Biopreservation and Biobanking

The efficient management of consent information is essential for the ethical and legal handling of biobank resources in accordance with participant consent. However, many traditional biobanks rely on paper-based consent forms, which are often illegible and unsuitable for processing at scale. This study aims to automate the reading and quality control of paper-based consent forms.

We optimized a proprietary optical character recognition (OCR) model to recognize handwritten Korean characters in a standard paper-based consent template. We generated 1000 synthetic consent documents for training. The test dataset, comprising synthetic standard consent forms (n = 192), was used to estimate recognition accuracy. Then, this model was further trained with synthetic nonstandard consent forms (n = 1000) to optimize for the unstructured consent forms. The final model was then applied to the routine consent management process of the biobank using 3,790 pages of consent forms for the performance evaluation.

This optimized OCR model showed an accuracy of 88.94% and 91.88% when tested on the 192-page standard and 1000-page nonstandard test datasets of paper-based consent forms, respectively. Moreover, when this OCR model was applied to consent forms in a routine of biobanking processes, it showed an accuracy of 91.25% and an F1-score of 0.91, indicating the model’s high overall performance and excellent generalization capability for data.

We optimized a proprietary artificial intelligence-based OCR tool to develop a highly efficient and reliable OCR-based consent management model for paper-based consent documents. This approach could contribute to the digital transformation of traditional biobanking processes of paper-based consent forms.

This study is part of the broader Stem Line project Mito-Cell-UAB073, specifically focusing on “Stem Cell Lines-Quality Control,” and aims to innovate in the field of Quality Control (QC) through a unique, artificial intelligence (AI)-powered model known as Life Cell AI UAB. This model utilizes deep learning algorithms and computer vision, allowing it to make accurate viability assessments of cell and stem cell lines based solely on static images captured through standard optical microscopes.

The aim of this study was to develop and validate an AI-driven, image-based model that reliably predicts cell line viability.

Our methodology involved training the Life Cell AI UAB model on single static images of cell lines using advanced computer vision and deep learning techniques. Performance evaluation was conducted on three independent blind test sets sourced from various biotechnology laboratories, allowing for assessment across diverse environments.

The Life Cell AI UAB model achieved a sensitivity of 82.1% in identifying viable cell lines and a specificity of 67.5% for non-viable lines across the test sets. Each blind test set exhibited a weighted accuracy above 63%, with a combined accuracy of 64.3%. Notably, predictions showed a clear distinction between correctly and incorrectly classified cells. The model outperformed traditional QC methods by improving accuracy in binary classification tasks by 21.9% (p = 0.042) and demonstrated a 42.0% enhancement over conventional Standard Operation Procedure (SOP) procedures (p = 0.026).

The Life Cell AI UAB model represents a notable advancement in biobanking QC, offering a precise, standardized, and non-invasive method for assessing cell line viability. This model has the potential to streamline QC processes across laboratories, minimizing the need for time-lapse imaging and promoting uniformity in QC practices for both cell and stem cells.

Biobanks often lack standard mechanisms to keep donors connected to their biospecimens, reflecting a broken feedback loop that compromises trust, engagement, and scientific progress. Decentralized biobanking empowers patients to track their donations throughout the research journey, supporting personalized feedback and research collaboration via a privacy-preserving blockchain network. This case study explores operational feasibility of implementing decentralized biobanking for a large breast cancer biobank at a US academic medical center.

A mixed-methods case study of the groundwork, implementation, and stakeholder feedback for a real-world decentralized biobanking app pilot was conducted. Biobank members were recruited from February to April 2023. Operational feasibility was assessed via analysis of institutional stakeholder perspectives, pilot engagement, and de-bi app activity.

Physicians and other biobank stakeholders surfaced challenges surrounding managing expectations, balancing empowerment with clinical and research workflows, and navigating power dynamics between patients, physicians, scientists, and leadership. A total of 1080 participants enrolled over 10 weeks, including nearly 10% of the biobank with about 4000 biospecimens. During the pilot, biobank enrollment increased 65% versus the prior year, and there were no biobank withdrawals during or within 1 year following the pilot (p < 0.001 for both). The app, which facilitated biospecimen tracking and research engagement, was downloaded by 405 users. A total of 140 users tested the blockchain component, with 89% successfully claiming a nonfungible token representing their unique, immutable connection and access to donated biospecimens. Feedback was solicited to inform potential process improvements and risk assessments related to public relations, systems infrastructure, and ethical governance, illuminating next steps.

We established operational feasibility for the first step toward decentralized biobanking, informed by requirements to manage expectations, workflows, and power dynamics. Our technical solution demonstrated robust participant engagement and compatibility with established biobanks, suggesting potential to build trust and align incentives and identifying next steps for communications, sustainability, and governance.

Advancements in biomedical research depend on the quality and availability of biological samples. Despite their sophisticated storage capabilities, biobanks face significant challenges in sample management, with stored specimens often remaining unused and researchers struggling to access the required samples.

To analyze the challenges in biospecimen access and traceability, evaluate existing solutions, and propose a framework for integrated sample management in global research collaboration.

A scoping review was conducted across PubMed, Scopus, and Web of Science databases, supplemented by grey literature (2004–2024). The analysis included an examination of Biobank Information Management Systems and an evaluation of sample management systems, tracking technologies, and governance frameworks.

The analysis revealed fragmented management systems, with at least 38 different biobanking software solutions offering limited interoperability. Proprietary systems and vendor lock-ins create significant barriers to data sharing. Sample tracking shows the evolution from manual to digital systems; however, cross-institutional tracking remains challenging. Reproducibility issues account for significant challenges in research, whereas inefficient resource utilization persists, with 67% of biobanks citing underutilization as a major concern.

Addressing biobank sample access and traceability requires a shift from an institution-centric to an ecosystem-wide approach. Its success depends on integrating technological solutions such as Blockchain, the Internet of Things, and artificial intelligence with governance frameworks while ensuring alignment with stakeholder needs. Future developments should focus on implementing integrated traceability systems that support transparent and accountable sample management across the global research ecosystem.