(1) Background: Hypertension is a prevalent chronic condition requiring sustained self-management to prevent complications; however, long-term adherence to monitoring, medication, and lifestyle modification remains suboptimal. Mobile health (mHealth) technologies, supported by cloud-based connectivity, offer scalable platforms for structured remote monitoring and patient engagement. This study aimed to design, develop, and validate MyHTCare, a user-centered mHealth application for comprehensive hypertension self-management and connected remote monitoring. (2) Methods: An Agile-based, three-iterative development framework was adopted, incorporating clinical recommendations and inputs from patients, caregivers, and physicians. The application was developed using Flutter and integrated with Cloud Fire store to enable secure cloud-based data storage and real-time synchronization. Core modules included blood pressure tracking, medication reminders, infographic-based lifestyle education, and automated clinical alerts. Content validation involved 10 experts (clinicians and IT professionals) and 30 end users (adults with hypertension or caregivers). Usability was assessed using a pre-tested structured questionnaire, and educational materials were evaluated using the Patient Education Materials Assessment Tool for Audiovisual Materials (PEMAT-A/V). (3) Results: End-user validation demonstrated high usability, with mean scores ranging from 4.4 to 5.0 on a 5-point scale. Educational materials achieved 100% actionability and 92–100% understandability. Expert evaluation showed high ratings across usability domains, with acceptability scores exceeding 70%. (4) Conclusions: MyHTCare demonstrated strong content validity and usability, supporting further clinical evaluation to determine its effectiveness in improving blood pressure control and self-management.
Prajwal L. Salins, P. Kundapur, S. K. Mandapam et al.· Digital· 0 citations
The core of precision agriculture lies in the effective management of weed growth, a practice that is crucial for achieving maximum crop yield while using only a required amount of herbicides. Although deep learning has improved automation at a high level, a challenge still remains in the area of high performance segmentation models: pixel-level annotations, which are expensive and rarely available in real-world datasets. Addressing this problem, we proposed a weakly supervised design framework to overcome the missing ground-truth segmentation masks. Instead of relying on manual tracing, our approach is made up of a two-stage pipeline using the bounding box annotations to create pseudo-masks with vegetation indices. This innovative idea allows the DeepLabV3+ model to train and learn to distinguish weed structures from complex soil textures and crop backgrounds. In the further stage, the obtained background-suppressed are fed as input into convolutional neural network architectures for species-level classification. The results show that despite the presence of class imbalance problem and high inter-class similarity, the background suppression strategy provides a very high classification accuracy up-to approximately 88%. To validate that model activations align with plant morphological structures such as leaf margins and venation patterns rather than environmental context, gradient-weighted class activation mapping (Grad-CAM) is applied, with quantitative analysis confirming improved morphological focus in segmentation-preprocessed models. Importantly, bounding box annotations are required only during training, at inference time, the pipeline accepts only a raw RGB field image with no annotation input. Finally, this study provides a very cost-effective, end-to-end optimized solution that bridges the gap between coarse, already available datasets and fine-grained precision required for practical field deployment.
Vaibhavv Maheshwari, Shreeya Mohanty, Prakash K. Aithal et al.· IEEE Access· 0 citations
Accurate delineation of target volumes and organs at risk (OAR) is essential in radiotherapy planning for cervical cancer. Deep learning (DL)-based auto-segmentation has the potential to improve contouring efficiency and workflow. This study reports the prospective development and internal validation of a DL-based auto-segmentation model- Deep contour (DC) for cervical cancer radiotherapy. In this prospective single-institution study, a 2-dimensional convolutional neural network based on the LinkNet architecture, DC, was trained on 190 computed tomography (CT) datasets for abdominal and pelvic OARs and 90 cervical cancer datasets for target volumes. Independent validation was performed on 20 CT datasets. Model performance was evaluated using dice similarity coefficient (DSC), Jaccard Index (JI), 95th percentile Hausdorff distance (HD95), average symmetric surface distance (ASSD), and surface dice coefficient (NSD). Expert internal and external radiation oncologists qualitatively explored clinical acceptability using a Likert scale, and segmentation time was compared with manual contouring. The DC demonstrated the greatest geometric performance for the femur (DSC 0.92 ± 0.03; NSD 0.94 ± 0.04), bowel bag (DSC 0.89 ± 0.03; NSD 0.77 ± 0.12), and bladder (DSC 0.88 ± 0.14; NSD 0.87 ± 0.12). Among the target volumes, the inguinal nodal clinical target volume (CTVn_inguinal) achieved the greatest agreement (DSC 0.77 ± 0.04; NSD 0.76 ± 0.07). Moderate performance was observed for the rectum (DSC 0.75 ± 0.16), liver (DSC 0.73 ± 0.21), and pelvic nodal clinical target volume (CTVn_pelvis) (DSC 0.60 ± 0.10), whereas lower performance was observed for anatomically complex structures such as the duodenum, anal canal, common bile duct, pancreas, and pelvic vessels. Clinical evaluation of two cases revealed a Likert score of III-IV for key pelvic organs, such as the bladder, femur, pelvic bowel bag, rectum, and sigmoid. Auto-segmentation significantly reduced the segmentation time from 77 min to 5 s per dataset (p < 0.001). This prospective validation demonstrates that DC auto-segmentation model can achieve acceptable geometric performance congruent across multiple abdominal and pelvic OARs and reasonable geometric performance for the elective inguinal CTV volume. Further validation on larger datasets and evaluation of clinical workflow integration are warranted. CTRI, TRN: CTRI/2024/02/063055, Registration date: February 22, 2024.
S. Menon, Mahak Gupta, Aakriti Bhardwaj et al.· Journal of the Egyptian Nati...· 0 citations
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