Enhancing Prostate Cancer Diagnosis with a Novel Artificial Intelligence-Based Web Application: Synergizing Deep Learning Models, Multimodal Data, and Insights from Usability Study with Pathologists.

Prostate cancer remains a significant cause of male cancer mortality in the United States, with an estimated 288,300 new cases in 2023. Accurate grading of prostate cancer is crucial for ascertaining disease severity and shaping treatment strategies. Modern deep learning techniques show promise in grading biopsies, but there is a gap in integrating these advances into clinical practice. Our web platform tackles this challenge by integrating human expertise with AI-driven grading, incorporating diverse data sources. We gathered feedback from four pathologists and one medical practitioner to assess usability and real-world alignment through a survey and the NASA TLX Usability Test. Notably, 60% of users found it easy to navigate, rating it 5.5 out of 7 for ease of understanding. Users appreciated self-explanatory information in popup tabs. For ease of use, all users favored the detailed summary tab, rating it 6.5 out of 7. While 80% felt patient demographics beyond age were unnecessary, high-resolution biopsy images were deemed vital. Acceptability was high, with all users willing to adopt the app, and some believed it could reduce workload. The NASA TLX Usability Test indicated a low-moderate perceived workload, suggesting room for improved explanations and data visualization.

Applications of AI in multi-modal imaging for cardiovascular disease.

Data for healthcare is diverse and includes many different modalities. Traditional approaches to Artificial Intelligence for cardiovascular disease were typically limited to single modalities. With the proliferation of diverse datasets and new methods in AI, we are now able to integrate different modalities, such as magnetic resonance scans, computerized tomography scans, echocardiography, x-rays, and electronic health records. In this paper, we review research from the last 5 years in applications of AI to multi-modal imaging. There have been many promising results in registration, segmentation, and fusion of different magnetic resonance imaging modalities with each other and computer tomography scans, but there are still many challenges that need to be addressed. Only a few papers have addressed modalities such as x-ray, echocardiography, or non-imaging modalities. As for prediction or classification tasks, there have only been a couple of papers that use multiple modalities in the cardiovascular domain. Furthermore, no models have been implemented or tested in real world cardiovascular clinical settings.

Solute exchanges between multi-depth groundwater and surface water of climatically vulnerable Gangetic delta front aquifers of Sundarbans.

The coastal aquifers of Sundarbans, an UNESCO world biodiversity heritage site, are highly vulnerable due to changing climatic conditions, intensification and increasing frequency of extreme climate events and uncontrolled abstraction of groundwater. The exchange of solutes between hydraulically connective shallow and deep aquifers, the seawater intrusion and the role of growing population are poorly understood in the Sundarbans. This study aims to address the solute exchange (Cl-, Sr2+, and salinity) process between surface water and groundwater (SW-GW) at local to regional scale under variable hydraulic head conditions, where annual rainfall is declining and population density is increasing [population 573 (1991) to 819 (2011)/Km2]. Electrical resistivity tomography (ERT) in combination with salinity and δ18O data was used to address the exchange of solutes between SW-GW in a hydraulic continuation. The results revealed that regionally, the Cl- concentration of Sundarbans shows an increasing trend (average 329-351 mg/L) with declining groundwater levels (⁓3 m). Local, depth-dependent study depicting there is a predominant exchange of Sr2+ between shallow depth [D1: 14-25 and D2: 30-50 m below ground level (m bgl)] with seawater (Sr2+: 30-85 µM), which is possibly absent at greater depths (D3:115 and D4: 333 m bgl). The recorded Sr2+ content ranged from 25 to 102 and 16 to 78 µM for shallow depth D1 and D2, respectively, whereas, the Sr2+ concentrations ranged from 1.4 to 6.8 and 1.2 to 5.7 µM for D3 and D4, respectively. The ERT data showed progressively increasing resistivity with increasing depth, similar to high salinity and enriched δ18O at shallow depths and depleted δ18O with low salinity at higher depth reflects the continuous distribution of solutes, which is possibly a result of local downward migration of contaminated shallow brackish water within this physically disconnected zone. The lateral and vertical transportation of solutes in variable hydraulic head conditions would be a measure of drinking water threat in present-day and in imminent future for millions of inhabitants near the coastal area.