Diagnostic Module Research Articles

Quantitative analysis of imaging characteristics in lung adenocarcinoma in situ using artificial intelligence.

With the rising incidence of pulmonary nodules (PNs), lung adenocarcinoma in situ (AIS) is a critical early stage of lung cancer, necessitating accurate diagnosis for early intervention. This study applies artificial intelligence (AI) for quantitative imaging analysis to differentiate AIS from atypical adenomatous hyperplasia (AAH) and minimally invasive adenocarcinoma (MIA), aiming to enhance clinical diagnosis and prevent misdiagnosis. The study analyzed 1215 PNs with confirmed AAH, AIS, and MIA from six centers using the Shukun AI diagnostic module. Parameters evaluated included demographic data and various CT imaging metrics to identify indicators for clinical application, focusing on the mean CT value's predictive value. Significant differences were found in several parameters between AAH and AIS, with nodule mass showing the highest predictive value. When comparing AIS to MIA, total nodule volume was the best predictor, followed by the maximum CT value. The mean CT value has limited discriminative power for AIS diagnosis. Instead, the maximum CT value and maximum 3D diameter are recommended for clinical differentiation. Nodule mass and volume of solid components are strong indicators for differentiating AIS from AAH and MIA, respectively.

The next-generation of metaverse embodiment interaction devices: A self-powered sensing smart monitoring system

Wearable electronic devices collect user data to provide personalized experiences and real time interactions for the metaverse, thereby driving its development. This paper introduces an innovative self-powered sensing smart monitoring system (SSSMS) designed to harvest human low-frequency inertial energy and achieve gait recognition and fall monitoring (GR-FM) capabilities. The SSSMS enhances energy harvesting efficiency through Halbach electromagnetic enhancement mechanisms, achieving self-sustainability. Additionally, the conical roller triboelectric nanosensor (CR-TENS) accurately captures various human gaits, transmitting these states in the form of electrical signals to a computer via OpenBCI in real time. These signals are integrated with a deep learning-driven diagnostic module to achieve GR-FM. The experimental results demonstrate that under optimal external excitation, the output power is 2.27 mW, with a power density of 32 W/m3. After 100,000 cycles, both the EMG and CR-TENS modules still maintain good performance, sufficiently providing stable and continuous charging for the battery and offering a stable signal for signal processing. Both the EMG and CR-TENS modules exhibit robust feature capture capabilities, effectively sensing different excitation frequencies, amplitudes, and gait patterns. The SSSMS based on the GRU deep learning model achieves recognition accuracies of 96.13 %, 96.60 %, and 92.22 % for frequency, amplitude, and gait, respectively. Deployment experiments demonstrate high accuracy and sensitivity in frequency, amplitude, gait recognition, and fall monitoring, highlighting the advantageous design of EMG and CR-TENS for perceiving subtle motion state changes. The SSSMS demonstrates outstanding capabilities in self-sustainability and real time user embodiment information capture, showcasing its potential as a next-generation interaction device for the metaverse.

A non-enhanced CT-based deep learning diagnostic system for COVID-19 infection at high risk among lung cancer patients.

Pneumonia and lung cancer have a mutually reinforcing relationship. Lung cancer patients are prone to contracting COVID-19, with poorer prognoses. Additionally, COVID-19 infection can impact anticancer treatments for lung cancer patients. Developing an early diagnostic system for COVID-19 pneumonia can help improve the prognosis of lung cancer patients with COVID-19 infection. This study proposes a neural network for COVID-19 diagnosis based on non-enhanced CT scans, consisting of two 3D convolutional neural networks (CNN) connected in series to form two diagnostic modules. The first diagnostic module classifies COVID-19 pneumonia patients from other pneumonia patients, while the second diagnostic module distinguishes severe COVID-19 patients from ordinary COVID-19 patients. We also analyzed the correlation between the deep learning features of the two diagnostic modules and various laboratory parameters, including KL-6. The first diagnostic module achieved an accuracy of 0.9669 on the training set and 0.8884 on the test set, while the second diagnostic module achieved an accuracy of 0.9722 on the training set and 0.9184 on the test set. Strong correlation was observed between the deep learning parameters of the second diagnostic module and KL-6. Our neural network can differentiate between COVID-19 pneumonia and other pneumonias on CT images, while also distinguishing between ordinary COVID-19 patients and those with white lung. Patients with white lung in COVID-19 have greater alveolar damage compared to ordinary COVID-19 patients, and our deep learning features can serve as an imaging biomarker.

Harnessing Fusion Modeling for Enhanced Breast Cancer Classification through Interpretable Artificial Intelligence and In-Depth Explanations

Integrating Artificial Intelligence (AI) into healthcare has shown tremendous promise in several domains, such as prediction, decision-making, and diagnosis. Nevertheless, the widespread belief that AI is incomprehensible and mysterious has restricted its practical use, particularly in medicine. This skepticism highlights the need for methods that provide precise predictions and clear explanations for these projections. The present study presents a hybrid method “BC2” (Breast Cancer Classification) that combines deep learning techniques to accurately detect breast cancer and provide detailed explanations for its predictions. “BC2” exploits the collaborative effects of Convolutional Neural Networks (CNN) and the Light Gradient Boosting Model (LGBM). The automated feature learning process of the model is integrated into many convolutional layers, and LGBM then follows it to provide predictions for class labels. To evaluate the effectiveness of the proposed approach, the authors performed experiments with real-world Breast Cancer Data. The results demonstrate substantial enhancements compared to the current methods in key metrics such as accuracy, precision, recall, and F1 score. The proposed technique obtains an accuracy of 0.982906, a precision of 0.987179, a recall of 0.987179, and an F1-score of 0.987179, demonstrating its exceptional performance. After generating exact forecasts, authors use the “SHAP” (SHapley Additive exPlanation) approach at local and global levels to explain the reasoning behind each projection. This guarantees clarity and improves the comprehensibility of the predictions made by the proposed model. The proposed hybrid model consists of three main components: a feature learning module, a diagnostic module, and an explanation-generating module. The proposed model can improve medical professionals’ diagnostic skills in managing breast cancer patients by clearly explaining its forecasts.

A multi-node self-powered fault detection system by triboelectric-electromagnetic nanosensors for smart transportation

The harnessing of vibrational energy is becoming increasingly pivotal in the development of intelligent rail transit systems. The integration of emerging technologies such as triboelectric nanogenerators (TENGs), electromagnetic generators (EMGs), or hybrid generators has become crucial for fault detection and energy harvesting in rail transit. This paper introduces a self-powered fault detection system (SPFDS). SPFDS combines multiple compact rotating Triboelectric-Electromagnetic Nanosensor (TENS) nodes with a deep learning-based diagnostic module to transform vibrational energy generated during train operations into electrical power and accurately identifies five distinct train bogie fault conditions. Simulations and experiments have shown that the TENS nodes, with a root mean square power of 0.21 W and a power density of 1595.7 W/m³, can efficiently detect various bogie faults. Additionally, their power output is adequate to support commercial sensors and Bluetooth modules. Through hyperparameter optimization, the diagnostic module utilizing multi-TENS nodes achieves an average diagnostic accuracy of 99.38 % for the five fault modes of freight train bogies. Implementing multiple TENS nodes in SPFDS enhances fault detection accuracy by an average of 32 % compared to a single TENS node, with a peak increase of 128 %. The multi-node TENS configuration and SPFDS's self-powered detection capabilities represent an innovative approach to complex fault detection, significantly contributing to the advancement of vibration energy harvesting and the development of distributed self-powered sensor network technologies for smart transportation.

Chemical fault diagnosis network based on single domain generalization

Recent developments in fault diagnosis have leveraged domain generalization to address the issue of domain shift. Most existing methods focus on learning domain-invariant representations from multiple source domains. However, collecting valuable fault samples from varying operational conditions is challenging, and it is common for available data to originate from a single operational condition. Thus, this paper introduces a Multi-scale generative and adversarial Metric networks (MGAMN) for Chemical Process Fault Diagnosis. To enhance model generalization, a domain generation module was developed to create augmented domains with significant distributional differences from the source domain. The diagnostic task module then extracts domain-invariant features from both the source and augmented domains. A multi-scale generation strategy is established, utilizing multi-scale deep separable convolutions (Dsc) to ensure that the generated samples contain rich state information. Additionally, an adversarial training and metric learning strategy is designed to learn generalized features capable of resisting unknown domain shifts. Extensive diagnostic experiments on the non-isothermal continuous stirred tank reactor (CSTR) and the Tennessee Eastman Process (TEP) chemical datasets validate the effectiveness of the proposed method. Moreover, ablation studies confirm the effectiveness of the proposed modular strategy, demonstrating significant potential for practical applications.

Mechanical design of ITER radial neutron camera Ex-Port system

The Radial Neutron Camera is an ITER diagnostic designed to measure the un-collided 14 MeV and 2.5 MeV neutrons from deuterium-tritium (DT) and deuterium-deuterium (DD) fusion reactions, through an array of detectors covering a poloidal plasma section along collimated Lines Of Sight (LOS). It is composed by two fan-shaped collimating structures viewing the plasma radially through vertical slots in the diagnostic shielding module of ITER Equatorial Port 1: the In-Port RNC, devoted to plasma edge coverage, and the Ex-Port RNC, devoted to the plasma core coverage. This paper presents an overview of the mechanical design of the Ex-Port RNC at the Preliminary Design Review (PDR) stage. The Ex-Port RNC is located in the Port Interspace and consists of a massive shielding structure hosting the detector units and two sets of collimators lying on different toroidal planes. The Ex-Port RNC design is presented both from the point of view of functional requirements (e.g. LOS positions and angles, radiation shielding, weight limitations) and of manufacturability. Finally, the Ex-port RNC structural integrity is assessed, and its design validated against the main loads and load combinations.

Design and Development of ECG Monitoring Cloud Platform Based on the Internet of Things Electrocardiograph

The design and development of electrocardiogram（ECG） monitoring cloud platform based on the Internet of Things（IoT） electrocardiograph is introduced. The platform is mainly composed of ECG acquisition module, algorithm module, diagnostic model comparison module, warning module, positioning module and medical aid system. The ECG acquisition module collects ECG signals of patients and displays them in real time on the mobile terminals. Then they are uploaded to the ECG monitoring cloud platform through the IoT. The algorithm module and the diagnostic model comparison module mark, process, analyze and diagnose the ECG. Meanwhile, the ECG diagnosis and warning results are pushed to patients and 120 emergency centers through the IoT. Furthermore, the cloud platform will guide patients to self-rescue and the emergency platform will open the green channel to save patients in time.The platform realizes from the onset to diagnosis and treatment in one step, and saves lives against time.

Assessing the Possibility of Implementing Distributed Diagnostic Systems for Hydrogen Fuel Cells Using Local and Global Telecommunication Networks

The development of effective methods and means for diagnosing hydrogen fuel cells during their operation is an urgent task, since their implementation will improve the energy characteristics and stability of operation by detecting incorrect operating modes and malfunctions at the early stages of development. This problem can be solved by using various approaches based on the analysis of impedance, relaxation or fluctuation-noise characteristics. All of these methods require the implementation of rather complex computational procedures and the accumulation of fairly large amount of statistical data, which makes it difficult to implement diagnostic devices on inexpensive embedded microprocessor tools. To overcome this drawback, this work proposes the use of distributed diagnostic systems with high-performance server devices connected to local modules via global or local telecommunication networks. Such an implementation will significantly simplify the requirements for local diagnostic modules and reduce their cost, but this raises the question of the efficiency of making diagnostic decisions by such systems.The work carried out, is a study of delays in making diagnostic and control decisions when using remote server devices for signal processing. It has been shown that the use of such systems makes it possible to achieve delays of less than one second even when using intercontinental communication lines. It should be noted that in applications critical to time delays, the distance between the client and server systems should be reduced. The advantages of the proposed implementation of a diagnostic system for electrochemical energy sources are the possibility of simplifying local measurement systems installed on individual sources, the possibility of accelerating signal processing through the use of more productive computing tools and the accumulation of statistical data.

Half-baked essays: the embedded use of writing exemplars to encourage active learning for radiography students

This paper discusses the implementation of exemplar essays within an undergraduate first-year diagnostic radiography module to encourage active learning and the development of academic literacies. Nine essays (~400 words each) were provided over six sessions, using lecturer-guided discussion to explore the academic virtues and pitfalls demonstrated alongside assessment criteria. Exemplars were based upon a fictitious baking profession to illicit student interest and eliminate the risk of imitation or plagiarism. The quality of essays varied, with overt examples of common mistakes in spelling, essay structure and selection of appropriate literature. Upon completion of the sessions, and prior to summative assessment, students were surveyed with regard to their opinion of the teaching intervention. Feedback was obtained from 65% of the cohort (n=31/48), who reported an improvement in essay writing confidence. Students found the marking of the essays in class useful and the baking analogy was well received. Not all students found the classroom activities engaging though, with some wishing greater interactivity due to boredom and repetition. Furthermore, there was a slight reduction in pass rates and average marks across the cohort when compared to the previous module delivery. Nevertheless, there was a substantial increase in students attaining marks of 80% or more. This study highlights exemplar essays as a low-tech, low-cost option to increase assessment literacy but accepts the wide diversity in student learning preferences and experience. The efficacy of exemplar essays cannot be guaranteed, and teaching staff must be cognisant of individual student learning needs.

Nuclear analyses for the integration of ITER equatorial Port 2

The present work is devoted to nuclear analyses in support of the ITER diagnostic Equatorial Port 2 (EP#2) integration. ITER EP #2 is a diagnostic port based on the long-modular Diagnostic Shielding Module (DSM) housing the following systems: Disruption Mitigation System (DMS) in DSM#1 and #3 and X-Ray Crystal Spectroscopy Core (XRCS-Core) in DSM#2. Ensuring adequate radiation shielding is a major challenge since the diagnostic systems require several apertures from the Vacuum Vessel (VV) through the Port Interspace (PI) and up to the Port Cell (PC). In the present study, a three-dimensional MCNP model of EP#2 has been developed, starting from the latest design available from Preliminary Design Review stage (PDR), and successively integrated into the reference 40° ITER C-Model. Comprehensive nuclear analyses have been carried out employing the D1SUNED v3.1.4 code based on the MCNP Monte Carlo transport code. Relevant nuclear quantities during and at the end of plasma operations have been evaluated: i.e., neutron and gamma fluxes and energy spectra along the port from the Diagnostic First Wall (DFW) up to the Bio-Shield Plug (BP), nuclear heating, neutron damage, helium and tritium production, and shutdown dose rate. This analysis allowed the identification of potentially critical areas, and therefore, the implementation of additional shielding options aimed at reducing the neutron streaming and mitigating of the radiation field in the PI region. In this work, the results of the analyses are presented and discussed. Some solutions to mitigate nuclear loads and to improve the shielding in PI area are proposed and their impact has been assessed. Finally, some recommendations for the optimization of the design of EP#2 are provided as well.

Vibration sensor as the basis of an equipment monitoring system

Currently, the high complexity and instability of engineering production processes leads to the need to make management decisions under conditions of uncertainty, which significantly affects the efficiency of production as a whole. Reducing uncertainty in operational production planning of machine-building industries is possible by analyzing data on the flow of technological processes collected using equipment monitoring systems, i.e. measuring and information systems. It is proposed to use vibration sensors as the main source of information to obtain reliable data on the operation of equipment. General issues regarding vibration sensors are considered and a variant of their classification according to various features is presented, such as sensitivity, operating principle, method of obtaining information, signal conversion mechanism, signal measurement method. Several versions of vibration sensors have been developed, designed to collect information about the level of vibration during processing and included in the diagnostic module for monitoring equipment operation. The first version of the printed circuit board is based on a microcontroller based on ATMega328P, designed to read MEMS accelerometer data and transmit the collected information using a wireless communication module to the operator panel for display in real time and transmission for storage and processing to the server. To charge the battery, a charge controller based on the TP4056 chip and a low-dropout linear stabilizer TPS730 are used. In the second option, the MPU9250 inertial measurement module is used, and information processing and data transmission is carried out using a specialized module NINA-B306. A printed circuit board of any of the presented options is installed in a vibration control sensor mounted on the back side of a three-jaw chuck inside the machine body. This placement of the diagnostic module eliminates the possibility of its unintentional damage while ensuring the required quality and reliability of signal transmission. The vibration sensor, fixed to the cartridge using quick-release strips, consists of a board located in the housing. Above the board there is an insert that protects the board from the battery. The housing is closed from above with a lid using screw washers through a gasket to ensure the housing is sealed. The clamping screw is used to secure the board in the case. The vibration sensor is turned on using a button. The proposed vibration sensor can be used as the basis of an measuring and information systems, because information about the level of vibration of a technological system, recorded by sensors, like a human cardiogram, provides comprehensive information about its condition. A diagnostic module built on the basis of a vibration sensor will increase the efficiency and “transparency” of machining processes and collect statistical data that allows assessing the efficiency of equipment operation.

TunGPR: Enhancing data-driven maintenance for tunnel linings through synthetic datasets, deep learning and BIM

Non-destructive Testing (NDT) techniques and data-driven technologies are increasingly applied in underground infrastructure maintenance, which can facilitate predictive monitoring for informed decision-making. Ground Penetrating Radar (GPR) is extensively utilised in rapid condition assessment of tunnel linings, particularly for detecting defects that lead to unforeseen changes of dielectric properties of materials. In this paper, a prototyped framework is proposed, namely TunGPR, for GPR-based tunnel lining assessment by incorporating Building Information Modelling (BIM), synthetic database and deep learning-enabled interpretation. The first module integrates laser-scanned point clouds and GPR Scan-to-BIM of tunnel lining with geological model. Subsequently, interoperability is achieved between the geo-integrated BIM and GPR simulation software. From the dielectric model retrieved from the BIM model, a database is established, considering a variety of condition combinations (i.e., voids, cavities, delamination, and water intrusion) leveraging domain randomisation and Finite-Difference Time-Domain (FDTD) modelling, as well as monitored field data. The dataset is then fed into the diagnostic module underpinned by a dual-rotational Convolutional Neural Network (CNN) that is customised to enhance accuracy and automation of hyperbola detection. Lastly, a preliminary risk assessment matrix is implemented into the BIM model for data management and action prioritisation. These efforts serve as an initial step to validate the feasibility and effectiveness of the GPR-enabled data-driven maintenance for tunnel linings in a BIM-centred framework.

Docking unmanned multi-rotor platform for the diagnostic of building structures strengthened with fiber-reinforced polymer overlays

External fiber-reinforced polymer overlays have become the most popular method of building structures' strengthening on a global scale. Since no widely used and reliable methods for diagnosing the connection of overlays with the strengthened surface have been developed so far, developing such a method with the entire system, enabling easy and reliable use, has become a vital need of the construction sector. The paper presents an original infrared-based diagnostic module placed on a drone, enabling docking under the structure and conducting precise, uninterrupted diagnostics. The article describes the solutions and configuration of the automatic drone control system and the diagnostic module in all phases of its operation. The embedded measurement and control system has a multi-level structure distributed between the onboard automation system and the ground control station. Laboratory and field tests have shown that the road viaduct solution accurately reproduces joint damage, enabling the assessment of the structure's safety.

The diagnostic value of serum miR-17-92 cluster in ischemic stroke.

Ischemic stroke (IS) is a prevalent disease that poses a significant threat to human life and is responsible for a substantial financial burden. Research has established the crucial role of the miR-17-92 cluster in lung cancer, cardiovascular diseases, and traumatic brain injury. Despite this, few research studies had fully detected the potential of the miR-17-92 cluster as a novel circulating marker for diagnosing IS. miR-17-92 cluster expression in IS was investigated using GSE117064 dataset via bioinformatics analysis. Moreover, qRT-PCR was conducted to further verify miR-17-92 cluster expression in 58 IS individuals and 50 healthy controls (HCs). These cluster members were examined regarding their potential for detecting and diagnosing IS using the ROC method. The expression level of serum miR-20a-5p, miR-19a-3p, miR-18a-5p, and miR-19b-3p was considerably lowered in IS in contrast with HC in both the GSE117064 cohort and clinical cohort. Moreover, these four miRNAs had a fair performance in IS detection. Thereafter, a diagnostic model based on these aforementioned four miRNAs was developed by logistic regression, which had an AUC of 0.974 in the ROC curve. This diagnostic module was verified using the GSE117064 dataset. Further analysis demonstrated an increasing level of the aforementioned miRNAs in day-7 IS patients compared with day-1 IS patients. This research verified the downregulation of the miR-17-92 cluster in IS. This diagnostic model enrolling four cluster members may be a promising biomarker for IS detection.

Pre-experiment of 70 MeV H - cyclotron for producing ISOL RI beam.

The Rare Isotope Science Project (RISP) is currently constructing equipment and facilities for the Rare Isotope Accelerator complex for ON-line experiments (RAON). The first experiment being conducted involves the acceleration of a proton to 70MeV using a cyclotron, which is then delivered to a SiC target to generate a radioactive isotope beam in the ISOL system. However, before conducting this experiment, a preliminary experiment was performed to confirm the exact location and shape of the proton beam to prevent loss of the proton beam. The pre-experiment involved configuring a faraday cup, wire grid scanner, collimator, and slit inside the proton beam diagnostic module to understand the characteristics of the proton beam at the target position. The beam current ranged from 1 μA to 1.5 μA, and the beam size was confirmed to be within the 2×2cm 2 slit size.

Искусственный интеллект в здравоохранении России: сбор и подготовка данных для машинного обучения.

This scientific article draws attention to the importance of collecting and preparing high-quality medical data for the development of artificial intelligence (AI) systems in Russian healthcare. Materials and methods. The main emphasis is on the need to create a unified federal standard for collecting structured digitized medical data, which will unify the processes of collecting and generating datasets for medical organizations. Results. The article proposes the introduction of digital assistants, including pre-medical diagnostic questionnaires, mobile applications and software modules for medical examinations, which will reduce the time spent filling out medical documentation and ensure the collection of more complete and accurate information. The next step is to reengineer the health data collection process, including patient engagement via smartphones and personal health assistants. The article identifies methodological problems such as unreliability, incompleteness and lack of extensional knowledge in the collected data, and proposes a roadmap for the development of a health data collection system, including the creation of digital standards, data collection methods and the development of AI-based decision support. Conclusions. Solving the described problems and the data development plan are important for the successful implementation of the national project «Data Economy», supporting the development and application of AI systems in the medical field.

A morphometric analysis of the retinal arterioles with adaptive optics imaging in RPE65-associated retinal dystrophy after treatment with voretigene neparvovec.

To investigate the changes in retinal arterial architecture after treatment with voretigene neparvovec in patients with retinal dystrophy caused by bi-allelic mutations in the RPE65 gene. Sixteen eyes treated with voretigene neparvovec at the University Eye Clinic in Tuebingen, Germany, underwent adaptive optics ophthalmoscopy (AO) imaging at baseline and 2 weeks, 1, 3, 6 and 12 months after treatment. Follow-up was performed in six eyes of four patients. For each eye, five different positions at arterial vessels were selected and the wall-to-lumen ratio (WLR), the lumen diameter (LD) and the wall cross-sectional area (WCSA) were measured by the manufacturer's software over the observational period. Vast retinal atrophy dominated all gained AO images. WLR fluctuated in the observation period without statistically significant change. LD and WCSA changed significantly after 2 weeks from the baseline examination and returned to values similar to baseline thereafter. There were no signs of inflammation such as macrophages or perivascular accumulated fluid visible. AO imaging of the retinal vessels in RPE65-associated retinal dystrophies (IRD) is challenging. There was no change in the retinal arterial vasculature over the observation period of 12 months that would indicate inflammatory changes. Decrease of the LD and WCSA shortly after treatment might be caused by the perioperative prednisolone intake. AO of retinal vessels can be used as a diagnostic module to complement monitoring the disease and effects of genetic treatments if the acquisition is possible in selected cases.

Preliminary validation of the Klenico diagnostic software self-report module through comparison with the diagnostic gold standard in an outpatient routine clinical sample

ABSTRACT Background Inaccuracy in current diagnostic procedures for mental disorders can lead to misdiagnosis and increase the burden on the healthcare system. Therefore, Klenico, a diagnostic software designed to support comprehensive and efficient clinical diagnostic procedures that is easy to apply in everyday clinical practice, was developed. This study aimed to take the first step toward validating the Klenico self-report module. Methods Data of 115 patients from a German psychotherapeutic outpatient clinic were included in this study. Criterion validity was tested by comparing Klenico with the diagnoses based on the structured clinical interview for DSM-IV (SCID). Construct validity was investigated by comparing Klenico with commonly used self-reporting questionnaires. Results The results showed that most of the Klenico disorder domains were able to differentiate between corresponding diagnoses and other diagnoses, confirming criterion validity. Construct validity was demonstrated by high correlations with the compared convergent questionnaire scales and non-significant or low correlations with most of the divergent scales. Conclusions These preliminary results demonstrate the psychometric properties of the Klenico self-report module and imply that the Klenico system has high potential to improve the accuracy of diagnostic procedures in everyday clinical practice.

Prevalence of Cannabis Use Disorder and Reasons for Use Among Adults in a US State Where Recreational Cannabis Use Is Legal.

Medical and nonmedical cannabis use and cannabis use disorders (CUD) have increased with increasing cannabis legalization. However, the prevalence of CUD among primary care patients who use cannabis for medical or nonmedical reasons is unknown for patients in states with legal recreational use. To estimate the prevalence and severity of CUD among patients who report medical use only, nonmedical use only, and both reasons for cannabis use in a state with legal recreational use. This cross-sectional survey study took place at an integrated health system in Washington State. Among 108 950 adult patients who completed routine cannabis screening from March 2019 to September 2019, 5000 were selected for a confidential cannabis survey using stratified random sampling for frequency of past-year cannabis use and race and ethnicity. Among 1688 respondents, 1463 reporting past 30-day cannabis use were included in the study. Patient survey-reported reason for cannabis use in the past 30 days: medical use only, nonmedical use only, and both reasons. Patient responses to the Composite International Diagnostic Interview-Substance Abuse Module for CUD, corresponding to Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition CUD severity (0-11 symptoms) were categorized as any CUD (≥2 symptoms) and moderate to severe CUD (≥4 symptoms). Adjusted analyses were weighted for survey stratification and nonresponse for primary care population estimates and compared prevalence of CUD across reasons for cannabis use. Of 1463 included primary care patients (weighted mean [SD] age, 47.4 [16.8] years; 748 [weighted proportion, 61.9%] female) who used cannabis, 42.4% (95% CI, 31.2%-54.3%) reported medical use only, 25.1% (95% CI, 17.8%-34.2%) nonmedical use only, and 32.5% (95% CI, 25.3%-40.8%) both reasons for use. The prevalence of CUD was 21.3% (95% CI, 15.4%-28.6%) and did not vary across groups. The prevalence of moderate to severe CUD was 6.5% (95% CI, 5.0%-8.6%) and differed across groups: 1.3% (95% CI, 0.0%-2.8%) for medical use, 7.2% (95% CI, 3.9%-10.4%) for nonmedical use, and 7.5% (95% CI, 5.7%-9.4%) for both reasons for use (P = .01). In this cross-sectional study of primary care patients in a state with legal recreational cannabis use, CUD was common among patients who used cannabis. Moderate to severe CUD was more prevalent among patients who reported any nonmedical use. These results underscore the importance of assessing patient cannabis use and CUD symptoms in medical settings.