Using image segmentation models to analyse high-resolution earth observation data: new tools to monitor disease risks in changing environments.

BACKGROUND: In the near future, the incidence of mosquito-borne diseases may expand to new sites due to changes in temperature and rainfall patterns caused by climate change. Therefore, there is a need to use recent technological advances to improve vector surveillance methodologies. Unoccupied Aerial Vehicles (UAVs), often called drones, have been used to collect high-resolution imagery to map detailed information on mosquito habitats and direct control measures to specific areas. Supervised classification approaches have been largely used to automatically detect vector habitats. However, manual data labelling for model training limits their use for rapid responses. Open-source foundation models such as the Meta AI Segment Anything Model (SAM) can facilitate the manual digitalization of high-resolution images. This pre-trained model can assist in extracting features of interest in a diverse range of images. Here, we evaluated the performance of SAM through the Samgeo package, a Python-based wrapper for geospatial data, as it has not been applied to analyse remote sensing images for epidemiological studies. RESULTS: We tested the identification of two land cover classes of interest: water bodies and human settlements, using different UAV acquired imagery across five malaria-endemic areas in Africa, South America, and Southeast Asia. We employed manually placed point prompts and text prompts associated with specific classes of interest to guide the image segmentation and assessed the performance in the different geographic contexts. An average Dice coefficient value of 0.67 was obtained for buildings segmentation and 0.73 for water bodies using point prompts. Regarding the use of text prompts, the highest Dice coefficient value reached 0.72 for buildings and 0.70 for water bodies. Nevertheless, the performance was closely dependent on each object, landscape characteristics and selected words, resulting in varying performance. CONCLUSIONS: Recent models such as SAM can potentially assist manual digitalization of imagery by vector control programs, quickly identifying key features when surveying an area of interest. However, accurate segmentation still requires user-provided manual prompts and corrections to obtain precise segmentation. Further evaluations are necessary, especially for applications in rural areas.

Item-validity analysis of the SED-S in a multicentre study of adults with intellectual disabilities.

BACKGROUND: Valid and reliable instruments for measuring emotional development are critical for a proper diagnostic assignment in individuals with intellectual disabilities. This exploratory study examined the psychometric properties of the items on the Scale of Emotional Development-Short (SED-S). METHOD: The sample included 612 adults with intellectual disabilities (Mage = 37.35, SDage = 13.27; 59.8% males). Item validity analysis comprising sensitivity and specificity rates and discriminatory power were determined. RESULTS: The relative mean frequency of 'yes' answers to all 200 items was 29.5%. The mean sensitivity rate was 67.5% and the mean specificity rate was 79.3%. Most items (85.0%) showed good discriminatory power with the adjacent stage(s), especially between SED-1, SED-2, SED-3 and SED-4. Particularly in SED-4 some items showed weaknesses in the differentiation between these stages. DISCUSSION: This study adds to previous validation studies by showing that most SED-S items have psychometrically sound properties.

Can sodium and potassium measured in timed voids be used as reference instruments for validating self-report instruments? Results from a urine calibration study.

BACKGROUND: Sodium and potassium measured in 24-h urine collections are often used as reference measurements to validate self-reported dietary intake instruments. OBJECTIVES: To evaluate whether collection and analysis of a limited number of urine voids at specified times during the day ("timed voids") can provide alternative reference measurements, and to identify their optimal number and timing. METHODS: We used data from a urine calibration study among 441 adults aged 18-39 y. Participants collected each urine void in a separate container for 24 h and recorded the collection time. For the same day, they reported dietary intake using a 24-h recall. Urinary sodium and potassium were analyzed in a 24-h composite sample and in 4 timed voids (morning, afternoon, evening, and overnight). Linear regression models were used to develop equations predicting log-transformed 24-h urinary sodium or potassium levels using each of the 4 single timed voids, 6 pairs, and 4 triples. The equations also included age, sex, race, BMI (kg/m2), and log creatinine. Optimal combinations minimizing the mean squared prediction error were selected, and the observed and predicted 24-h levels were then used as reference measures to estimate the group bias and attenuation factors of the 24-h dietary recall. These estimates were compared. RESULTS: Optimal combinations found were as follows: single voids-evening; paired voids-afternoon + overnight (sodium) and morning + evening (potassium); and triple voids-morning + evening + overnight (sodium) and morning + afternoon + evening (potassium). Predicted 24-h urinary levels estimated 24-h recall group biases and attenuation factors without apparent bias, but with less precision than observed 24-h urinary levels. To recover lost precision, it was estimated that sample sizes need to be increased by ∼2.6-2.7 times for a single void, 1.7-2.1 times for paired voids, and 1.5-1.6 times for triple voids. CONCLUSIONS: Our results provide the basis for further development of new reference biomarkers based on timed voids. CLINICAL TRIAL REGISTRY: clinicaltrials.gov as NCT01631240.