RESUMEN
IMPORTANCE: Up to 50% of patients report not readily seeking treatment for pelvic floor disorders (PFDs). The increase in phone applications (apps) for health care information is an opportunity to increase access to care. OBJECTIVE: The aim of the study was to systematically evaluate content and function of apps for patients with PFDs. STUDY DESIGN: Apps were screened using PFD-related search terms. Included apps were on the Apple store, in English, and targeted patients with PFDs. The primary outcome was app quality based on the APPLICATIONS scoring system (scored 0-16). Secondary outcomes included professional medical involvement, iTunes rating details, the presence of a voiding/bowel diary, tracking of diet, pain/symptoms, exercise, and medication, graphing or social functions, reminders, disease information, and decision support. Data was reported with descriptive statistics (medians (ranges) and n (percentages). RESULTS: Eight hundred forty apps were identified and 83 were analyzed. The top 3 PFD categories represented were defecatory dysfunction (29), overactive bladder (28), and stress incontinence (27). The median APPLICATIONS score was 7 (3-12). Most apps (78%) were developed without professional medical involvement. Most apps were free, while the remainder ranged from $1.99 to $4.99. No app had all features. Twenty-five apps (30%) included a voiding diary, 33 (40%) had a bowel diary, 27 (33%) included exercise tracking, and 44 (53%) had reminder systems. CONCLUSIONS: Most apps had reasonable, but not high, functionality. Current apps provide varying degrees of overall utility, with limited disease information and decision support. Further collaboration with medical providers in app development would support better integration of clinician and patient needs.
RESUMEN
Background and Objective: Cancer is a leading cause of morbidity and mortality worldwide. The emergence of digital pathology and deep learning technologies signifies a transformative era in healthcare. These technologies can enhance cancer detection, streamline operations, and bolster patient care. A substantial gap exists between the development phase of deep learning models in controlled laboratory environments and their translations into clinical practice. This narrative review evaluates the current landscape of deep learning and digital pathology, analyzing the factors influencing model development and implementation into clinical practice. Methods: We searched multiple databases, including Web of Science, Arxiv, MedRxiv, BioRxiv, Embase, PubMed, DBLP, Google Scholar, IEEE Xplore, Semantic Scholar, and Cochrane, targeting articles on whole slide imaging and deep learning published from 2014 and 2023. Out of 776 articles identified based on inclusion criteria, we selected 36 papers for the analysis. Key Content and Findings: Most articles in this review focus on the in-laboratory phase of deep learning model development, a critical stage in the deep learning lifecycle. Challenges arise during model development and their integration into clinical practice. Notably, lab performance metrics may not always match real-world clinical outcomes. As technology advances and regulations evolve, we expect more clinical trials to bridge this performance gap and validate deep learning models' effectiveness in clinical care. High clinical accuracy is vital for informed decision-making throughout a patient's cancer care. Conclusions: Deep learning technology can enhance cancer detection, clinical workflows, and patient care. Challenges may arise during model development. The deep learning lifecycle involves data preprocessing, model development, and clinical implementation. Achieving health equity requires including diverse patient groups and eliminating bias during implementation. While model development is integral, most articles focus on the pre-deployment phase. Future longitudinal studies are crucial for validating models in real-world settings post-deployment. A collaborative approach among computational pathologists, technologists, industry, and healthcare providers is essential for driving adoption in clinical settings.