Uterine disorders classification based on symptomic, behaviorial data and ultrasound images

The Uterine Disorder Diagnostic Multimodal Dataset (UDDMD-2025) consists of 223 anonymized patient records collected through structured interviews and clinical ultrasound images and reports from the Department of Gynecology, R.S. Unit, Shaheed Tajuddin Ahmad Medical College Hospital, Bangladesh. Data collection took place between May 5–August 7, 2025 (95 days).

Each participant record includes diagnostic parameters extracted from ultrasound and medical reports, along with symptomatic and behavioral indicators gathered in person. All participants provided informed consent and shared their experiences without external influence.

The text dataset is provided as a single .xlsx file (also convertible to .csv), where each row represents one participant and each column corresponds to clinical or behavioral variables. The dataset is publicly accessible via Mendeley Data (DOI: 10.17632/pdzpdgc497.1).

To support multimodal research, the dataset includes a complete set of raw ultrasound images. For every participant in the .xlsx file, a corresponding image directory is provided, named P1, P2, …, P223, allowing direct mapping between tabular data and image evidence. Images were captured using Google Pixel 6 and iPhone 15 Pro devices and are shared in their original, unedited form for flexible preprocessing and analysis.

Ethical clearance was obtained from the Institutional Ethics Committee of Daffodil International University, and permission for clinical data collection was granted by the Assistant Director of Shaheed Tajuddin Ahmad Medical College Hospital.

All diagnostic fields were verified by departmental experts, and ultrasound summaries were manually entered and checked for consistency. The .xlsx file and image folders contain no personally identifiable information, ensuring full anonymization.

Ultrasound summaries were collected immediately after physician consultations, ensuring the reliability of diagnostic information. The combination of raw images and structured text allows robust multimodal applications, including image–text alignment, diagnostic modeling, and cross-modal learning.

This dataset supports research in:
-Predictive modeling of uterine disorders
-Behavioral–clinical correlation analysis
-Machine learning for reproductive health
-Multimodal medical data integration
-Image–text paired training for medical AI

By integrating validated diagnostics, raw ultrasound images, and behavioral indicators, UDDMD-2025 offers a strong foundation for advancing AI-based women’s health research and evidence-driven gynecological analytics.