Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis.

BACKGROUND & AIMS: Our purpose was to detect pancreatic ductal adenocarcinoma (PDAC) at the prediagnostic stage (3-36 months before clinical diagnosis) using radiomics-based machine-learning (ML) models, and to compare performance against radiologists in a case-control study. METHODS: Volumetric pancreas segmentation was performed on prediagnostic computed tomography scans (CTs) (median interval between CT and PDAC diagnosis: 398 days) of 155 patients and an age-matched cohort of 265 subjects with normal pancreas. A total of 88 first-order and gray-level radiomic features were extracted and 34 features were selected through the least absolute shrinkage and selection operator-based feature selection method. The dataset was randomly divided into training (292 CTs: 110 prediagnostic and 182 controls) and test subsets (128 CTs: 45 prediagnostic and 83 controls). Four ML classifiers, k-nearest neighbor (KNN), support vector machine (SVM), random forest (RM), and extreme gradient boosting (XGBoost), were evaluated. Specificity of model with highest accuracy was further validated on an independent internal dataset (n = 176) and the public National Institutes of Health dataset (n = 80). Two radiologists (R4 and R5) independently evaluated the pancreas on a 5-point diagnostic scale. RESULTS: Median (range) time between prediagnostic CTs of the test subset and PDAC diagnosis was 386 (97-1092) days. SVM had the highest sensitivity (mean; 95% confidence interval) (95.5; 85.5-100.0), specificity (90.3; 84.3-91.5), F1-score (89.5; 82.3-91.7), area under the curve (AUC) (0.98; 0.94-0.98), and accuracy (92.2%; 86.7-93.7) for classification of CTs into prediagnostic versus normal. All 3 other ML models, KNN, RF, and XGBoost, had comparable AUCs (0.95, 0.95, and 0.96, respectively). The high specificity of SVM was generalizable to both the independent internal (92.6%) and the National Institutes of Health dataset (96.2%). In contrast, interreader radiologist agreement was only fair (Cohen's kappa 0.3) and their mean AUC (0.66; 0.46-0.86) was lower than each of the 4 ML models (AUCs: 0.95-0.98) (P < .001). Radiologists also recorded false positive indirect findings of PDAC in control subjects (n = 83) (7% R4, 18% R5). CONCLUSIONS: Radiomics-based ML models can detect PDAC from normal pancreas when it is beyond human interrogation capability at a substantial lead time before clinical diagnosis. Prospective validation and integration of such models with complementary fluid-based biomarkers has the potential for PDAC detection at a stage when surgical cure is a possibility.

Automated Segmentation of Kidney Cortex and Medulla in CT Images: A Multisite Evaluation Study.

BACKGROUND: In kidney transplantation, a contrast CT scan is obtained in the donor candidate to detect subclinical pathology in the kidney. Recent work from the Aging Kidney Anatomy study has characterized kidney, cortex, and medulla volumes using a manual image-processing tool. However, this technique is time consuming and impractical for clinical care, and thus, these measurements are not obtained during donor evaluations. This study proposes a fully automated segmentation approach for measuring kidney, cortex, and medulla volumes. METHODS: A total of 1930 contrast-enhanced CT exams with reference standard manual segmentations from one institution were used to develop the algorithm. A convolutional neural network model was trained (n=1238) and validated (n=306), and then evaluated in a hold-out test set of reference standard segmentations (n=386). After the initial evaluation, the algorithm was further tested on datasets originating from two external sites (n=1226). RESULTS: The automated model was found to perform on par with manual segmentation, with errors similar to interobserver variability with manual segmentation. Compared with the reference standard, the automated approach achieved a Dice similarity metric of 0.94 (right cortex), 0.90 (right medulla), 0.94 (left cortex), and 0.90 (left medulla) in the test set. Similar performance was observed when the algorithm was applied on the two external datasets. CONCLUSIONS: A fully automated approach for measuring cortex and medullary volumes in CT images of the kidneys has been established. This method may prove useful for a wide range of clinical applications.

A systematic review on the use of artificial intelligence in gynecologic imaging - Background, state of the art, and future directions.

OBJECTIVE: Machine learning, deep learning, and artificial intelligence (AI) are terms that have made their way into nearly all areas of medicine. In the case of medical imaging, these methods have become the state of the art in nearly all areas from image reconstruction to image processing and automated analysis. In contrast to other areas, such as brain and breast imaging, the impacts of AI have not been as strongly felt in gynecologic imaging. In this review article, we: (i) provide a background of clinically relevant AI concepts, (ii) describe methods and approaches in computer vision, and (iii) highlight prior work related to image classification tasks utilizing AI approaches in gynecologic imaging. DATA SOURCES: A comprehensive search of several databases from each database's inception to March 18th, 2021, English language, was conducted. The databases included Ovid MEDLINE(R) and Epub Ahead of Print, In-Process & Other Non-Indexed Citations, and Daily, Ovid EMBASE, Ovid Cochrane Central Register of Controlled Trials, and Ovid Cochrane Database of Systematic Reviews and ClinicalTrials.gov. METHODS OF STUDY SELECTION: We performed an extensive literature review with 61 articles curated by three reviewers and subsequent sorting by specialists using specific inclusion and exclusion criteria. TABULATION, INTEGRATION, AND RESULTS: We summarize the literature grouped by each of the three most common gynecologic malignancies: endometrial, cervical, and ovarian. For each, a brief introduction encapsulating the AI methods, imaging modalities, and clinical parameters in the selected articles is presented. We conclude with a discussion of current developments, trends and limitations, and suggest directions for future study. CONCLUSION: This review article should prove useful for collaborative teams performing research studies targeted at the incorporation of radiological imaging and AI methods into gynecological clinical practice.

Atrial Fibrillation in Indigenous Australians: A Multisite Screening Study Using a Single-Lead ECG Device in Aboriginal Primary Health Settings.

BACKGROUND: Circulatory diseases continue to be the greatest cause of mortality for Australian Aboriginal and Torres Strait Islander people, and a major cause of persistently lower life expectancy compared with non-Aboriginal Australians. The limited information that exists on atrial fibrillation (AF) prevalence in Aboriginal and Torres Strait Islander communities is mostly based on hospital admission data. This shows AF as principal or additional admission diagnosis was 1.4 times higher compared to non-Aboriginal Australians, a higher incidence of AF across the adult life span after age 20 years and a significantly higher prevalence among younger patients. Our study estimates the first national community prevalence and age distribution of AF (including paroxysmal) in Australian Aboriginal people. A handheld single-lead electrocardiograph (ECG) device (iECG), known to be acceptable in this population, was used to record participant ECGs. METHODS: This co-designed, descriptive cross-sectional study was conducted in partnership with 16 Aboriginal Community Controlled Health organisations at their facilities and/or with their services delivered elsewhere. The study was also conducted at one state community event. Three (3) Australian jurisdictions were involved: New South Wales, Western Australia and the Northern Territory. Study sites were located in remote, regional and urban areas. Opportunistic recruitment occurred between June 2016 and December 2017. People <45 years of age were excluded. RESULTS: Thirty (30) of 619 Aboriginal people received a 'Possible AF' and 81 an 'Unclassified' result from a hand-held smartphone ECG device. A final diagnosis of AF was made in 29 participants (4.7%; 95%CI 3.0-6.4%), 25 with known AF (five paroxysmal), and four with previously unknown AF. Three (3) of the four with unknown AF were aged between 55-64 years, consistent with a younger age of AF onset in Aboriginal people. Estimated AF prevalence increased with age and was higher in those aged >55 years than the general population (7.2% compared with 5.4%). Slightly more men than women were diagnosed with AF. CONCLUSIONS: This study is a significant contribution to the evidence which supports screening for AF in Aboriginal and Torres Strait Islander people commencing at a younger age than as recommended in the Australian guidelines (>65 years). We recommend the age of 55 years. Consideration should be given to the inclusion of AF screening in the Australian Government Department of Health annual 'Aboriginal and Torres Strait Islander Health Assessment'. CLINICAL TRIAL REGISTRATION: ACTRN12616000459426.

Culturally tailored tobacco control: Aboriginal community perspectives in Sydney, Australia.

INTRODUCTION: A community-based project was developed to support quit attempts and denormalise smoking in Aboriginal communities. This qualitative study evaluates the effectiveness of project strategies, messages and the partnership process, and explores impacts observed by Aboriginal community members, complementing findings from a quantitative evaluation. METHODS: The study comprised five focus groups (40 participants) and 14 in-depth interviews (with 15 community members). Data were analysed thematically by manual and NVivo software methods. RESULTS: Results demonstrate that the project attracted community attention, was well recalled and messages were considered convincing and persuasive. Participants reported being more likely to quit and to discuss smoking with family and friends, and noticed that many people were quitting. Participants observed an increase in asking people not to smoke in the home and fewer people smoking at events, but noted that many smokers struggled to stay quit. The partnership and participation of Aboriginal Health Workers were viewed as crucial. CONCLUSION: The qualitative findings reinforce quantitative evaluation findings suggesting that the project contributed to denormalising smoking and motivating quit attempts. SO WHAT: The evaluation provided insight into how the project changed attitudes and motivated community members to make quit attempts and provided ideas to meet the ongoing challenge.

Oral health status, behaviours, food and beverage consumption of aboriginal children in Australia.

ISSUE ADDRESSED: Dental decay is prevalent among Australian Aboriginal children, yet little is known about their oral health-related behaviours. This study explored the oral health status, behaviours, food and beverage consumption of Aboriginal school children aged 7-9 years in Sydney, Australia. METHODS: Parents who were part of an existing longitudinal birth cohort ("Gudaga") were surveyed when their child was between 7 and 9 years. Children (n = 110) also received oral health screening by a trained nurse. RESULTS: A number of children (62%-91%) had at least one visible oral health problem across the 2 years. Around two thirds (62%-67%) of parents rated their child's oral health as excellent/very good and less than half the children (32%-45%) had received dental check-ups. Most children (79%-90%) brushed their teeth and drank water (97%) but more than half (57%-70%) also drank sugar sweetened beverages daily. CONCLUSIONS: Parents are instilling good oral health behaviours, however, the oral health screening suggests children are experiencing oral health issues of which parents may be unaware. Parents also seem to be unaware of beverage consumption practices that can increase the risk of childhood decay. SO WHAT?: The findings highlight the need for greater oral health awareness among Aboriginal families on how to recognise early symptoms dental decay and risk factors like sugar sweetened beverages among school going children. This suggests that existing health promotion strategies may not be reaching many Aboriginal families in the urban areas and more culturally appropriate programs may be needed.

Staying smoke-free: Factors associated with nonsmoking among urban Aboriginal adolescents in the Study of Environment on Aboriginal Resilience and Child Health (SEARCH).

ISSUE ADDRESSED: To examine the factors associated with preventing regular smoking among Aboriginal adolescents. METHODS: Cross-sectional analysis of data from 106 Aboriginal adolescents aged 12-17 years, and their caregivers, from four Aboriginal Community Controlled Health Services in urban New South Wales, 2008-2012. The relation of individual, social, environmental and cultural factors to having 'never' smoked tobacco regularly was examined using Poisson regression. RESULTS: Overall, 83% of adolescents had never smoked regularly; 13 reported current and five past smoking. Most lived in smoke-free homes (60%) despite 75% reporting at least one current smoker caregiver. Participants were significantly more likely to have never smoked regularly if they had good mental health (PR = 1.4, 95% CI: 1.1-1.9), their mother as their primary caregiver (1.3, 1.0-1.6), good family relationships (1.2, 1.0-1.5), stable housing (1.3, 1.1-1.7), had never used alcohol (1.8, 1.3-2.4), were not sexually active (3.1, 1.3- 7.2) and had no criminal justice interactions (1.8, 1.2-2.8). CONCLUSIONS: Most participants lived in smoke-free homes and the vast majority had never smoked regularly. Promoting good mental health and strengthening social connections may be protective against smoking as those experiencing less social disruption were more likely to have never smoked regularly. Smoking may be an indicator of psychosocial conditions and a prompt for screening and simultaneous treatment. SO WHAT?: Organisations should be resourced to deliver holistic adolescent health promotion programs. Programs and policies should support positive family relationships and stable housing as this may protect against the uptake of regular smoking.

Evaluating an Aboriginal tobacco social marketing project in Sydney, Australia.

INTRODUCTION: A partnership between three Aboriginal Community Controlled Organisations and a mainstream health service was formed to develop, implement and evaluate a comprehensive and culturally appropriate social marketing project which aimed to encourage smokers to quit smoking. The project also supported quit attempts and promoted denormalisation of smoking. METHODS: The project was evaluated through baseline (n = 427) and follow-up (n = 611) surveys carried out face-to-face with Aboriginal and/or Torres Strait Islander participants 18 years and older recruited through convenience sampling at community events and venues during 2010-2011 and 2015. RESULTS: The proportion of participants who had made one or more quit attempts increased significantly between baseline and follow-up surveys (54%, 101 out of 187; vs 64%, 189 out of 297; P < 0.05). Participants who had intended to quit within 6 months (AOR, 3.29; 95% CI 1.90-5.68; P < 0.01); and participants disagreeing with the statement "I don't mind if people smoke inside my home" (AOR, 1.74; 95% CI 1.06-2.84; P < 0.05) were significantly more likely to have made one or more quit attempts compared to the respective reference groups. CONCLUSION: Study findings demonstrate that the project was associated with increased quit attempts. Intention to quit and attitude were found to be the predictors of making a quit attempt. SO WHAT?: Many studies suggest the need to denormalise smoking; this study demonstrated both change in attitudes and an increase in quit attempts. It is recognised that many quit attempts may be needed for long-term smoking cessation.

The oral health behaviours and fluid consumption practices of young urban Aboriginal preschool children in south-western Sydney, New South Wales, Australia.

ISSUE ADDRESSED: Australian Aboriginal children have a higher risk of dental caries yet there is limited focus on oral health risk factors for urban Aboriginal preschool children. This study examined the oral health behaviours and fluid consumption practices of young children from an urban Aboriginal community in south-western Sydney, Australia. METHODS: In total, 157 Aboriginal children who were recruited to the "Gudaga" longitudinal birth cohort participated in this study. A survey design was employed and parents responded to the oral health questions when their child was between 18 and 60 months. RESULTS: Few parents (20%) were concerned about their child's oral health across the time period. By 60 months, only 20% of children had seen a dentist while 80% were brushing their teeth at least once daily. High levels of bottle use were seen up to 30 months. Consumption of sugary drinks was also very high in the early years, although this was replaced by water by 36 months. CONCLUSIONS: While there are some encouraging findings, such as the rates of tooth brushing and increasing rates of water consumption, the findings do highlight the poor uptake of dental services and high levels of bottle usage among urban aboriginal children during their early years. SO WHAT?: Targeted oral health promotional programs are needed in the urban Aboriginal community to better support parents understanding of good oral health practices in the early years and engagement with dental health services.

Housing conditions of urban households with Aboriginal children in NSW Australia: tenure type matters.

BACKGROUND: Housing is a key determinant of the poor health of Aboriginal Australians. Most Aboriginal people live in cities and large towns, yet research into housing conditions has largely focused on those living in remote areas. This paper measures the prevalence of housing problems amongst participants in a study of urban Aboriginal families in New South Wales, Australia, and examines the relationship between tenure type and exposure to housing problems. METHODS: Cross-sectional survey data was provided by 600 caregivers of 1406 Aboriginal children aged 0-17 years participating in Phase One of the Study of Environment on Aboriginal Resilience and Child Health (SEARCH). Regression modelling of the associations between tenure type (own/mortgage, private rental or social housing) and housing problems was conducted, adjusting for sociodemographic factors. RESULTS: The majority (60%) of SEARCH households lived in social housing, 21% rented privately and 19% either owned their home outright or were paying a mortgage ("owned"). Housing problems were common, particularly structural problems, damp and mildew, vermin, crowding and unaffordability. Physical dwelling problems were most prevalent for those living in social housing, who were more likely to report three or more physical dwelling problems than those in owned (PR 3.19, 95%CI 1.97, 5.73) or privately rented homes (PR 1.49, 1.11, 2.08). However, those in social housing were the least likely to report affordability problems. Those in private rental moved home most frequently; children in private rental were more than three times as likely to have lived in four or more homes since birth than those in owned homes (PR 3.19, 95%CI 1.97, 5.73). Those in social housing were almost half as likely as those in private rental to have lived in four or more homes since birth (PR 0.56, 95%CI 0.14, 0.77). Crowding did not vary significantly by tenure type. CONCLUSIONS: The high prevalence of housing problems amongst study participants suggests that urban Aboriginal housing requires further attention as part of efforts to reduce the social and health disadvantage experienced by Aboriginal Australians. Particular attention should be directed to the needs of those renting in the private and social housing sectors, who are experiencing the poorest dwelling conditions.

Smoking among Aboriginal adults in Sydney, Australia.

Issue addressed Tobacco consumption contributes to health disparities among Aboriginal Australians who experience a greater burden of smoking-related death and diseases. This paper reports findings from a baseline survey on factors associated with smoking, cessation behaviours and attitudes towards smoke-free homes among the Aboriginal population in inner and south-western Sydney. Methods A baseline survey was conducted in inner and south-western Sydney from October 2010 to July 2011. The survey applied both interviewer-administered and self-administered data collection methods. Multiple logistic regression was performed to determine the factors associated with smoking. Results Six hundred and sixty-three participants completed the survey. The majority were female (67.5%), below the age of 50 (66.6%) and more than half were employed (54.7%). Almost half were current smokers (48.4%) with the majority intending to quit in the next 6 months (79.0%) and living in a smoke-free home (70.4%). Those aged 30-39 years (AOR 3.28; 95% CI: 2.06-5.23) and the unemployed (AOR 1.67; 95% CI: 1.11-2.51) had higher odds for current smoking. Participants who had a more positive attitude towards smoke-free homes were less likely to smoke (AOR 0.79; 95% CI: 0.74-.85). Conclusions A high proportion of participants were current smokers among whom intention to quit was high. Age, work status and attitudes towards smoke-free home were factors associated with smoking. So what? The findings address the scarcity of local evidence crucial for promoting cessation among Aboriginal tobacco smokers. Targeted promotions for socio-demographic subgroups and of attitudes towards smoke-free homes could be meaningful strategies for future smoking-cessation initiatives.

Abnormal Brachial Plexus Differentiation from Routine Magnetic Resonance Imaging: An AI-based Approach.

Automatic abnormality identification of brachial plexus (BP) from normal magnetic resonance imaging to localize and identify a neurologic injury in clinical practice (MRI) is still a novel topic in brachial plexopathy. This study developed and evaluated an approach to differentiate abnormal BP with artificial intelligence (AI) over three commonly used MRI sequences, i.e. T1, FLUID sensitive and post-gadolinium sequences. A BP dataset was collected by radiological experts and a semi-supervised artificial intelligence method was used to segment the BP (based on nnU-net). Hereafter, a radiomics method was utilized to extract 107 shape and texture features from these ROIs. From various machine learning methods, we selected six widely recognized classifiers for training our Brachial plexus (BP) models and assessing their efficacy. To optimize these models, we introduced a dynamic feature selection approach aimed at discarding redundant and less informative features. Our experimental findings demonstrated that, in the context of identifying abnormal BP cases, shape features displayed heightened sensitivity compared to texture features. Notably, both the Logistic classifier and Bagging classifier outperformed other methods in our study. These evaluations illuminated the exceptional performance of our model trained on FLUID-sensitive sequences, which notably exceeded the results of both T1 and post-gadolinium sequences. Crucially, our analysis highlighted that both its classification accuracies and AUC score (area under the curve of receiver operating characteristics) over FLUID-sensitive sequence exceeded 90%. This outcome served as a robust experimental validation, affirming the substantial potential and strong feasibility of integrating AI into clinical practice.

Don't Follow the Smoke-Listening to the Tobacco Experiences and Attitudes of Urban Aboriginal Adolescents in the Study of Environment on Aboriginal Resilience and Child Health (SEARCH).

Preventing smoking among young Aboriginal people is important for reducing health inequities. Multiple factors were associated with adolescent smoking in the SEARCH baseline survey (2009-12) and discussed in a follow-up qualitative study that aimed to inform prevention programs. Twelve yarning circles were facilitated by Aboriginal research staff at two NSW sites in 2019 with 32 existing SEARCH participants aged 12-28 (17 female, 15 male). Open discussion around tobacco was followed by a card sorting activity, prioritising risk and protective factors and program ideas. The age of initiation varied by generation. Older participants had established smoking in their early adolescence, whereas the current younger teens had little exposure. Some smoking commenced around high school (from Year 7), and social smoking increased at age 18. Mental and physical health, smoke-free spaces and strong connections to family, community and culture promoted non-smoking. The key themes were (1) drawing strength from culture and community; (2) how the smoking environment shapes attitudes and intentions; (3) non-smoking as a sign of good physical, social and emotional wellbeing; and (4) the importance of individual empowerment and engagement for being smoke-free. Programs promoting good mental health and strengthening cultural and community connections were identified as a priority for prevention.

Establishing an enduring co-production platform in Aboriginal health.

Co­production in Aboriginal health research builds on participatory, strength-based approaches where Aboriginal knowledge, expertise and priorities are valued and used to generate evidence to drive improvements in Aboriginal health and healthcare. The Coalition for Research to Improve Aboriginal Health (CRIAH), led by a partnership between the Aboriginal Health & Medical Research Council (AH&MRC), Sax Institute and six Aboriginal Community Controlled Health Services (ACCHSs) in NSW, has a long history of successfully co-producing evidence to guide policy and program planning. Data collected through CRIAH's first project, the Study of Environment on Aboriginal Resilience and Child Health (SEARCH), a cohort of urban Aboriginal children, has been effectively used to improve child health outcomes. An evaluation of CRIAH's co-production model highlighted trusting relationships, sharing of power, valuing Aboriginal knowledge and expertise, respectful communication, strong Aboriginal leadership, and ongoing investment and collaboration as instrumental to the success and longevity of this multidisciplinary partnership. In recent years, CRIAH's co-production platform has responded to emerging areas of need identified by participating ACCHSs. This paper highlights three initiatives driven through the co-production platform: 1) examining how local mental health service systems are working for Aboriginal children and young people and how they can be improved; 2) understanding how the cancer care system is working for older Aboriginal people to develop scalable and sustainable approaches to improving cancer care; and 3) finding effective ways to measure the impact of innovative, Aboriginal-led primary health care programs. These initiatives represent co-produced, fit-for-purpose research aimed at driving tangible improvements in Aboriginal health..

Automated measurement of total kidney volume from 3D ultrasound images of patients affected by polycystic kidney disease and comparison to MR measurements.

PURPOSE: Total kidney volume (TKV) is the most important imaging biomarker for quantifying the severity of autosomal-dominant polycystic kidney disease (ADPKD). 3D ultrasound (US) can accurately measure kidney volume compared to 2D US; however, manual segmentation is tedious and requires expert annotators. We investigated a deep learning-based approach for automated segmentation of TKV from 3D US in ADPKD patients. METHOD: We used axially acquired 3D US-kidney images in 22 ADPKD patients where each patient and each kidney were scanned three times, resulting in 132 scans that were manually segmented. We trained a convolutional neural network to segment the whole kidney and measure TKV. All patients were subsequently imaged with MRI for measurement comparison. RESULTS: Our method automatically segmented polycystic kidneys in 3D US images obtaining an average Dice coefficient of 0.80 on the test dataset. The kidney volume measurement compared with linear regression coefficient and bias from human tracing were R2 = 0.81, and - 4.42%, and between AI and reference standard were R2 = 0.93, and - 4.12%, respectively. MRI and US measured kidney volumes had R2 = 0.84 and a bias of 7.47%. CONCLUSION: This is the first study applying deep learning to 3D US in ADPKD. Our method shows promising performance for auto-segmentation of kidneys using 3D US to measure TKV, close to human tracing and MRI measurement. This imaging and analysis method may be useful in a number of settings, including pediatric imaging, clinical studies, and longitudinal tracking of patient disease progression.

Radiomics-based machine learning (ML) classifier for detection of type 2 diabetes on standard-of-care abdomen CTs: a proof-of-concept study.

PURPOSE: To determine if pancreas radiomics-based AI model can detect the CT imaging signature of type 2 diabetes (T2D). METHODS: Total 107 radiomic features were extracted from volumetrically segmented normal pancreas in 422 T2D patients and 456 age-matched controls. Dataset was randomly split into training (300 T2D, 300 control CTs) and test subsets (122 T2D, 156 control CTs). An XGBoost model trained on 10 features selected through top-K-based selection method and optimized through threefold cross-validation on training subset was evaluated on test subset. RESULTS: Model correctly classified 73 (60%) T2D patients and 96 (62%) controls yielding F1-score, sensitivity, specificity, precision, and AUC of 0.57, 0.62, 0.61, 0.55, and 0.65, respectively. Model's performance was equivalent across gender, CT slice thicknesses, and CT vendors (p values > 0.05). There was no difference between correctly classified versus misclassified patients in the mean (range) T2D duration [4.5 (0-15.4) versus 4.8 (0-15.7) years, p = 0.8], antidiabetic treatment [insulin (22% versus 18%), oral antidiabetics (10% versus 18%), both (41% versus 39%) (p > 0.05)], and treatment duration [5.4 (0-15) versus 5 (0-13) years, p = 0.4]. CONCLUSION: Pancreas radiomics-based AI model can detect the imaging signature of T2D. Further refinement and validation are needed to evaluate its potential for opportunistic T2D detection on millions of CTs that are performed annually.

Housing conditions associated with recurrent gastrointestinal infection in urban Aboriginal children in NSW, Australia: findings from SEARCH.

OBJECTIVE: To examine the associations between housing and gastrointestinal infection in Aboriginal children in urban New South Wales. METHODS: A total of 1,398 Aboriginal children were recruited through four Aboriginal Community Controlled Health Services. Multilevel regression modelling of survey data estimated associations between housing conditions and recurrent gastrointestinal infection, adjusting for sociodemographic and health factors. RESULTS: Of the sample, 157 children (11%) had recurrent gastrointestinal infection ever and 37 (2.7%) required treatment for recurrent gastrointestinal infection in the past month. Children in homes with 3+ housing problems were 2.51 (95% CrI 1.10, 2.49) times as likely to have recurrent gastrointestinal infection ever and 6.79 (95% CrI 2.11, 30.17) times as likely to have received recent treatment for it (versus 0-2 problems). For every additional housing problem, the prevalence of recurrent gastrointestinal infection ever increased by a factor of 1.28 (95% CrI 1.14, 1.47) and the prevalence of receiving treatment for gastrointestinal infection in the past month increased by a factor of 1.64 (95% CrI 1.20, 2.48). CONCLUSIONS: Housing problems were independently associated with recurrent gastrointestinal infection in a dose-dependent manner. Implications for public health: The role of housing as a potential determinant of health in urban Aboriginal children merits further attention in research and policy settings.

The Study of Environment on Aboriginal Resilience and Child Health (SEARCH): a long-term platform for closing the gap.

The full potential for research to improve Aboriginal health has not yet been realised. This paper describes an established long-term action partnership between Aboriginal Community Controlled Health Services (ACCHSs), the Aboriginal Health and Medical Research Council of New South Wales (AH&MRC), researchers and the Sax Institute, which is committed to using high-quality data to bring about health improvements through better services, policies and programs. The ACCHSs, in particular, have ensured that the driving purpose of the research conducted is to stimulate action to improve health for urban Aboriginal children and their families. This partnership established a cohort study of 1600 urban Aboriginal children and their caregivers, known as SEARCH (the Study of Environment on Aboriginal Resilience and Child Health), which is now having significant impacts on health, services and programs for urban Aboriginal children and their families. This paper describes some examples of the impacts of SEARCH, and reflects on the ways of working that have enabled these changes to occur, such as strong governance, a focus on improved health, AH&MRC and ACCHS leadership, and strategies to support the ACCHS use of data and to build Aboriginal capacity.

A case study of enhanced clinical care enabled by Aboriginal health research: the Hearing, EAr health and Language Services (HEALS) project.

OBJECTIVE: To describe and evaluate Hearing EAr health and Language Services (HEALS), a New South Wales (NSW) health initiative implemented in 2013 and 2014 as a model for enhanced clinical services arising from Aboriginal health research. METHODS: A case-study involving a mixed-methods evaluation of the origins and outcomes of HEALS, a collaboration among five NSW Aboriginal Community Controlled Health Services (ACCHS), the Sydney Children's Hospitals Network, NSW Health, the Aboriginal Health and Medical Research Council, and local service providers. Service delivery data was collected fortnightly; semi-structured interviews were conducted with healthcare providers and caregivers of children who participated in HEALS. RESULTS: To circumvent health service barriers, HEALS used relationships established through the Study of Environment on Aboriginal Resilience and Child Health (SEARCH) to form a specialist healthcare network. HEALS employed dedicated staff and provided a Memorandum of Understanding (detailing mutual goals and responsibilities) for each ACCHS. Despite very tight timeframes, HEALS provided services for 653 Aboriginal children, including 5,822 speech-language pathology sessions and 219 Ear, Nose and Throat procedures. Four themes reflecting the perceived impact of HEALS were identified: valued clinical outcomes, raising community awareness, developing relationships/networks and augmented service delivery. CONCLUSIONS: HEALS delivered rapid and effective specialist healthcare services through an existing research collaboration with five ACCHS, cooperation from local health service providers, and effective community engagement. Implications for Public Health: HEALS serves as a framework for targeted, enhanced healthcare that benefits Aboriginal communities by encapsulating the 'no research without service' philosophy.