Artificial Intelligence (AI)

Digital technologies have evolved exponentially in the dental medicine field endorsing a change between the conventional methods to virtually based methodologies in daily clinical and laboratorial practice. Combining facial aspects and proportions with dento-gingival parameters are the basis when planning a new smile design and a final rehabilitationFacial surface images can be used for more predictable measurement and quantification of vertical dimension of occlusion and lip support before, during and after a full mouth rehabilitation. Besides that, the information obtained by facial scanners have a major impact in treatment planning process especially in multidisciplinary complex cases with the simulation of the treatment, identification of patient's expectations and the implementation of an effective communication tool. The 4D-virtual patient is the future regarding the management of a patient in dental medicine, since the beginning of the process with data acquisition for the diagnosis to the definitive oral rehabilitation procedures. Similar to any methodology, it is important to understand what are the basis of the facial scanning and what protocols can obtain better results in terms of accuracy and reliability.

1. Background \& Rationale: Accurate assessment of a patient's blood volume (BV) status before surgery is critical for preventing perioperative complications. However, there is currently no clinically feasible, accurate, and non-invasive method for direct BV quantification. We hypothesize that dynamic ultrasound videos of major blood vessels contain rich, sub-visual spatiotemporal information about vascular compliance and filling that can be leveraged to estimate BV. 2. Objective: To develop and validate a deep learning model that integrates multi-modal ultrasound video data to achieve non-invasive, quantitative estimation of preoperative blood volume. 3. Study Design: A prospective, single-center, observational study. 4. Methods: Participants: Adult patients scheduled for surgery. Data Acquisition: Input (Features): Preoperative ultrasound video clips will be recorded in standardized views of four key vessels: the Internal Jugular Vein (IJV), Subclavian Vein (SCV), Inferior Vena Cava (IVC), and Common Carotid Artery (CA). Target (Label): The true Blood Volume (BV) will be calculated for each patient using the acute normovolemic hemodilution (ANH) method. The change in hemoglobin concentration before and after this process is used to calculate the total blood volume with high clinical reliability. Model Development: A hybrid deep learning architecture (e.g., CNN + LSTM/Transformer) will be trained to extract features from the ultrasound videos and learn the complex, non-linear mapping to the BV value derived from ANH. The model will be trained and internally validated using a k-fold cross-validation approach. 5. Expected Outcome \& Significance: We anticipate the development of a novel, end-to-end deep learning model capable of providing a quantitative BV estimate from routine ultrasound scans. This technology has the potential to revolutionize perioperative fluid management by offering a rapid, non-invasive, and accurate tool for objective volume status assessment, ultimately guiding personalized therapy and improving patient outcomes.

Pulmonary hypertension is often underdiagnosed due to extensive category of etiology. The diagnosis and treatment of pulmonary hypertension have changed dramatically through the re-defined diagnostic criteria and advanced drug development in the past decade. The application of Artificial Intelligence for the detection of elevated pulmonary arterial pressure (ePAP) was reported recently. An AI model based on electrocardiograms (ECG) has shown promise in not only detecting ePAP but also in predicting future risks related to cardiovascular mortality.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by disturbances in communication, poor social skills, and aberrant behaviors. Particularly detrimental are the presence of restricted and repetitive stereotyped behaviors and uncontrollable temper outbursts over trivial changes in the environment, which often cause emotional stress for the children, their families, schools and neighborhood communities. Fundamental to these cognitive and behavioral problems is the disordered cortical connectivity and resultant executive dysfunction that underpin the use of effective strategies to integrate information across contexts. Brain connectivity problems affect the rate at which information travels across the brain. Slow processing speed relates to a reduced capacity of executive function to recall and formulate thoughts and actions automatically, with the result that autistic children with poor processing speed have great difficulty learning or perceiving relationships across multiple experiences. In consequence, these children compensate for the impaired ability to integrate information from the environment by memorizing visual details or individual rules from each situation. This explains why children with autism tend to follow routines in precise detail and show great distress over seemingly trivial changes in the environment. To date, there is no known cure for ASD, and the disorder remains a highly disabling condition. Recently, a non-invasive brain stimulation technique, transcranial direct current Stimulation (tDCS) has shown great promise as a potentially effective and costeffective tool for reducing core symptoms such as anxiety, aggression, impulsivity, and inattention in patients with autism. This technique has been shown to modify behavior by inducing changes in cortical excitability and enhancing connectivity between the targeted brain areas. However, not all ASD patients respond to this intervention the same way and predicting the behavioral impact of tDCS in patients with ASD remains a clinical challenge. This proposed study thus aims to address these challenges by determining whether resting-state EEG and clinical data at baseline can be used to differentiate responders from non-responders to tDCS treatment. Findings from the study will provide new guidance for designing intervention programs for individuals with ASD.

Cardiovascular disease is a major threat to the health of Chinese residents, and echocardiography, as its core diagnostic tool, directly affects clinical decision-making in terms of measurement accuracy and efficiency. However, traditional ultrasound evaluation heavily relies on physician experience, with pain points such as strong subjectivity, time-consuming measurements, and uneven levels of primary diagnosis. There is an urgent need for technological innovation to improve diagnostic standardization. In recent years, artificial intelligence (AI) technology has shown great potential in the field of medical image analysis, which can achieve automated quantitative measurement of cardiac chamber structure and function. However, existing AI models generally have problems such as insufficient multi center validation and limited adaptability to complex cases, which restrict their clinical translation and application. To overcome these bottlenecks, this project collaborates with multiple medical institutions to conduct clinical research, systematically evaluating the measurement differences between AI algorithms and physicians of different levels, and assessing the accuracy and stability of AI algorithms. The research will focus on verifying the value of AI technology in improving diagnostic consistency, optimizing workflows, and exploring its potential applications in complex cardiovascular diseases. By establishing a standardized evaluation system, this project aims to promote the standardized application of AI ultrasound technology, ultimately achieving the goal of improving diagnosis and treatment efficiency, promoting the sinking of high-quality medical resources, and helping to improve the overall level of cardiovascular disease prevention and treatment.

The gestational period, a physiological condition linked to elevated physiological stress, induces significant cardiac remodeling and systemic hemodynamic adaptations in maternal organisms. AA, a rare but life-threatening hematologic disorder characterized by pancytopenia and bone marrow hypoplasia, poses profound challenges during pregnancy, with significant risks of maternal and perinatal morbidity and mortality. Physiological adaptations in pregnancy, including hemodilution and increased metabolic demands, exacerbate AA-related hematologic deficits, elevating risks of severe anemia, thrombocytopenia-related hemorrhage, and immunosuppression-associated infections. These outcomes underscore the critical need for dynamic risk stratification and tailored interventions. Currently, most cohort studies on pregnant women with AA in China are retrospective, single-center studies with small sample sizes, resulting in insufficient data and a lack of multicenter, prospective cohort studies. This study is a multicenter, retrospective and prospective observational study that will enroll pregnant women with aplastic anemia. It will collect baseline patient information and diagnostic data, conduct regular prospective follow-ups via questionnaires, telephone interviews, video consultations, online platforms, and in-person visits, and record treatment regimens, comorbidities, and prognostic outcomes. The study aims to provide comprehensive data on the epidemiology and clinical outcomes of pregnant women with aplastic anemia in China, and aimed to develop and validate a predictive model for adverse pregnancy outcomes in pregnant women with AA, with the goal of guiding early clinical decision-making and improving their overall health outcomes.

The primary efficacy endpoints are the standard deviation and coefficient of determination (R2) between predicted and actual values for the bilirubin regression model, and the grading accuracy for the jaundice severity classification model. The secondary efficacy endpoint is the mean percentage error between predicted and actual bilirubin values. There are no relevant safety risks. Statistical differences for categorical variables (e.g., jaundice grading evaluation indicators) will be analyzed using the chi-square test or Fisher's exact probability test. For continuous variables (e.g., bilirubin prediction evaluation indicators), t-tests (normal distribution) or non-parametric tests (non-normal distribution) will be used. The 95% confidence interval for jaundice grading accuracy will be calculated using the Wilson method. The study duration is estimated to be 3 months.

In this study, we collected the data of immunohistochemistry, gene detection, image, OS, PFS, Orr, and so on. Secondly, the database of immunotherapy for malignant tumor was established, and the predictive model was constructed to verify and establish the rationality and validity of the biomarkers and predictive system of immunotherapy

During the RCT the AI support tool will be randomized to be turned on or off (1:1) at the mammography exam level. Patients who return for screening exams in year 2 of recruitment will be randomized again (e.g., they will not retain their prior randomization). Radiologists will not be able to sort exams based on AI availability or AI scores. Randomizing by exam level will ensure that we capture a substantial number of interpretations with vs. without AI for each radiologist, allowing for quantification of the radiologist-level AI learning curve. We are not randomizing at the facility level as some radiologists interpret exams acquired at different facilities on the same day. By randomizing AI at the exam level, we will have the best ability to estimate and adjust for temporal trends in screening outcomes across individual radiologists. Randomization across large regional health systems will be managed independently at each participating site. Our RCT randomizes screening mammography exams to be interpreted either with or without an AI decision-support tool. As a result, radiologists cannot be blinded to study arm during screening mammography interpretation. However, interpreting radiologists and facility staff (e.g., those scheduling the exams) will not know in advance which patients will be randomized to the AI tool. Randomization occurs within minutes after the breast imaging acquisition (i.e., when the mammography technologist captures the images) by an automated system that was developed by a third-party AI platform and successfully piloted at UCLA. Thus, the AI data (or lack thereof) is embedded within the mammogram before the radiologist opens the exam, preventing any option to "add AI" to an exam randomized to be interpreted without AI. Radiologists will be aware of AI availability only at the time of interpretation, as AI information will appear upon opening the exam (e.g., the AI information pops up with the exam images).

This study collected clinical, laboratory, and CT parameters of acute patients with acute pulmonary embolism from admission to predict adverse outcomes within 30 days after admission into hospital. The investigators aim to build a predictive tool for Adverse Outcome of Acute Pulmonary Embolism by Artificial Intelligence System Based on CT Pulmonary Angiography. Eligible patients were randomized in some ratio into derivation and validation cohorts. The derivation cohort was used to develop and evaluate a multivariable logistic regression model for predicting the outcomes of interest. The discriminatory power was evaluated by comparing the nomogram to the established risk stratification systems. The consistency of the nomogram was evaluated using the validation cohort.