Scoring PD-L1 Expression in Urothelial Carcinoma: An International Multi-Institutional Study on Comparison of Manual and Artificial Intelligence Measurement Model (AIM-PD-L1) Pathology Assessments.

Assessing programmed death ligand 1 (PD-L1) expression on tumor cells (TCs) using Food and Drug Administration-approved, validated immunoassays can guide the use of immune checkpoint inhibitor (ICI) therapy in cancer treatment. However, substantial interobserver variability has been reported using these immunoassays. Artificial intelligence (AI) has the potential to accurately measure biomarker expression in tissue samples, but its reliability and comparability to standard manual scoring remain to be evaluated. This multinational study sought to compare the %TC scoring of PD-L1 expression in advanced urothelial carcinoma, assessed by either an AI Measurement Model (AIM-PD-L1) or expert pathologists. The concordance among pathologists and between pathologists and AIM-PD-L1 was determined. The positivity rate of ≥ 1%TC PD-L1 was between 20-30% for 8/10 pathologists, and the degree of agreement and scoring distribution for among pathologists and between pathologists and AIM-PD-L1 was similar both scored as a continuous variable or using the pre-defined cutoff. Numerically higher score variation was observed with the 22C3 assay than with the 28-8 assay. A 2-h training module on the 28-8 assay did not significantly impact manual assessment. Cases exhibiting significantly higher variability in the assessment of PD-L1 expression (mean absolute deviation > 10) were found to have patterns of PD-L1 staining that were more challenging to interpret. An improved understanding of sources of manual scoring variability can be applied to PD-L1 expression analysis in the clinical setting. In the future, the application of AI algorithms could serve as a valuable reference guide for pathologists while scoring PD-L1.

Artificial Intelligence Enables Quantitative Assessment of Ulcerative Colitis Histology.

Ulcerative colitis is a chronic inflammatory bowel disease that is characterized by a relapsing and remitting course. Assessment of disease activity critically informs treatment decisions. In addition to endoscopic remission, histologic remission is emerging as a treatment target and a key factor in the evaluation of disease activity and therapeutic efficacy. However, manual pathologist evaluation is semiquantitative and limited in granularity. Machine learning approaches are increasingly being developed to aid pathologists in accurate and reproducible scoring of histology, enabling precise quantitation of clinically relevant features. Here, we report the development and validation of convolutional neural network models that quantify histologic features pertinent to ulcerative colitis disease activity, directly from hematoxylin and eosin-stained whole slide images. Tissue and cell model predictions were used to generate quantitative human-interpretable features to fully characterize the histology samples. Tissue and cell predictions showed comparable agreement to pathologist annotations, and the extracted slide-level human-interpretable features demonstrated strong correlations with disease severity and pathologist-assigned Nancy histological index scores. Moreover, using a random forest classifier based on 13 human-interpretable features derived from the tissue and cell models, we were able to accurately predict Nancy histological index scores, with a weighted kappa (κ = 0.91) and Spearman correlation (⍴ = 0.89, P < .001) when compared with pathologist consensus Nancy histological index scores. We were also able to predict histologic remission, based on the absence of neutrophil extravasation, with a high accuracy of 0.97. This work demonstrates the potential of computer vision to enable a standardized and robust assessment of ulcerative colitis histopathology for translational research and improved evaluation of disease activity and prognosis.

A Usability Evaluation of the InfoSAGE App for Family-Based Medication Management.

The design of a mobile medication manager within a broader family and elder-centric collaboration platform faces challenges of usability and wide applicability. To inform the development and use cases of eldercare apps, we present the preliminary results of a usability study of an iOS and Android app intended for both family members and aging adults for the mobile management of medication lists. Seven participants were recorded during the performance of eight typical use-case scenarios of the medication portion of the InfoSAGE app. Audio and video recordings were analyzed for themes and events. The aim of this paper is to help inform future design choices for eldercare mobile apps.

Information sharing across generations and environments (InfoSAGE): study design and methodology protocol.

BACKGROUND: Longevity creates increasing care needs for healthcare providers and family caregivers. Increasingly, the burden of care falls to one primary caregiver, increasing stress and reducing health outcomes. Additionally, little has been published on adults', over the age of 75, preferences in the development of health information sharing with family members using online platforms. This study aims to assess a novel, Internet based, family-centric communication and collaboration platform created to address the information needs of elders and their informal caregivers in a community setting. METHODS: This study is an internet-based, open prospective cohort study, enrolling dyad pairs of one adult over the age of 75 with one informal caregiver. Dyads will be offered to use the InfoSAGE online platform without prospective assignment. Participants will consent using an online process that enables participation from any location and shares important study and privacy details. The platform will enable the capture of search queries and tracking of functions such as tasks and discussions. Surveys every six months assess health status, health and social needs, and caregiver burden using validated instruments over a two-year period. We will use a mixed methods approach, utilizing qualitative survey data along with website usage analytic data. DISCUSSION: Analysis of the longitudinal usage and survey data will help to examine the patterns of family communication and health information seeking as the central older adult ages. We will use the study data to inform design recommendations relevant to a complex mixture of users, with special consideration to the needs of older adult users and potential physical limitations.

A Systematic Review on Promoting Adherence to Antiretroviral Therapy in HIV-infected Patients Using Mobile Phone Technology.

OBJECTIVE: Adherence to antiretroviral therapy (ART) is paramount to successful long-term suppression of human immunodeficiency virus (HIV). For poorly adherent patients with HIV, barriers to remaining adherent may be overcome by the implementation of targeted interventions delivered via mobile devices. This systematic review is focused specifically on mobile phone technologies to deliver adherence interventions in HIV/acquired immunodeficiency syndrome (AIDS) populations. METHODS: This review (PROSPERO #CRD42017065131) systematically extracted data from published literature from five databases on mobile phone interventions to improve adherence to ART for HIV. The reported studies had been conducted between 2007 and 2017. Risk of bias was assessed using the Cochrane method ranking each criterion as low, high, or unclear risk of bias. RESULTS: Of the 835 articles returned, we identified 26 randomized controlled trials (RCTs), retrospective and prospective cohort trials, or mixed method studies with a comparison group that fit criteria for inclusion. No standard measure of adherence was consistent throughout the examined studies, and assessments by self-report, pill counting, and medication event monitoring system (MEMS) were utilized. The studies reported mixed results, with 17 reporting significant improvements to adherence, 3 reporting improvements without supplying p-values, and 6 reporting no significant change or a reduction in adherence. CONCLUSION: The mixed nature of the results exemplifies the need for more comprehensive approaches and larger scale trials to confirm results observed in limited cohort sizes. To better retain satisfactory adherence within the HIV population, and especially in low-resource settings, we recommend that future interventions incorporate multiple strategies: mobile-based reminders, social support structures, and personalized content.

InfoSAGE: Supporting Elders and Families through Online Family Networks.

With an increasingly elderly population, families are finding it increasingly challenging to coordinate care for their older family members. This paper reports on the findings of InfoSAGE, an online private social network that has tools for communication and care coordination for elders and their families. The InfoSAGE system has 257 registered users; 52 of these opted into an in-depth longitudinal study. A descriptive analysis of these early participants, the online family networks, and barriers to participation that were encountered are presented.

InfoSAGE: Use of Online Technologies for Communication and Elder Care.

Aging creates new information and communication needs for families who are helping to coordinate care for frail parents. To identify how information and communication needs evolve with the aging process, we created a living laboratory of families, supported by an online private social network with tools for care coordination. Site registrants are invite to participate in a more in-depth survey-based longitudinal study. In year one, we assessed the feasibility of an online living laboratory. During this first year, 155 individuals registered on InfoSAGE, and 26% opted into the more in-depth longitudinal study. The survey response rate for those in the study was 61%. We present here a descriptive analysis of our early participants and networks, as well as barriers to participation that the study team encountered.