To avoid the low engagement and limited efficacy of digital behavioral health interventions, robust human-centered design (HCD) processes are needed.iSIPsmarter as an example. iSIPsmarter is a digital intervention for reducing the consumption of sugar-sweetened beverages (SSBs) that comprises 6 internet-based cores metered out over time to deliver the program content, an integrated SMS text message strategy to engage users in reporting SSB behaviors, and an electronic cellular-enabled scale for in-home weighing. The secondary objective is to illustrate the key components and characteristics of iSIPsmarter that resulted from the HCD process.The primary objective of this study was to describe a flexible, step-by-step HCD process to develop digital behavioral health interventions by illustrating Model for Internet Interventions and by best practices in HCD and instructional design processes . The 3-phased  process followed in this study included a series of 13 semistructured one-on-one interviews with 7 advisory team participants from the targeted Appalachian user group. The interviews were content coded by 2 researchers and then deductively coded to the suggested areas of digital behavioral health interventions.The methods were guided by the iSIPsmarter’s appearance, behavioral prescriptions, burdens, content, delivery, message, participation, and assessment. These inputs included requests for built-in flexibility to account for varying internet and SMS text message accessibility among users; ideas to resolve the issues and problems encountered when using the prototypes, including those related to navigation and comprehension of content; ideas to enhance personalized feedback to support motivation and goal setting for SSB consumption and weight; and feedback to refine the development of realistic and relatable vignettes. The participants were able to interact with multiple prototype drafts, allowing researchers to capture and incorporate feedback related to the iSIPsmarter dashboard, daily SSB and weight diaries, action planning, core content, interactions, and vignettes.The participants provided rich perspectives pertaining to iSIPsmarter intervention that were hypothesized to support users’ engagement and promote behavior change. As evidenced by the rich feedback received from the advisory team members and the resulting iSIPsmarter product, the HCD methodology was instrumental in the development process. Although the final iSIPsmarter content is specific to improving SSB consumption behaviors among adults in rural areas, the intent is that this HCD process will have wide applications in the development of digital behavioral health interventions across multiple geographic and behavioral contexts.Using scientific models and established processes is critical for building robust and efficacious interventions. By applying an existing model and HCD and instructional design processes, we were able to identify assumptions and address the key areas of the  The availability of digital behavioral health interventions has surged in recent years. However, when deployed in research trials and disseminated in real-world practice, the uptake of and engagement with many digital interventions are often lower than desired -3. In tuModel for Internet Interventions [Model for Internet Interventions highlights 8 main areas that comprise the digital health application used to deliver the intervention . To improve the likelihood of digital health applications meeting the needs and requirements of users, HCD and instructional design processes should be applied when considering and manipulating these 8 areas.Importantly, there are a number of models and frameworks available to guide the digital intervention development processes -9. The Mventions  is a keyventions -13. Thisventions . FurtherAlthough this and other models and frameworks provide helpful principles and guidelines -9, they Despite the flexibility in methodological processes, the application of HCD and instructional design processes in the development of digital behavioral health interventions is consistently recommended . The IntComplementary to HCD concepts, instructional design processes involve designers executing cycles of continuous formative evaluation to ensure that the intervention meets the users’ needs, prior knowledge, and experience . These iAlthough HCD processes are important across different contexts, the need for them is magnified in rural regions, including Appalachia. Historically, extending digital interventions into the Appalachian region has been hindered by digital divide ,22. HoweIn addition to the geographic and digital divide context of Appalachia, the behavioral context of sugar-sweetened beverage  consumption is noteworthy. SSBs are the single largest source of calories in the US diet and account for approximately 7% of the total energy intake of the US adults . In AppaiSIPsmarter as an example. iSIPsmarter is a technology-based behavior and health literacy intervention aimed at improving SSB consumption behaviors among Appalachian adults. The secondary objective is to illustrate the key components and characteristics of iSIPsmarter that resulted from the HCD process. Although iSIPsmarter’s content is specific to improving SSB consumption behaviors among Appalachian adults, the intent is that the HCD process will have wide applications in the development of digital behavioral health interventions across multiple geographic and behavioral contexts.The primary objective of this study was to describe a flexible, step-by-step approach to and an HCD process for developing digital behavioral health interventions by illustrating iSIPsmarter was guided by the Model for Internet Interventions [iSIPsmarter included 13 semistructured interviews across three nonsequential iterative phases: (1) contextual, (2) prototype testing, and (3) end user testing phases.The development process for the digital behavioral health intervention ventions  and by bventions  and instventions -20, whicThis formative, flexible, and step-by-step approach allowed the advisory team participants to provide insights at all phases of the development process and interact with multiple prototype drafts developed by the content development team (CDT).iSIPsmarter intervention, and adaptation process. Subsequently, the 3 phases of data collection and the data analysis strategy are described.The following sections describe the considerations for the CDT, advisory team participants, adaptation context, This program evaluation project involved activities that do not represent human participant research and, therefore, did not require submission to the institutional review board (Human Research Protection Program Standard Operating Procedures ).The development of digital behavioral health interventions should be conducted by a multidisciplinary CDT ,16,35. TiSIPsmarter included 4 doctoral-level researchers: 2 (50%) SSB content experts who had led prior SSB behavioral intervention trials in the targeted Appalachian region, 1 (25%) expert on digital behavioral health technology interventions, and 1 (25%) instructional design expert. This team was further supported by research staff experienced in nutrition, public health, and digital health development and implementation, including those with expertise in the design and creation of robust user interface and user experience. For the duration of this HCD process, the team met weekly to collaboratively draft advisory team interview guides with clear objectives, develop prototypes, and respond to feedback from the participants. Likewise, the CDT also wrote content for the iSIPsmarter cores, developed media-rich interactions and videos, drafted personas and vignettes, and developed an integrated internet-based platform.The multidisciplinary CDT involved in the development of When developing digital behavioral health interventions, the selection of advisory team members is an important decision. Ideally, the participants should represent the intended end user of the intervention in terms of demographic and cultural characteristics, health literacy and digital literacy skills, geographic location, and patterns of the targeted behaviors .iSIPsmarter were a convenience sample of prior SIPsmartER participants who resided in the Appalachia region. The advisory team members also had to have internet access and the ability to review the iSIPsmarter program materials on the web. These members were diverse in terms of age (21 to 60 years), gender, and socioeconomic status. Of the 15 prior SIPsmartER participants invited, 7 (47%) joined the advisory team. These participants were involved throughout the intervention design and development processes, with the first interview conducted in February 2019 and the final interview in December 2021. In total, the advisory team members were compensated with up to US $200 in electronic gift cards for their participation.The advisory team members involved in the development and design of iSIPsmarter is a technology-based behavior and health literacy intervention aimed at improving SSB behaviors among Appalachia adults [iSIPsmarter was adapted from the evidence-based intervention SIPsmartER [smartER is a 6-month intervention that includes 3 in-person small-group classes, 1 live teach-back call, and 11 interactive voice response (IVR) calls; the intervention also comprises a 12-month maintenance phase that includes monthly IVR calls . The cl[smartER -41. The concepts -46. In bVirginia ,37,47-58iSIPsmarter, the CDT had ample experience understanding and intervening on SSB consumption in the targeted region and had over a decade’s worth of rich qualitative, observational, and experimental data from the previous SIPsmartER trials. As such, content and behavior change techniques related to SSB consumption were relatively well established when embarking on the digital intervention development. However, little was known about the opportunities, barriers, and access to technology in the targeted population for intervention delivery or data collection purposes. Similarly, weight self-monitoring and the use of cellular-enabled scales to encourage in-home weighing had not been previously explored in the targeted population and region. Therefore, the intention of the iSIPsmarter adaptation process was largely focused on changing the mode of delivery and adding key content around weight while maintaining other core components and the cultural relevance for the intended Appalachia target audience.At the onset of developing iSIPsmarter comprises 6 internet-delivered interactive cores, which are metered out sequentially over time, with a new core becoming available 1 week after completion of the previous core; an integrated SMS text message strategy to engage users in tracking and reporting SSB consumption behaviors; and an electronic cellular-enabled scale for in-home weighing [iSIPsmarter is highly interactive and contains a media-rich format of text, audio, graphics, animation, interaction, and video. Vignettes are woven throughout the intervention, which include stories (based on the experiences of past SIPsmartER participants) to model and describe situations related to setting goals, behavior changes, resolving barriers, and encountering slips. Finally, iSIPsmarter includes a stepped care engagement strategy  to provide encouragement, technical assistance, and strategies to promote core completion. The adaptation processes, HCD approach, and findings leading to the final content and structure of iSIPsmarter are further detailed in the subsequent sections.In its final form, weighing . At the Model for Internet Interventions highlights 8 areas to consider when developing digital behavioral health interventions . This process resulted in 90% of the iSIPsmarter core content pages meeting the criteria of <eighth grade level.Content refers to the actual intervention information and may be the single most important component of the program . Focus irvention ,37. The d length . iSIPsmaiSIPsmarter core, videos and interactions were developed using gamification principles to encourage the users to engage in learning and practicing key behavioral strategies and techniques [smartER small-group classes, personas and vignettes were developed for iSIPsmarter. Personas are defined as user archetypes that represent the characteristics of future users or actual people from a targeted group [smartER participants and intended users of the digital iSIPsmarter intervention, 9 personas were identified and developed to represent the range of traits and SSB consumption change patterns that the CDT witnessed among SIPsmartER participants. These personas detailed numerous user characteristics, such as (1) demographics and family or social characteristics, (2) SSB consumption change patterns and barriers, (3) weight-related patterns and barriers, (4) motivation level and perceived behavioral control, and (5) the use of planned digital technology components. Then, journey maps were created to share with the advisory team participants. These journey maps were single-page snapshots modeling each persona’s potential path through iSIPsmarter, including experiences, successes, and challenges with improving SSB consumption and weight behaviors. Feedback was solicited from the advisory team participants. On the basis of the participants’ insights, the personas were narrowed down and further refined. Next, vignettes  were developed for each persona to model and describe situations related to setting goals, behavior changes, resolving barriers, and encountering slips. Finally, vignettes were mapped to key content through each of the iSIPsmarter cores and several of the interactions.Delivery refers to the form in which the intervention content is disseminated and includes text, audio, illustrations or graphics, animations, video, and vignettes or stories or testimonials . Particichniques . The inted group . VignettiSIPsmarter, the personalized action planning process, behavioral monitoring, and personalized feedback loop were transitioned from the original format of small-group classes and IVR calls in SIPsmartER to a fully digital format. The web development team built iSIPsmarter on a proprietary software platform called the Research Infrastructure Containing eHealth (RICE) interventions, developed for building digital health programs. The RICE platform integrates all aspects of a digital intervention for a seamless user experience and research administration. iSIPsmarter provides an opportunity to extend the technological capability of the intervention infrastructure by incorporating SMS text message and sensor integration, specifically, the BodyTrace scale . Daily SMS text message prompts are sent to encourage users to report the of ounces of SSBs consumed the previous day. In addition, an electronic cellular-enabled BodyTrace scale is provided for weight data collection. Users are encouraged to step on the scale daily, with a minimum threshold of 3 days per week, to receive personalized feedback. The integration of the internet-based platform, SMS text messages, and cellular-enabled scales created an integrated experience, whereby users can log into the internet-based platform and view their progress through the cores, along with synced personal diary data (SSB consumption and weight) on their iSIPsmarter dashboard. When appropriate, gamification principles are also applied when providing tailored feedback and recommendations  [Behavioral prescriptions instruct the user on how to address the targeted problem. They are designed to increase commitment and boost adherence and may include, for example, behavioral contracts as well as automated and personalized prompts  . Assessmentered) .iSIPsmarter, understanding the burden of the users was prioritized when developing features associated with diary tracking, dashboard navigation, the action plan process. Given that goal setting, planning, self-monitoring, and feedback are among the most important behavior change techniques, understanding and resolving potential user burdens with the digital content was especially important. In addition, attention was paid to the overall length of each core and to balancing the amount of content with the projected user fatigue.Burdens are specific to the intervention content and can include problems related to use, such as poor application navigation and program length . In iSIPiSIPsmarter, a graphic designer developed key images and icons with a consistent theme and style. These graphics were applied throughout the intervention, including all core screens and printable documents.Appearance refers to the look and feel of the application and can include the use of color, page or screen layout, organization of content, and screen size . For iSIModel for Internet Interventions  [iSIPsmarter development process. As an example, the model describes supports  that can directly impact users’ adherence to digital health applications. During the development and design of iSIPsmarter, interviews were conducted with the advisory team members to assess their understanding and perceptions of program reminders and gather inputs on the timing and content of stepped care messages.The ventions  also illion use) . Many ofiSIPsmarter components were developed using a flexible and iterative 3-phased HCD process. This process included semistructured interviews with the advisory team participants. For each interview, the CDT collaboratively developed key objectives and designed interview guides during weekly team development meetings. One of the researchers drafted the initial questions based on the focus area and agreed upon objectives. Subsequently, other team members provided feedback until a final version was agreed upon. All the interview guides used open-ended questions and probes. The interview content was prioritized by the current development activities. Therefore, it was possible to follow an ongoing, iterative, HCD process that matched the steps and pace of the program development. One or 2 researchers led the audio-recorded interviews. The interviews were completed over telephone and via Zoom (Zoom Video Communications Inc). The sessions ranged from 45 to 60 minutes.The Model for Internet Interventions  [iSIPsmarter use as well as research evaluation components.The contextual phase of the HCD process involves gathering information from the intended users regarding their behaviors and their context and requirements for using the technologies through which the digital health interventions will be implemented ,16,18. G change) , the 4 iThe initial advisory team meeting explored participants’ technology ownership and use, internet availability and use, and perceived benefits of and barriers to receiving a digital behavioral health intervention. The 3 subsequent contextual interviews assessed the participants’ understanding and perception of program reminders and gathered feedback on the timing and content of stepped care messages. Likewise, the participants’ experiences with daily weighing on scales and perceptions toward using cellular-enabled scales for a digital health intervention were evaluated. Finally, the participants’ feedback on the research outcome data components that were assessed was solicited via an internet-based survey, telephone calls, and cellular-enabled scales, all of which were completed by the participants in the comfort of their homes.Prototype testing includes the visual representation of the to-be-developed technology, where the CDT explored different concepts and possible solutions with the intended users. Testable prototypes can take many forms, such as paper prototypes, mock-ups, and wireframes  ,16,18. PiSIPsmarter prototypes developed in this phase were paper- and web-based sketches that illustrated planned scenarios. The participants were sent links to the web-based prototypes, which included preprogrammed pages that displayed various feedback points as the participants moved through the content. This intentional rapid prototyping allowed the participants to interact with multiple prototype drafts and allowed the researchers to incorporate iterative feedback from the participants. The think-aloud method, which is a common approach to assessing the usability of digital health interventions and involves the participants verbally narrating their thoughts when completing a task related to the prototypes, was used. The researchers guided the process and asked the participants to complete specific tasks. Open-ended questions allowed for a robust understanding of usability and functionality. The interviewers documented where the participants encountered problems and difficulties using the prototypes.The iSIPsmarter user dashboard, daily SSB diary, action planning, and vignettes. The prototypes of the iSIPsmarter user dashboard and daily SSB diary were shown to the participants multiple times. The participants were asked to interact with the dashboard prototypes to collect data on their comprehension of the dashboard interface. This included navigating the dashboard to start a core (lesson) and add a daily SSB diary. Diary prototypes were also tested to collect data on the users’ understanding of adding daily SSB ounces. In addition, the participants were presented with SMS text message screenshots to assess their comprehension of daily SSB consumption tracking features. Similarly, prototypes of SSB and weight action plans were presented to the users to inform the development of a key intervention behavior change technique, personalized goal setting. Moreover, as previously mentioned, the journey maps were shared with the advisory team participants, and feedback was solicited to help narrow down, refine, and develop the vignettes associated with the personas.This phase included 6 interviews to assess the participants’ comprehension and experience and the overall prototype functionality. The prototypes largely focused on the design and content of the In this phase, the advanced functioning prototype or beta versions of the application are exposed to the end users and evaluated ,18. ThisiSIPsmarter, once the prototypes transitioned into the web-based RICE platform, the program-enabled website underwent numerous rounds of internal review by the CDT for clarity of content, flow, and transitions and for addressing programmatic bugs. Subsequently, the end user testing phase began. The advisory team participants were given access to the iSIPsmarter website in their own environment. They completed the cores, including the embedded interactions, videos, and vignettes. Furthermore, they tracked their SSB intake and weight using the SMS text message feature and electronic cellular-enabled scale, respectively. A total of 3 semistructured interviews explored the usability and functionality of and user experience and satisfaction with the iSIPsmarter website.For After each round of interviews, the researchers who conducted the interviews reviewed the audio transcripts and created interview summary documents that summarized each participant’s response to each question. Following each interview series, these summary documents with screenshots were shared with the CDT to incorporate feedback into the ongoing intervention content and programmatic development.Model for Internet Interventions. During this second step, they also used CDT meeting minutes and artifacts from the development process  to inform higher-level findings. Again, the 2 researchers met to build consensus on the overarching phase-level findings. Finally, to illustrate the phase-level findings, key quotes from the advisory team members were extracted from the transcripts.The analysis and data interpretation process involved several steps ,62. FirsModel for Internet Interventions, are further summarized below.Interviews 1, 3, 11, and 12 focused on the contextual and technology-related aspects of intervention delivery and data collection. The following key findings emerged:participation, the participants described the need for built-in flexibility to account for varying levels of internet and SMS text message accessibility .To promote assessments.The participants reported the need for accountability and personalized support) that could boost adherence and core completion .Plans for stepped care contacts were viewed as a helpful and important intervention component (participation) and personalization (assessment), yet barriers to weighing varied based on user characteristics and environmental factors, such as limited cellular service to transmit weights.The participants saw value in a weight monitoring component to promote reinforcement . For example, the participants who prefer paper diaries could first log their daily diary data via pencil and paper. Then, they could log into their dashboard weekly and back enter their diary data. Alternatively, the participants who prefer the digital methods could respond to the daily SMS text messages or email messages.The participants felt that flexibility in diary tracking methods and resources  would enable high engagement and burden), with clear content, and contained helpful tailored user information (assessment), including visual cues to signify the completion of the core and diary tasks (appearance).Through multiple iterations, the dashboard became easy to navigate (low burden); the content and delivery features were clear; a personalized and tailored feedback was perceived to be helpful when setting and monitoring SSB and weight goals .The action planning process was easy to navigate . Our approach can be interpreted within the context of the findings of recent narrative scoping review by Kip et al [This paper presents a flexible, step-by-step approach to and an HCD process for developing digital behavioral health interventions using ventions  as well ventions  and instventions -20, we hip et al , in whicip et al , our appiSIPsmarter development process, some of the biggest challenges faced when transitioning from the original group class structure to the digital structure include (1) replicating engagement provided by participant-to-participant and participant-to-instructor communication and relationships; (2) balancing the demands and cognitive load of the behavior change content  and trying to mimic visual, experiential, and hands-on class-based activities; (3) building flexibility to account for varying levels of internet and SMS text message accessibility among users; (4) creating a seamless user experience by integrating internet, SMS text messages, and sensor information; (5) operationalizing all aspects of the intervention content for digital delivery; and (6) automating all elements while ensuring that all permutations were considered. Similar challenges have been highlighted in a few other digital behavioral health intervention adaptation papers [Model for Internet Interventions [In terms of the n papers . Howeverg users;  creatingventions , to helpAlthough several behavioral interventions targeting SSB consumption reduction among adults have been developed and evaluated , only a iSIPsmarter, we offer 6 broad considerations for other teams developing or adapting digital behavioral health interventions  with 4 assessment points  [iSIPsmarter as well as identify future areas of study.In terms of study implications, the efficacy of 5030753) . When efAssemble a multidisciplinary team of experts and end users: similar to our iSIPsmarter experience, the value of multidisciplinary team science and participatory processes in the development of digital behavioral health interventions is largely supported by other studies [ studies ,16,18,35Support efficient communication and decision-making processes among teams: anticipating diverse feedback among different stakeholders, coordinating efficient communication among subteams, and finding a compromise are imperative to efficiently advance the HCD process [iSIPsmarter. In some instances, the advisory team requirements were different and contradictory to one another and to the requirements of the content development team. process ,18. For Define areas of adaptation at the onset of the process: Similar to the adaptation of any behavioral intervention, the adaptation of digital behavioral health interventions can be driven by several distinct purposes. For example, the Adaptome [iSIPsmarter from the evidence-based SIP smart ER trial with the clear goals of preserving the core components, cultural aspects, and the intended rural Appalachia target audience. This allowed us to concentrate on the mode of delivery and add key content around weight management. For example, we were able to focus end user feedback on potential digital divide concerns in Appalachia [iSIPsmarter with flexible features intended to enhance the likelihood of engaging in web-based cores, SMS text messages, and in-home weighing using cellular-enabled scales.Adaptome  describepalachia -26. ThisApply the available models and frameworks to guide digital intervention development processes: Despite the promise and increased availability of digital behavioral health interventions, rapid disengagement and small effect sizes remain problematic [blematic . Althougblematic -9.Clearly define instructional design goals to guide the HCD process: To improve the likelihood of digital health applications meeting the needs and requirements of the users, instructional design objectives  should be applied in the HCD process [iSIPsmarter interviews, we were interested in evaluating the participants’ knowledge, problem-solving skills, attitudes, and values associated with completing certain tasks . By having clear instructional design objectives, we were able to better understand and modify iSIPsmarter features to meet the needs of the users and support the achievement of improved sugar-sweetened beverage consumption behaviors. process ,16. For Allocate appropriate resources and time to successfully execute HCD processes: The time and resources required to develop or adapt digital health interventions can vary widely; however, they are often underestimated. For example, our iterative 3-phased HCD process was nearly an 18-month process. Researchers and developers should carefully consider and anticipate investment in robust HCD processes, as these processes are likely critical to the long-term uptake, engagement, and impacts of most digital health interventions.iSIPsmarter. Third, although we applied the Model for Internet Interventions, we limited the focus and scope of our manuscript to highlight the 8 main areas that comprise digital health applications [iSIPsmarter adaptation process. Despite these potential limitations, we hope that the illustrated processes, scientific frameworks, and context-specific instructional design methodology will have wide applications in the development of digital behavioral health interventions across multiple geographic and behavioral contexts.Overall, 3 key limitations of this study should be considered. First, generalizability may be limited by the relatively small sample of advisory team members, the targeted rural Appalachia region, and SSB-specific content. Second, qualitative interviews can result in socially desirable responses. However, efforts were made by our trained interviewers to minimize this potential limitation by probing and clarifying the participant responses. This approach resulted in the participants providing thoughtful and constructive critiques of ications . Future iSIPsmarter adaptation process. The importance of the contextual and iterative prototype testing phases was largely reinforced by the overwhelming positive feedback received in the user testing phase. By illustrating iSIPsmarter content, we have highlighted the user-informed, high-quality products that resulted from our robust HCD process.Our process emphasizes the value of researchers and developers applying the existing models and frameworks as well as best practices in HCD and instructional design processes in digital intervention development processes. Together, the complementary skills of the CDT and advisory team members were invaluable in the


The COVID-19 pandemic decreased the in-person outpatient visits and accelerated the use of telehealth services among mental health patients. Our study investigated the sociodemographic and clinical correlates of the intensity of telehealth use among mental health patients residing in rural Louisiana, United States. The study sample included 7069 telehealth visits by 1115 unique patients encountered from 1 April 2020 to 31 March 2021 at six mental health outpatient clinics managed by the Northeast Delta Human Services Authority (NEDHSA). We performed a negative binomial regression analysis with the intensity of service use as the outcome variable. Being younger, female, and more educated were associated with a higher number of telehealth visits. The prevalence of other chronic conditions increased telehealth visits by 10%. The telehealth service intensity varied across the nature of mental health diagnoses, with patients diagnosed with the schizophrenia spectrum and other psychotic disorders utilizing 15% fewer telehealth visits than patients diagnosed with depressive disorders. The promotion of telehealth services among mental health patients in the rural setting might require the elimination of the digital divide with a particular focus on the elderly, less educated, and those with serious mental health illnesses such as schizophrenia and psychotic disorders. Mental health disorder is one of the leading causes of disability and morbidity globally and in the United States ,2. AlthoThe grief and physical isolation during the COVID-19 pandemic exacerbated the psychological distress to an unprecedented level, raising alarms about the mental health conditions across the country. At the same time, the government undertook multiple measures in containing the spread of the virus including the declaration of a nationwide emergency and the implementation of stay-in-home policies that actively discouraged in-person medical visits for non-life-threatening conditions  and masspredisposing, enabling, and need factors [predisposing factors are centered on sociodemographic characteristics  and attitude toward the treatment or illness. The enabling factors include financial and organizational resources including health care accessibility components such as income, community resources, social support, and service availability. The need factors capture the illness-related factors such as the co-occurrence of multiple chronic conditions and type and nature of the illness. Predisposing, enabling, and need factors, together, are important in explaining and predicting healthcare service use. Knowledge about the relative contribution of these factors might help inform the state of disparity in healthcare service use and might provide guidance on the best course of public health action [A central focus of the public health and clinical research on mental health has been on improving healthcare utilization, which might be affected by various factors such as socioeconomic context, health attitudes and beliefs, and physical and mental status of health . Anderse factors ,21. The h action .In this paper, we explore the dynamics of telemedicine service utilization by mental health patients encountered during the COVID-19 pandemic in a health system serving rural communities in Louisiana, which is a state with relatively poor overall health and quality of life . In partOur research is focused on the twelve, mostly rural parishes  of northeast Louisiana: Union, Lincoln, Jackson, Ouachita, Caldwell, Morehouse, Richland, Franklin, Tensas, Madison, East Carroll, and West Carroll. The region has a high prevalence of food insecurity, poverty, and mental health challenges . The meaThe Northeast Delta Human Services Authority (NEDHSA) is one of the local government entities of the Louisiana Department of Health and provides healthcare services to individuals with mental health challenges, developmental disabilities, and addictive disorders. NEDHSA serves the residents of northeast Louisiana through its six outpatient behavioral health clinics, which are located in Caldwell, Morehouse, Ouachita, Lincoln, Madison, and Franklin parishes. Trained health professionals working for NEDHSA provide services in the outpatient clinics. This study uses patients aged 18 years or older who received psychiatric care in NEDHSA outpatient clinics between 1 April 2020 and 31 March 2021. Out of the 7105 total services that NEDHSA provided during the study period, only 36 visits were physical visits, and thus, the associated patients were excluded from the analysis. A total of 7069 telehealth visits by 1115 unique patients were included in this study. Patient demographics, primary diagnosis, service date and type, number of visits, and discharge were obtained from NEDHSA’s electronic health record system. The Diagnostic and Statistical Manual of Mental Disorder, Fifth Edition (DSM-5-TR) was used to classify mental illness diagnosis and reason for visits. Informed consent was obtained from patients, and all study procedures were reviewed and approved by the Institutional Review Board of the Louisiana Department of Health.The intensity of visit, defined as the number of total visits by a patient during the study window, is the dependent variable. Because the outcome is a count-variable, the conventional way of modeling it is by the Poisson point process  such thaIn Poisson distribution, the first moment (mean) should be equal to the second moment (variance):However, the equidispersion property of Poisson distribution often becomes violated in a real scenario . In the X is the vector of covariates , and We perform negative binomial regression analysis with the intensity of visit as the outcome variable and predisposing, enabling, and need factors as explanatory variablessed data ,32,33. Tsed data .Our study included 7069 telehealth visits by 1115 unique patients from 1 April 2020 to 31 March 2021. Patients’ demographic and clinical characteristics are summarized in The prevalence of mental health disorders among U.S. adults living in urban and rural areas is almost the same . HoweverTo provide effective treatment and appropriate allocation of clinical resources, it is important to understand the factors that determine the utilization of mental health services. Decision making in the utilization of healthcare services and engagement in ongoing treatment is affected by various factors such as sociodemographic characteristics, health attitudes and beliefs, and physical and mental health status . Poor enWe find that several demographic and socioeconomic factors help determine the level of mental health service utilization. First, we find the intensity of telehealth service use among elderly patients to be lower. Although the intensity of mental health visits for the individuals in the older age group is higher ,40, thisWith regard to the need factors in Anderson’s model, we find that several clinical factors are associated with the intensity of telehealth service use. Unsurprisingly, telehealth service use intensity is lower for discharged patients and higher for patients diagnosed with multiple mental health disorders. Moreover, individuals with chronic health conditions are more likely to suffer from mental illness compared to those not having chronic health conditions and may require more hospital visits ,48. For There are several strengths of this study. First, we explore the factors associated with the intensity of outpatient mental health clinic visits in a rural setting during a period of a national health emergency. This is particularly important, as there is increasing use of telehealth services after the onset of the COVID-19 pandemic, while the studies on its effectiveness, especially among mental health patients in rural areas, remain limited. Second, unlike other studies that examine the intensity of clinic visits based on patients’ self-report, we did not use the self-reported survey data, which might be subject to recall bias or social desirability bias. Instead, we used the electronic health records of the patients encountered during an outpatient clinic visit, which likely offers a more objective picture of the use of health services. Our major limitation in the study is that we only considered the primary mental health diagnosis of the patient, even though there were many patients with multiple mental health conditions. Whereas we controlled for the number of diagnoses in the model, there remains the issue of identifiability of the effect of each single mental health diagnosis.On the whole, our study speaks to the importance of removing barriers in telehealth service use among mental health patients living in rural communities. Because rural dwellers are affected by the existing digital divide, are more likely to be an older age group, are less likely to have education completed until high school, and are more likely to develop chronic health conditions , attentiEffective treatment of mental health conditions is largely dependent on the optimum utilization of healthcare services. The COVID-19 pandemic disrupted traditional in-person medical care, and health providers adapted to telehealth services. This study examined the trend of telehealth visits and explored the factors associated with the intensity of service use among rural dwellers. Using Anderson’s behavioral model of healthcare utilization as the theoretical framework, we find that several predisposing and need factors help determine the level of telehealth service use. The elderly, less educated, and mental health patients with schizophrenia are less likely to use telehealth services. Policies targeted to reduce the digital divide and tailored to specific sub-classes of mental health illnesses could be helpful in promoting telehealth services to rural residents.


Osteoarthritis (OA) is a common disease that severely restricts human activities and degrades the quality of life. Every year, millions of people worldwide are diagnosed with osteoarthritis, placing a heavy burden on society. Hydrogels, a polymeric material with good biocompatibility and biodegradability, are a novel approach for the treatment of osteoarthritis. In recent years, this approach has been widely studied with the development of materials science and tissue engineering technology. We reviewed the research progress of hydrogels in the treatment of osteoarthritis in the past 3 years. We summarized the required hydrogel properties and current applications according to the development and treatment of osteoarthritis. Furthermore, we listed the challenges of hydrogels for different types of osteoarthritis and presented prospects for future development. Osteoarthritis (OA) is a degenerative arthropathy that causes joint pain, limited joint motion, and joint deformity . During Currently, relatively advanced treatment methods, commonly including intra-articular drug injection and cytological treatment, cannot provide satisfactory results for the treatment of OA. Intra-articular drug therapy may increase drug bioavailability and reduce the risk of systemic toxicity. However, most drug preparations used for intra-articular injection are dissolved in solution, and after injection, the drug rapidly spreads into the systemic circulation, resulting in a short retention time and a limited therapeutic effect. The cartilage layer is a dense structure filled with collagen and proteoglycans, which hinder drug penetration. In recent years, cell-based therapy has focused more on cartilage regeneration. Stem, progenitor, or primary cells are used to replace or repair OA-damaged tissue . HoweverHydrogels comprise natural or synthetic hydrophilic polymer chains interconnected at crosslinking points. They have a unique three-dimensional network structure, material exchange ability, good biodegradability, and adjustable mechanical properties and provide a suitable microenvironment. They have considerable application prospects in tissue engineering regeneration and repair . TherefoCurrently, hydrogel products made from natural, synthetic, and natural–synthetic mixed-base materials have been recently developed, and an increasing number of studies focus on the use of hydrogel products in the treatment of osteoarthritis . In receIn this study, clinical treatment needs were adopted as the cut-in point. First, the pathological changes of osteoarthritis and molecular mechanisms that affect the treatment process of hydrogels were reviewed and the characteristics of hydrogels required for the treatment of different degrees of osteoarthritis lesions were summarized accordingly. Finally, we emphasize the tailor-made strategy for the performance of hydrogels of different treatment methods to provide the possibility of individualized treatment of osteoarthritis, thus meeting the requirements of clinical diagnosis and treatment.The initial pathological changes that occur at the onset of osteoarthritis are unclear; however, it is certain that various pathological changes associated with osteoarthritis are aggravated in combination, including cartilage degradation, subchondral osteosclerosis, angiogenesis, and synovitis. The difficulty of treating osteoarthritis with hydrogels is attributed to the bone’s particular internal structure and inflammatory environment, which makes both drug and cytological therapies difficult. According to previous studies, the internal environment of osteoarthritis is mainly hypoxic: the precise oxygen tension in the cartilage is estimated to be approximately 1–5%, and the gradient from the cartilage surface to the deeper area steadily decreases due to the absence of blood supply in the articular cartilage. Stem cells, which do not adapt to hypoxic conditions at the joint, cause disorders in cellular therapy for osteoarthritis . SecondlHydrogel materials can be adopted to treat osteoarthritis in several ways. 1) The mechanical properties of hydrogels, including pressure resistance and lubricity, are used to alleviate joint inflammation or repair the defect tissue. 2) Hydrogel can be used as the carrier of drug transportation and be implanted into the affected area by injection and open surgery, such that the utilization rate and effect of the drug can be improved. 3) Hydrogels can be used as scaffolds for cellular transport and promote the adhesion, proliferation, and differentiation of stem cells in cytological therapy.In terms of clinical treatment, the progress of osteoarthritis can be divided into three stages. 1) In the early stage of osteoarthritis, due to the rise in abnormal shear force in the joint cavity, the secretion of chondrocytes and the ability of proliferation are weakened. An aseptic inflammatory reaction appears in the local part of the joint cavity, resulting in the decrease of joint lubrication ability in the joint cavity. The patient experiences pain during exercise, but no evident organic pathological changes are found at this time. The elimination of local inflammatory factors, improvement of the inflammatory microenvironment, and increase in the lubrication ability of the joint cavity can significantly alleviate the joint inflammatory symptoms . 2) DuriStudies have suggested that exploring effective treatments to prevent or slow the disease progression is of great interest for OA . In histAlmost frictionless joint movements play a crucial role in daily life. The degradation of biomolecules in the synovial fluid increases cartilage wear, leading to the most common joint disease, osteoarthritis (OA) . This fiin vitro release profile showed a sustained release pattern of HA from PLGA particles. The particles exhibit good in vitro cellular compatibility, and HA-loaded PLGA particles act as a viscous supplement and have a higher inflammatory inhibitory effect than HA solutions—indicating that the preparation may be a promising alternative to the current commercial dosage form of HA. Hyaluronic acid (HA), the main component of synovial fluid (SF) in the human joint cavity , mainly As another important component of glycosaminoglycan, glucosamine (GlcN) has been extensively used to reduce joint pain and slow the progression of osteoarthritis, while the stability of polysaccharide drugs remains a major problem in the treatment. in vitro cell tests, the chondrocytes treated with interleukin-1β (IL-1β) were co-cultured with the abovementioned GelMA solutions. The results show that biocomposite microspheres significantly upregulate the expression of cartilage anabolic genes and downregulate the expression of cartilage catabolic protease genes. The benefits of the functionalized microspheres in the rat OA model were also apparent , and eicosapentaenoic acid (EPA). Dexamethasone, an anti-inflammatory corticosteroid for intra-articular injection, is used in osteoarthritis treatment . García-apparent . The injScavenging oxygen-free radicals and inhibiting the production of ROS have become important strategies for regulating inflammation in the joint cavity. The active and precise release of the hydrogel material provides great convenience for the early inflammation treatment of OA. Drug delivery systems based on mechanically active biomaterials have a fine active response to mechanical signals, such as HA hydrogels with mechanically sensitive dexamethasone-loaded block copolymer micelles (BCMs) capable of continuously releasing dexamethasone in a controlled manner when the cartilage is compressed during daily activities, which can be used for pain management in patients with osteoarthritis .in vivo biocompatibility and biodegradability. Effectively breaking through the dense structure and high-density negative charge of the cartilage matrix is key to transporting the targeted drug to the lesion area. In summary, no cartilage defect is indicated in early arthritis, and the main therapeutic goals are to increase joint lubrication with hydrogels and thus control disease progression with their sustained release. When the cartilage defect is further developed, different repair methods must be used, depending on the depth of the defect.in situ cartilage repair, including their loading capacity, differentiation promoting capacity, adhesion, and mechanical strength.Progressive osteoarthritis is characterized by broken cartilage with partial thickness. Some of the thickness defects occur on the surface of the cartilage. The bone marrow mesenchymal stem cells (BMSCs) fail to migrate or are not recruited into the defective area . AccordiSimilar to other treatment strategies, the intra-articular injection challenge serves to improve cartilage regeneration. Cell-based therapy reverses OA . AutologNatural polymer materials have been used widely in human health engineering and regenerative tissue—for example, skin , cartilaCollagen is the most abundant protein in articular cartilage and constitutes the main component of its extracellular matrix. Collagen as a carrier of MSCs or chondrocytes has been widely studied for cartilage repair in recent years. Jiang et al. demonstrated that collagen-based hydrogels mediate the expression of Sox9, a transcription factor regulating cartilage formation, indicating that properly designed hydrogel scaffolds can act without growth factors and open novel pathways for cartilage tissue engineering . Howeverin vitro and in vivo, and provides a good three-dimensional microenvironment for cartilage repair.Gelatin, as a denatured collagen, not only has the biocompatibility and degradability of collagen but also has the adjustability of mechanical properties. Its gel state is easy to adjust and changes with the temperature . The cheSericin is a protein extracted from silk fiber that has high biocompatibility. One of the advantages of sericin is the nutritional supply characteristic, which allows chondrocyte proliferation even in the absence of nutrition. Sericin methacryloyl hydrogels produced by the functionalization of methacryloyl materials have similar molecular and mechanical properties to natural cartilage . YodmuanAs a hydrogel base material for proteins, the main problems in practical application are the difficulty of processing and the high price, which hinders its application.Polysaccharide materials have been widely studied by numerous researchers owing to their abundant reserves and stable chemical structure. Polysaccharide materials mainly include HA, chondroitin sulfate, cellulose, and alginic acid materials. The greatest advantage of polysaccharide materials is that they are highly biocompatible, have vast reserves and sources, and contain glycan components, which are similar to those of ECM. However, their shortcomings are likewise evident. Pure natural polysaccharide materials lack mechanical strength. Except for HA, they have no biological function, such as the ability to interact with proteins and cells . Therefoin vitro and in vivo  or by light crosslinking using ultraviolet light . 3O4 as a physical crosslinking agent based on the dual network structure of the flexible AAm binary polymer and rigid chitosan crosslinking. Taking full advantage of the synergistic effect of the DN strategy and weak non-covalent interaction  can effectively improve the strength, toughness, and self-recovery properties of composite hydrogels. With the help of the strategy, the originally weak hydrogels are more promising in the field of osteochondral repair. The improved mechanical properties expand the application of GelMA material, especially in the process of arthroscopic combined treatment, and sufficient mechanical strength can cooperate with arthroscopic operation, directly introducing the cartilage defect under the direction of arthroscopy. This improves the accuracy of injection, thereby improving the accuracy and speed of treatment. The light source may be shared with an arthroscopic instrument, and the transition from the injected liquid to the hydrogel may be achieved Poly  (PVA) hydrogel is a reliable and high-performance carrier . PVA hydNumerous research teams have chosen PEG as the base material to design hydrogels for cartilage repair, mainly owing to its good mechanical properties and ease of modification . In partIt is difficult for hydrogel materials to retain their adhesive ability in moist environments. The limited volume of the joint cavity and excessively large or complex molecular polymers make the material of excessive size and lose its application value in clinical treatment. With the further study of marine organisms, researchers have discovered the law of underwater adhesion of mussels and developed a series of high strength, high adhesion, and high biocompatibility hydrogel materials for the joint cavity environment. For example, To mimic the structural characteristics of articular cartilage with a natural cell gradient from tissue surface (high cell) to depth (low cell), researchers used magnetically controlled methods to construct hydrogels with different cell gradient densities . Magnetiin vitro, there is no risk of immunogenicity and disease transmission. The hydrogel base material may provide conditions for cell recruitment. In addition, the mechanical properties of the cartilage surface and osteochondral tissue are different. It may be better to repair osteochondral defects with multilayered scaffolds with graded tissue.The full-thickness defect implies that the cartilage defect is deep and penetrates the tidal line but does not penetrate the subchondral bone. An osteochondral defect indicates that the defect is deep enough to reach the bone marrow. Full-thickness defects and osteochondral defects can be partially repaired by bone marrow stromal cells (BMSCs) . The endIn vivo experiments of rabbit articular cartilage defects showed that the cell-free hydrogel system was beneficial to cartilage regeneration. Thus, the composite hydrogel system shows potential for application in cellular cartilage tissue engineering. In the study by in situ cartilage defects and the remodeling of the extracellular matrix. HUC-MSCs-sEVs derived from human umbilical cord mesenchymal stem cells promote the migration, proliferation, and differentiation of chondrocytes and hBMSCs. In vivo, GelMA/Gel-nano-sEVs hydrogels containing hUC-MSCs-sEVs could effectively promote cartilage regeneration. These results suggest that Gel-nano-sEVs have a good ability to stimulate cartilage formation and repair cartilage defects.For severe osteoarthritis with cartilage defects, stem cell therapies have obtained effective results in the laboratory for both exogenous and autologous stem cell recruitment . HoweverThe abovementioned limitations can be improved by using autologous stem cell recruitment, and we have reason to believe that tissue engineering technologies with strong stem cell recruitment capability are highly promising in cartilage repair.To date, the performance of scaffold materials for the treatment of OA is still being breaken through and improved constantly. The physiological environment of osteoarthritis makes the requirements of tissue engineering materials very complex and requires a variety of performance superpositions, even though some performance is contradictory. Materials require strong mechanical strength, controlled absorption efficiency, good biocompatibility, drug or cell loading capacity, and bone or chondrogenesis. Furthermore, in recent years, tissue adhesion, anti-fatigue performance, and self-healing performance have been found to exert great influence on the treatment effect of arthritis. Moreover, the required hydrogel stent performance varies for different osteoarthritis types. Hydrogel scaffolds with different substrates exhibit different performance characteristics. How to fuse different materials and obtain their common advantages is an urgent problem. Machine learning and artificial intelligence can help us simulate material hybridizations, reduce the number of our experiments, replace our trial and error, and potentially help us find the scaffold material that is ultimately suitable for osteoarthritis treatment.The development process and treatment of osteoarthritis are highly complicated, which brings significant trouble to doctors and patients in clinical diagnosis and treatment. The main reason is that a bone-cartilage unit consisting of subchondral bone and cartilage plays an important role in joint homeostasis and OA development. At the same time, two different joint units experience different abnormal changes in terms of their tissue structure and cell activity. However, the current research and development of biomaterials and related therapies are mostly aimed at a single structural unit, which may significantly limit the therapeutic effect. The cross-talk between chondrocytes and subchondral osteocytes and the interaction of the microenvironment in the pathological state have not been elucidated in the pathophysiology. Therefore future research should focus on the pathological processes of cartilage and subchondral bone, the relationship between cell interactions and OA progression, as well as the development of targeted biomaterials for simultaneous treatment of compound joint units to achieve better therapeutic outcomes.We review the application of hydrogel scaffolds in the treatment of osteoarthritis in the recent 3 years. They can be divided into natural, synthetic, and hybrid polymer scaffolds. They have their own advantages and disadvantages; hence, we put forward some suggestions for later research and development: 1) Because the joint cavity exists in a wet environment, although the current gelatin, collagen, and other protein hydrogel products show excellent adhesion, their adhesion performance in a wet environment still must be tested. The mimicking mechanism of the mussel may provide a new possibility for wet adhesion. 2) Osteoarthritis drugs and hydrogels form complex polymers. The use of currently known steroidal anti-inflammatory drugs and numerous other drugs, including traditional Chinese medicine, curcumin, and berberine, proved to be effective in the treatment of osteoarthritis. Further research is required on these Chinese patent drugs. 3) The treatment processes of osteoarthritis may be accompanied by open or conservative treatments, sometimes even combined with arthroscopy, photodynamic, and other auxiliary treatment methods. How to design suitable hydrogel materials for different treatment methods is the follow-up research direction. We classify osteoarthritis into early osteoarthritis, advanced osteoarthritis, and late osteoarthritis according to the degrees of clinical development and provide different hydrogel design strategies for different stages of disease development . For exaThere are two main approaches to treat OA with hydrogels. The first is to use the different properties of the hydrogel itself to treat different osteoarthritis. Using its lubricating properties and fluidity, the hydrogel could be injected directly into the joint cavity to improve the friction of joint movement and relieve the state of osteoarthritis. Otherwise, one may use its adhesion and mechanical strength to construct artificial cartilage, which may be a hard lubricating hydrogel. Another approach is to use hydrogel as carriers of drugs or cells, which promotes bone and cartilage regeneration by loading cells or drugs into the material before treatment or recruiting corresponding cells at the lesion site after implantation. This requires a personalized design of hydrogel scaffold materials. This article reviews the application of hydrogel scaffolds for different osteoarthritis lesions and shows the broad prospects for the treatment of OA. Through the cooperation of cytology, material science, and pathophysiology, the reversion of OA or even a cure could be attainable.


The thyroid nodule segmentation of ultrasound images is a critical step for the early diagnosis of thyroid cancers in clinics. Due to the weak edge of ultrasound images and the complexity of thyroid tissue structure, it is still challenging to accurately segment the delicate contour of thyroid nodules. A local and context-attention adaptive network (LCA-Net) for thyroid nodule segmentation is proposed to address these shortcomings, which leverages both local feature information from convolution neural networks and global context information from transformers. Firstly, since most existing thyroid nodule segmentation models are skilled at local detail features and lose some context information, we propose a transformers-based context-attention module to capture more global associative information for the network and perceive the edge information of the nodule contour. Secondly, a backbone module with  The thyroid gland is honored as the ‘life gland’ of human beings, which is an essential endocrine organ in the body . The thyBefore the rise of deep learning, the thyroid nodule segmentation of ultrasound images relied on conventional image processing technologies, which could be roughly divided into three categories : contourWith the remarkable development of deep learning, it has been applied to medical image diagnosis and provides auxiliary diagnosis advice in clinics . In the erations ,15 and lerations , it is sIn this paper, a local and context-attention adaptive network (LCA-Net) is proposed to further overcome the shortcomings of the current thyroid nodule segmentation methods. This model could more accurately and automatically depict the contour of nodules and even better assist radiologists in the subsequent diagnosis of clinics. Considering the characteristic of convolutional neural networks focusing on local details and the merits of transformers in capturing global context information, this paper mainly employs CNNs and transformers to design the whole novel end-to-end network structure. Specifically, the backbone modules based on CNNs and the context-attention modules based on transformers designed in this paper are utilized to extract local feature details and global associative information. Simultaneously, a nodules adaptive convolutions (NAC) module is embedded into the encoder of the network, which assists the network to have promising adaptability for various sizes and positions of nodules. Furthermore, an optimized loss function is proposed to solve the pixels class imbalance problem in segmentation. Experiments are performed on the public TN-SCUI2020 dataset and TN3K dataset to evaluate our method, and the results are superior to the current thyroid nodule segmentation models.A novel local and context-attention adaptive network (LCA-Net) is proposed for thyroid nodule segmentation in ultrasound images. Compared with the existing state-of-the-art methods of thyroid nodule segmentation, the LCA-Net shows promising segmentation accuracy and strong generalization performance.A context-attention module based on transformers is proposed to gain global associative information to the model.A backbone module with The nodule adaptive convolutions (NAC) module is embedded into the encoder of the LCA-Net to enhance the robustness of the network to various sizes and positions of nodules.A novel loss function is proposed to solve the pixels’ class imbalance problem for thyroid nodule segmentation.The main contributions of our algorithm are five-fold:The rest of the paper is organized as follows. In This paper aimed to establish a high-accuracy and strong generalization thyroid nodule segmentation model to assist radiologists in clinical diagnosis. In this section, we propose a novel thyroid nodule segmentation scheme. Firstly, we introduce the proposed LCA-Net architecture which includes backbone modules, context-attention modules and a nodule adaptive convolution module. Afterward, we present the details of the context-attention module, the backbone module, the nodule adaptive convolution module and a novel loss function, respectively.Currently, existing thyroid nodule segmentation models in the literature are often not comprehensive enough in design, which ignores the global information capture ability or the generalization performance of the practical clinical application. Considering the weak edge and low contrast of thyroid nodules in ultrasound images, it is easy to misdiagnose using convolution neural networks to extract the local features of thyroid nodules. Hence, we designed a novel thyroid nodule segmentation model called LCA-Net from the perspective of taking global context, local details and generalization performance into account. interest , and conputation ,17. The s is the step size of moving the key-value patches. Notably, the query is different from the key-value patch sizes, and the key-value patch is overlapped, which is larger than the query patch. Extensive experiments show that keeping larger and overlapping key-value patches leads to significant performance gains due to the small information exchange between two neighborhood windows. This design aims to save the amount of computation and maximize the transformer’s ability to capture contextual information as much as possible. Additionally, the function permute is used to transform the dimensions of Q, K and V to facilitate subsequent matrix operations. At this point, the dimensions of on index ,18. By rThe proposed self-attention takes into account the information and position of all pixels in the feature map, so it can extract more global context features. We define the input feature map of the context-attention module as We denote Furthermore, the feature map passes through the rest of the module, and the calculation process can be defined as:As shown in In general, because the smaller convolution kernel has merits such as better nonlinear expression and fewer parameters, researchers use it to build deep neural networks. Nevertheless, the smaller convolution kernel will also lead to the incomplete extraction of local information, resulting in the loss of thyroid nodule contour information. Simultaneously, the application of pooling layers further leads to the reduction in the image resolution and the loss of spatial feature information ,19. To atraining . At thisAs is known to all, the activation function is nonlinear, which improves the expression ability of the neural network to the model. Due to its fast convergence speed, no gradient saturation and gradient disappearance, the ReLU function has become the mainstream neural network activation function. Meanwhile, this function also has shortcomings such as neuron necrosis and not being smooth enough. Generally speaking, the smoother activation function can better transfer information and improve the accuracy and generalization ability of the model. Considering the fact that the loss of information should be avoided as much as possible when extracting the detailed features of nodules, an advanced activation function Mish  is introAs seen in The remaining two In clinical practice, the size and location of thyroid nodules are various. Hence, it is difficult to merely match the variable nodules with the convolution kernel of fixed size . The larLet us denote the input and output of the module as c is the pixel class. Hence, the prediction probability of misclassified pixels is c denotes the pixel class and In actual ultrasound images, the thyroid nodule occupies only a small part of the image, while the rest are mostly background. This can easily cause pixel class imbalance, which affects the segmentation performance of the model. Therefore, if the conventional dice loss or cross-entropy loss is directly used to optimize the network parameters, the prediction segmentation maps in the test stage are not ideal. Motivated by focal loss structure , an optiIn this paper, in order to realize the function of the LCA-Net, we needed to utilize the suitable thyroid nodule datasets of ultrasound images to train and test the model. The TN-SCUI2020  and TN3kThe TN-SCUI2020 dataset comes from the challenge of Thyroid Nodule Segmentation and Classification in Ultrasound Images, which provides a public 2D dataset of a thyroid nodules with approximately 7288 ultrasound images. These images were collected from different ages, genders and in different sites using various ultrasound machines such as Mindray DC-8, Philips-cx50 and TOSHIBA Aplio300 . Each imIn order to facilitate the development of thyroid nodule segmentation, Gong et al.  contribuDifferent metrics are employed to test the results of the proposed model. The performance of the thyroid nodule segmentation will be evaluated by dice, Jaccard, recall and precision, which are defined as:ediction . The PA ediction . FurtherThe hyperparameters of each experiment in this work are generally consistent. During training, these experiments appropriately use data enhancement and apply data enhancement with a probability of AdaBound  optimizeIn this section, we compare our proposed LCA-Net with other state-of-the-art thyroid nodule segmentation networks, such as SGU-Net , TRFE-NeFirstly, we calculate the nodule segmentation evaluation metrics defined above based on the TN-SCUI2020 dataset, and the comparison results for all methods are listed in In addition, to further prove the generalization performance and effectiveness of the models, we conducted a new set of segmentation experiments on the TN3k dataset. The calculated evaluation metrics and experimental conditions are precisely the same as above. The specific experiment results are illustrated in Finally, we mixed the two datasets to further test the generalization ability and clinical application value of the model. We utilized the TN-SCUI2020 dataset as the training set and the TN3k dataset as the test set for experiments and vice versa. The specific experimental results are shown in In this section, in order to thoroughly evaluate the proposed LCA-Net framework and verify the performance of the selected loss function and optimization method, a variety of ablation studies were carried out on the TN-SCUI2020 dataset. Considering the fact that the TN-SCUI2020 dataset has wider data sources and more clinical representation than the TN3k dataset, we only use TN-SCUI2020 as the dataset for ablation experiments.Since our proposed LCA-Net integrates the merits of context-attention modules, backbone modules and a nodule adaptive convolutions module concurrently to design the model, it is essential to investigate the impact of each kind of module on the performance of LCA-Net. We conduct this ablation experiment and calculate the metrics with different modules selections. The results are summarized in Several examples of segmentation results with modules variations are shown in Furthermore, we demonstrate the effect of different loss functions on the segmentation performance of the model. In most ultrasound images, the background size is more significant than that of the nodule, which can easily lead to the problem of class imbalance during segmentation and make it difficult to continue training. The classical dice loss  is oftenThis paper compares the segmentation results of binary cross-entropy loss, dice loss and our proposed loss function under the same network structure and AdaBound optimizer. It can be seen from Finally, we also conducted a set of ablation experiments for optimizer selection. The optimizer method is employed to assist in training to obtain the best network parameters and find the minimum loss of the loss function. Extensive experiments have shown that the SGD  optimizeThe optimization of our proposed loss function by the three optimizers is shown in In this paper, a local and context-attention adaptive network (LCA-Net) for thyroid nodule segmentation in ultrasound images is proposed. The transformers-based context-attention module is proposed to capture more global associative information and perceive edge information of nodule contours to make up for the deficiency of convolutional neural networks in capturing context information. Additionally, the backbone module enables the network to extract more local features. Furthermore, a nodule adaptive convolutions (NAC) module is designed to deal with thyroid nodules of different sizes and positions adaptively. For the training of the model, an optimized loss function for pixel class imbalance is introduced to further enhance the segmentation performance. Compared with the latest algorithms, our method achieves promising segmentation accuracy and strong generalization performance. We believe that the proposed model could also serve other medical image segmentation tasks and has broad application prospects for diagnosing thyroid nodules.


Drug-induced interstitial lung disease (DILD) is the most common pulmonary adverse event of anticancer drugs. In recent years, the incidence of anticancer DILD has gradually increased with the rapid development of novel anticancer agents. Due to the diverse clinical manifestations and the lack of specific diagnostic criteria, DILD is difficult to diagnose and may even become fatal if not treated properly. Herein, a multidisciplinary group of experts from oncology, respiratory, imaging, pharmacology, pathology, and radiology departments in China has reached the “expert consensus on the diagnosis and treatment of anticancer DILD” after several rounds of a comprehensive investigation. This consensus aims to improve the awareness of clinicians and provide recommendations for the early screening, diagnosis, and treatment of anticancer DILD. This consensus also emphasizes the importance of multidisciplinary collaboration while managing DILD.


Electrochemistry is now a powerful tool in organic chemistry not only for analyzing the electron transfer behavior of organic molecules and macromolecules, but also for driving organic reactions to produce value-added products. Electrochemical synthesis (or simply electrosynthesis) is increasingly recognized for the high academic and industrial importance, in line with the concept of green chemistry proposed in 1998 and the Sustainable Development Goals (SDGs) adopted by the United Nations in 2015. This is evidenced by the recent publication of special issues on organic electrosynthesis in various academic journals [In addition to the conventional two- or three-electrode batch-type electrolytic cells, recent developments include microflow electrolytic reactors, polymer electrolyte membrane electrolysis technology, and new methods coupled with photoredox catalysts or transition metal catalysis, resulting in remarkable progress in organic electrosynthetic processes. Theoretical calculations have also led to a better understanding of the electron transfer behavior of organic molecules and the estimation of subsequent reactions, resulting in a much better understanding of the reaction mechanism. Furthermore, because organic electrosynthesis requires the setting of many complex parameters, such as applied potential, current density, electrolyte, temperature, and so on, it has a high affinity to informatics approaches, e.g., machine learning, which is expected to become an increasingly important tool in the future.Progress in the design of organic molecules and polymers and the understanding of the redox behavior of these compounds has led to the development of organic electrochemistry for energy material applications. Organic semiconductor design for electron or hole transport is important for transistor and solar cell applications, and redox-active (but stable) organic and polymeric materials are promising for secondary batteries and redox flow batteries. By understanding organic electron transfer reactions, we can face the challenge of how to design materials with better cycle properties by suppressing undesired side reactions.To showcase this area of research, the present thematic issue focuses on the recent advances in molecular and macromolecular electrochemistry. The scope of this interdisciplinary issue ranges from synthetic aspects (such as electrosynthesis and reaction mechanisms) to materials science (including redox properties and devices).Shinsuke Inagi and Mahito AtobeYokohama, October 2022


The increasing use of computed tomography (CT) and cone beam computed tomography (CBCT) in oral and maxillofacial imaging has driven the development of deep learning and radiomics applications to assist clinicians in early diagnosis, accurate prognosis prediction, and efficient treatment planning of maxillofacial diseases. This narrative review aimed to provide an up-to-date overview of the current applications of deep learning and radiomics on CT and CBCT for the diagnosis and management of maxillofacial diseases. Based on current evidence, a wide range of deep learning models on CT/CBCT images have been developed for automatic diagnosis, segmentation, and classification of jaw cysts and tumors, cervical lymph node metastasis, salivary gland diseases, temporomandibular (TMJ) disorders, maxillary sinus pathologies, mandibular fractures, and dentomaxillofacial deformities, while CT-/CBCT-derived radiomics applications mainly focused on occult lymph node metastasis in patients with oral cancer, malignant salivary gland tumors, and TMJ osteoarthritis. Most of these models showed high performance, and some of them even outperformed human experts. The models with performance on par with human experts have the potential to serve as clinically practicable tools to achieve the earliest possible diagnosis and treatment, leading to a more precise and personalized approach for the management of maxillofacial diseases. Challenges and issues, including the lack of the generalizability and explainability of deep learning models and the uncertainty in the reproducibility and stability of radiomic features, should be overcome to gain the trust of patients, providers, and healthcare organizers for daily clinical use of these models. Technological advances are changing many aspects of our society and industries, including healthcare. Innovative digital technologies such as computer-aided design/manufacturing, rapid prototyping, augmented/virtual reality, and “omics” analysis have been increasingly used in several medical and dental disciplines for diagnostic and therapeutic purposes . ArtificMachine learning is a subfield of AI that enables algorithms to learn the intrinsic statistical patterns in a set of data without being explicitly programmed and then to make predictions on unseen data . Due to Radiomics is an emerging translational field in quantitative imaging, related to machine learning. It is capable of quantifying the textural information of specific regions of interest in digital diagnostic images through mathematic extraction of signal intensity distribution and pixel/voxel interrelationships that cannot be perceived by the human eye . Based oRadiographic examination is an integral component in the diagnosis and management of most dento-maxillofacial diseases, and so a great amount of digital radiographic images are readily available in the archiving systems and associated databases of many hospitals and clinics. Hence, the majority of AI models in dentistry have been developed based on radiographic images to assist dentists in the diagnosis , treatment planning , and prediction  of various dental and maxillofacial diseases . ComputeMaxillofacial diseases include both odontogenic and nonodontogenic diseases in the jaws and related structures including salivary glands, temporomandibular joints (TMJs), and facial muscles. Due to their anatomical complexity and proximity to critical vascular and neural structures, 3D imaging (such as CT/CBCT) is often required in the diagnostic and treatment planning processes, serving as one of the essential components of digital workflows for patient management. Thus, a wide range of deep learning and radiomics applications have been developed based on CT/CBCT images for diagnosis, treatment planning, and prediction of various maxillofacial diseases .Incidental findings of lesions in the jaws are often observed on routine dental radiographic examination. These lesions are usually cystic in nature and thus challenging for general practitioners to differentiate between cysts and tumors on radiographic images. Motivated by the need for more timely and accurate diagnosis of maxillofacial cysts and tumors, several studies have developed AI, especially deep learning, models on 2D panoramic radiographs for automatic diagnosis of various cysts and tumors of the jaws ,13,14,15Differentiating various types of jaw cysts and tumors on CBCT using deep learning has been proposed by several groups ,17,18. LOral squamous cell carcinoma (OSCC) is the sixth most frequent malignancy globally and associated with a high rate of invasiveness and lymph node metastasis . CervicaAriji et al. first developed a CNN model to differentiate histopathologically proven metastatic cervical lymph nodes from the negative ones on cropped contrast-enhanced CT images of OSCC patients . The modThe potential of radiomics in the screening, diagnosis, and prediction of oral, head and neck cancer has been increasingly exploited . Few stuSalivary gland diseases are a group of inflammatory, infectious, and neoplastic conditions, mainly occurring in the parotid glands, followed by minor salivary glands, submandibular glands, and sublingual glands. Diagnosis of salivary gland diseases is a great challenge in dentistry and oto-rhino-laryngology, as it heavily relies on the practitioners’ experiences and diagnostic imaging. Deep learning models for the diagnosis of salivary gland diseases have been developed largely on magnetic resonance imaging (MRI) scans  because The intrinsic radiomic features of malignant parotid gland tumors on CT images may be extracted to assist in differentiating between benign and malignant salivary gland tumors . Zhang eTMJ disorders are one of the most common orofacial dysfunctions that frequently cause joint clicking sounds, limited mouth opening, pain, and headache . DegenerThe maxillary sinus is the largest paranasal sinus and is frequently involved in various dento-maxillofacial surgical procedures, such as apical surgeries of the maxillary posterior teeth and sinus augmentation for dental implant placement ,43. AccuThe mandible is the only moveable and the most commonly fractured bone of the face in trauma cases. A CNN model was developed to automatically detect mandibular fractures on CT images . The modDentofacial deformities and malocclusion are characterized by abnormalities of the dentition, jaws, and face that affect not only the oral function and appearance of patients but also their physical, social, and psychological well-being. Orthodontic and orthognathic treatment is commonly performed to correct these abnormalities. Conventional treatment planning of orthognathic surgery consists of a series of steps based on clinical examination, cephalometric analysis, dental casts, facebow, and articulators. Compared with the conventional approach, a digital workflow improves the accuracy and efficiency of orthognathic surgery without the need of a facebow record and model surgery .® . Automated multimodal image registration to merge CBCT and optical intra-oral/model scans using deep learning has been proposed by some groups [The digital workflows in dentistry frequently require a 3D virtual augmented patient model that is created based on multimodal image data  to serve as the foundation for subsequent treatment planning and guided surgery for many surgical procedures including orthognathic surgery. Segmentation of anatomical structures and multimodal image registration are the essential steps in the process of building a virtual patient model. They can be performed manually through visual inspection or semi-automatically by using the fiducial marker registration method or surface-based matching algorithm . The mane groups ,65. The e groups . The modIn addition, deep learning was applied to determine whether an individual needs orthognathic surgery and to predict the surgical outcomes. Kim et al. developed several CNN models on CBCT images to automatically classify individuals into Class I, II, and III skeletal malocclusion according to several parameters including the anteroposterior relationship of the maxillary and mandibular first molars and the alignment of teeth with reference to the line of occlusion . The perBased on current evidence, early diagnosis, accurate prognostic prediction, and efficient treatment planning are main focuses of deep learning and radiomics models developed on CT/CBCT for maxillofacial diseases . Few stuDespite the promising performance of deep learning models proposed, their generalizability has not been validated sufficiently. Most of them were trained using CT/CBCT images acquired at a certain time point from a single institution and were tested with the cross-validation or split sample validation method using images from the same institution, which is very likely to cause overfitting of the trained model. Some studies have reported that their models had inferior performance when tested on images from other institutions ,71. MoreDeep learning algorithms allow for automatic extraction and selection of imaging features on radiographic images in the neural network. As deep learning models automatically extract hierarchical features in complex data and optimize the weighted parameters from raw data, their decision-making process cannot be deduced, and thus, they are considered as “black-box” models . CompareIt remains unknown whether CT- and CBCT-derived radiomic features are interchangeable. Few studies have assessed the differences in radiomic feature values of head and neck cancer between CT and CBCT images of the same individuals ,80. It whttps://ibsi.readthedocs.io/ (accessed on 5 December 2022)) is an independent international collaboration where experts in various areas of medical imaging from several institutions in eight countries work together to standardize the extraction of image biomarkers  from diagnostic imaging for the purpose of achieving greater harmonization of radiomics research [The reproducibility in the radiomic feature values calculated by different software packages  remains uncertain. Some found that the values of features in certain categories  were not consistent across packages  while otresearch . StandarAt present, most of the deep learning and radiomics models for maxillofacial diseases were developed based solely on CT/CBCT image data. Enriching these models with diverse data from the individual level , setting level , and system level  may facilitate a deeper and more holistic understanding of individual health and disease and may therefore enable a more precise and personalized management of patients with maxillofacial diseases . Most imA wide range of deep learning and radiomic models on CT/CBCT have been proposed for automatic diagnosis, segmentation, and classification of jaw cysts and tumors, cervical lymph node metastasis, salivary gland diseases, TMJ disorders, maxillary sinus pathologies, mandibular fractures, and dentomaxillofacial deformities. The models with performance on par with specialists have the potential to serve as clinically practicable tools to achieve the earliest possible diagnosis and treatment, leading to a more precise and personalized approach for the management of maxillofacial diseases.