the collected data. RESULTS: Factors related to care process and information technology were perceived to affect vital sign data quality. Despite electronic health records (EHRs) being available in all hospitals, these were not always used for vital sign documentation. Only four out of nine sites had a completely digitalized vital sign documentation flow and paper-based triage records were perceived to provide a better mobile workflow support than EHRs. Observed documentation practices resulted in low currency, completeness, and interoperability of the vital signs. To improve vital sign data quality, we propose to standardize the care process, improve the digital documentation support, provide workflow support, ensure interoperability and perform quality control. CONCLUSIONS: Vital sign data quality in Swedish emergency departments is currently not fit for use by CDSS. To address both technical and organisational challenges, we propose five steps for vital sign data quality improvement to be implemented in emergency care settings.

Rural providers' access to online resources: a randomized controlled trial.

OBJECTIVE: The research determined the usage and satisfaction levels with one of two point-of-care (PoC) resources among health care providers in a rural state. METHODS: In this randomized controlled trial, twenty-eight health care providers in rural areas were stratified by occupation and region, then randomized into either the DynaMed or the AccessMedicine study arm. Study participants were physicians, physician assistants, and nurses. A pre- and post-study survey measured participants' attitudes toward different information resources and their information-seeking activities. Medical student investigators provided training and technical support for participants. Data analyses consisted of analysis of variance (ANOVA), paired t tests, and Cohen's d statistic to compare pre- and post-study effects sizes. RESULTS: Participants in both the DynaMed and the AccessMedicine arms of the study reported increased satisfaction with their respective PoC resource, as expected. Participants in both arms also reported that they saved time in finding needed information. At baseline, both arms reported too little information available, which increased to "about right amounts of information" at the completion of the study. DynaMed users reported a Cohen's d increase of +1.50 compared to AccessMedicine users' reported use of 0.82. DynaMed users reported d2 satisfaction increases of 9.48 versus AccessMedicine satisfaction increases of 0.59 using a Cohen's d. CONCLUSION: Participants in the DynaMed arm of the study used this clinically oriented PoC more heavily than the users of the textbook-based AccessMedicine. In terms of user satisfaction, DynaMed users reported higher levels of satisfaction than the users of AccessMedicine.

Predicting severity of pathological scarring due to burn injuries: a clinical decision making tool using Bayesian networks.

It is important for clinicians to understand which are the clinical signs, the patient characteristics and the procedures that are related with the occurrence of hypertrophic burn scars in order to carry out a possible prognostic assessment. Providing clinicians with an easy-to- use tool for predicting the risk of pathological scars. A total of 703 patients with 2440 anatomical burn sites who were admitted to the Department of Plastic and Reconstructive Surgery, Burn Center of the Traumatological Hospital in Torino between January 1994 and May 2006 were included in the analysis. A Bayesian network (BN) model was implemented. The probability of developing a hypertrophic scar was evaluated on a number of scenarios. The error rate of the BN model was assessed internally and it was equal to 24·83%. While classical statistical method as logistic models can infer only which variables are related to the final outcome, the BN approach displays a set of relationships between the final outcome (scar type) and the explanatory covariates (patient's age and gender, burn surface area, full-thickness burn surface area, burn anatomical area and wound-healing time; burn treatment options such as advanced dressings, type of surgical approach, number of surgical procedures, type of skin graft, excision and coverage timing). A web-based interface to handle the BN model was developed on the website www.pubchild.org (burns header). Clinicians who registered at the website could submit their data in order to get from the BN model the predicted probability of observing a pathological scar type.

Diagnostic Utility of Artificial Intelligence-assisted Transperineal Biopsy Planning in Prostate Cancer Suspected Men: A Prospective Cohort Study.

BACKGROUND AND OBJECTIVE: Accurate magnetic resonance imaging (MRI) reporting is essential for transperineal prostate biopsy (TPB) planning. Although approved computer-aided diagnosis (CAD) tools may assist urologists in this task, evidence of improved clinically significant prostate cancer (csPCa) detection is lacking. Therefore, we aimed to document the diagnostic utility of using Prostate Imaging Reporting and Data System (PI-RADS) and CAD for biopsy planning compared with PI-RADS alone. METHODS: A total of 262 consecutive men scheduled for TPB at our referral centre were analysed. Reported PI-RADS lesions and an US Food and Drug Administration-cleared CAD tool were used for TPB planning. PI-RADS and CAD lesions were targeted on TPB, while four (interquartile range: 2-5) systematic biopsies were taken. The outcomes were the (1) proportion of csPCa (grade group ≥2) and (2) number of targeted lesions and false-positive rate. Performance was tested using free-response receiver operating characteristic curves and the exact Fisher-Yates test. KEY FINDINGS AND LIMITATIONS: Overall, csPCa was detected in 56% (146/262) of men, with sensitivity of 92% and 97% (p = 0.007) for PI-RADS- and CAD-directed TPB, respectively. In 4% (10/262), csPCa was detected solely by CAD-directed biopsies; in 8% (22/262), additional csPCa lesions were detected. However, the number of targeted lesions increased by 54% (518 vs 336) and the false-positive rate doubled (0.66 vs 1.39; p = 0.009). Limitations include biopsies only for men at clinical/radiological suspicion and no multidisciplinary review of MRI before biopsy. CONCLUSIONS AND CLINICAL IMPLICATIONS: The tested CAD tool for TPB planning improves csPCa detection at the cost of an increased number of lesions sampled and false positives. This may enable more personalised biopsy planning depending on urological and patient preferences. PATIENT SUMMARY: The computer-aided diagnosis tool tested for transperineal prostate biopsy planning improves the detection of clinically significant prostate cancer at the cost of an increased number of lesions sampled and false positives. This may enable more personalised biopsy planning depending on urological and patient preferences.

Automated monitoring of tooth wear progression using AI on intraoral scans.

OBJECTIVES: This study aimed to develop and evaluate a fully automated method for visualizing and measuring tooth wear progression using pairs of intraoral scans (IOSs) in comparison with a manual protocol. METHODS: Eight patients with severe tooth wear progression were retrospectively included, with IOSs taken at baseline and 1-year, 3-year, and 5-year follow-ups. For alignment, the automated method segmented the arch into separate teeth in the IOSs. Tooth pair registration selected tooth surfaces that were likely unaffected by tooth wear and performed point set registration on the selected surfaces. Maximum tooth profile losses from baseline to each follow-up were determined based on signed distances using the manual 3D Wear Analysis (3DWA) protocol and the automated method. The automated method was evaluated against the 3DWA protocol by comparing tooth segmentations with the Dice-Sørensen coefficient (DSC) and intersection over union (IoU). The tooth profile loss measurements were compared with regression and Bland-Altman plots. Additionally, the relationship between the time interval and the measurement differences between the two methods was shown. RESULTS: The automated method completed within two minutes. It was very effective for tooth instance segmentation (826 teeth, DSC = 0.947, IoU = 0.907), and a correlation of 0.932 was observed for agreement on tooth profile loss measurements (516 tooth pairs, mean difference = 0.021mm, 95% confidence interval = [-0.085, 0.138]). The variability in measurement differences increased for larger time intervals. CONCLUSIONS: The proposed automated method for monitoring tooth wear progression was faster and not clinically significantly different in accuracy compared to a manual protocol for full-arch IOSs. CLINICAL SIGNIFICANCE: General practitioners and patients can benefit from the visualization of tooth wear, allowing quantifiable and standardized decisions concerning therapy requirements of worn teeth. The proposed method for tooth wear monitoring decreased the time required to less than two minutes compared with the manual approach, which took at least two hours.

Clinical Utility of an Artificial Intelligence Radiomics-based Tool for Risk Stratification of Pulmonary Nodules.

BACKGROUND: Clinical utility data on pulmonary nodule (PN) risk stratification biomarkers are lacking. We aimed to determine the incremental predictive value and clinical utility of using an artificial intelligence (AI) radiomics-based computer-aided diagnosis (CAD) tool in addition to routine clinical information to risk stratify PNs among real-world patients. METHODS: We performed a retrospective cohort study of patients with PNs who underwent lung biopsy. We collected clinical data and used a commercially available AI radiomics-based CAD tool to calculate a Lung Cancer Prediction (LCP) score. We developed logistic regression models to evaluate a well-validated clinical risk prediction model (the Mayo Clinic model) with and without the LCP score (Mayo vs Mayo + LCP) using area under the curve (AUC), risk stratification table, and standardized net benefit analyses. RESULTS: Among the 134 patients undergoing PN biopsy, cancer prevalence was 61%. Addition of the radiomics-based LCP score to the Mayo model was associated with increased predictive accuracy (likelihood ratio test, P = .012). The AUCs for the Mayo and Mayo + LCP models were 0.58 (95% CI, 0.48-0.69) and 0.65 (95% CI, 0.56-0.75), respectively. At the 65% risk threshold, the Mayo + LCP model was associated with increased sensitivity (56% vs 38%; P = .019), similar false positive rate (33% vs 35%; P = .8), and increased standardized net benefit (18% vs -3.3%) compared to the Mayo model. CONCLUSIONS: Use of a commercially available AI radiomics-based CAD tool as a supplement to clinical information improved PN cancer risk prediction and may result in clinically meaningful changes in risk stratification.

The role of information systems in emergency department decision-making-a literature review.

OBJECTIVES: Healthcare providers employ heuristic and analytical decision-making to navigate the high-stakes environment of the emergency department (ED). Despite the increasing integration of information systems (ISs), research on their efficacy is conflicting. Drawing on related fields, we investigate how timing and mode of delivery influence IS effectiveness. Our objective is to reconcile previous contradictory findings, shedding light on optimal IS design in the ED. MATERIALS AND METHODS: We conducted a systematic review following PRISMA across PubMed, Scopus, and Web of Science. We coded the ISs' timing as heuristic or analytical, their mode of delivery as active for automatic alerts and passive when requiring user-initiated information retrieval, and their effect on process, economic, and clinical outcomes. RESULTS: Our analysis included 83 studies. During early heuristic decision-making, most active interventions were ineffective, while passive interventions generally improved outcomes. In the analytical phase, the effects were reversed. Passive interventions that facilitate information extraction consistently improved outcomes. DISCUSSION: Our findings suggest that the effectiveness of active interventions negatively correlates with the amount of information received during delivery. During early heuristic decision-making, when information overload is high, physicians are unresponsive to alerts and proactively consult passive resources. In the later analytical phases, physicians show increased receptivity to alerts due to decreased diagnostic uncertainty and information quantity. Interventions that limit information lead to positive outcomes, supporting our interpretation. CONCLUSION: We synthesize our findings into an integrated model that reveals the underlying reasons for conflicting findings from previous reviews and can guide practitioners in designing ISs in the ED.

Informed consent for capacity assessment.

In this paper we examine the role of informed consent to capacity assessment, focussing primarily on the two jurisdictions of England and Wales, and Ireland. We argue that in both jurisdictions, a capacity assessment should be regarded as a distinct intervention, separate from the 'original' intervention at issue, and that specific informed consent to the assessment should generally be sought in advance. As part of this, we consider what information should be provided so as to ensure informed consent. Having established a baseline requirement for informed consent, we also recognise that informed consent to assessment will not always be possible, either because the person is unable to understand the information about assessment or because the person refuses to be assessed and so, in the final part of the article, we explore how to proceed when informed consent is either not possible or not forthcoming, including an analysis of the implications of the statutory presumption of capacity.