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(TSFAM) model, an adaptive human-AI teaming framework designed to enhance hard-to-place kidney acceptance decision-making by integrating transplant surgeons’ individualized expertise with advanced AI analytics (Figure 1). Methods: TSFAM is an innovative solution for complex issues in kidney transplant decision-making support. It employs fuzzy associative memory to capture and codify unique decision-making rules of transplant surgeons. Using the Deceased Donor Organ Assessment (DDOA) and Final Acceptance AI models designed to evaluate hard-to-place kidneys, TSFAM integrates fuzzy logic with deep learning techniques to manage inherent uncertainties in donor organ assessments. Surgeon-specifi c ontologies and membership functions are extracted through interviews. Similar to how a pain scale is used for understanding patients, an ontology ambiguity scale is used to develop surgeon rules (Figure 2). Fuzzy logic captures ambiguity and enables the model to adapt to evolving clinical, environmental, and policy conditions. The structured incorporation of human expertise ensures decision support remains closely aligned with local clinical practices and global best evidence. Results: This novel framework incorporates human expertise into AI decisionmaking tools to support donor organ acceptance in transplantation. Integrating surgeon-defi ned criteria into a robust decision-support tool enhances accuracy and transparency of organ allocation decision-making support. TSFAM bridges the gap between data-driven models and nuanced judgment required in complex clinical scenarios, fostering trust and promoting responsible AI adoption. Conclusions: TSFAM fuses deep learning analytics with subtleties of human expertise for a promising pathway to improve decision-making support in transplant surgery. The framework enhances clinical assessment and sets a precedent for future systems prioritizing human-AI collaboration. Prospective studies will focus on clinical implementation with dynamic interfaces for a more patient-centered, evidencebased model in organ transplantation. The intent is for this approach to be adaptable to individual case scenarios and the diverse needs of key transplant team members
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Reducing Kidney Discard with Artificial Intelligence Decision Support: The Need for a Transdisciplinary Systems Approach
Purpose of Review: A transdisciplinary systems approach to the design of an artificial intelligence (AI) decision support system can more effectively address the limitations of AI systems. By incorporating stakeholder input early in the process, the final product is more likely to improve decision-making and effectively reduce kidney discard. Recent Findings: Kidney discard is a complex problem that will require increased coordination between transplant stakeholders. An AI decision support system has significant potential, but there are challenges associated with overfitting, poor explainability, and inadequate trust. A transdisciplinary approach provides a holistic perspective that incorporates expertise from engineering, social science, and transplant healthcare. A systems approach leverages techniques for visualizing the system architecture to support solution design from multiple perspectives. Summary: Developing a systems-based approach to AI decision support involves engaging in a cycle of documenting the system architecture, identifying pain points, developing prototypes, and validating the system. Early efforts have focused on describing process issues to prioritize tasks that would benefit from AI support.
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- Award ID(s):
- 2026324
- PAR ID:
- 10299348
- Date Published:
- Journal Name:
- Current transplantation reports
- ISSN:
- 2196-3029
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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The objective of this study was to investigate factors influencing one’s decision to become a live kidney donor under the framework of sociotechnical systems, by expanding the focus to include larger organizational influences and technological considerations. Semi-structured interviews were conducted with live kidney donors who donated through University of Louisville Health, Trager Transplant Center, a mid-scale transplant program, in the years 2017 through 2019. The interview transcripts were analyzed for barriers and facilitators to live kidney donation within a sociotechnical system. The most salient facilitators included: having an informative, caring, and available care team; the absence of any negative external pressure toward donating; donating to a family or friend; and the ability to take extra time off work for recovery. The most recurrent barriers included: short/medium-term (<1 year) negative health impacts because of donation; the need to make minor lifestyle changes (e.g., less alcohol consumption) after donation; and mental health deterioration stemming from the donation process. The sociotechnical systems framework promotes a balanced system comprised of social, technical, and environmental subsystems. Assessing the facilitators and barriers from the sociotechnical system perspective revealed the importance of and opportunities for developing strategies to promote integration of technical subsystem, such as social media apps and interactive AI platforms, with social and environmental subsystems to enable facilitators and reduce barriers effectively.more » « less
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Kidney transplantation remains the preferred treatment for patients with end-stage kidney disease. However, the ongoing shortage of donor organs continues to limit the availability of transplant treatments. Existing evaluation methods, such as the kidney donor profile index (KDPI) and pre-transplant donor biopsy (PTDB), have various limitations, including low discriminative power, invasiveness, and sampling errors, which reduce their effectiveness in organ quality assessment and contribute to the risk of unnecessary organ discard. In this study, we explored the dynamic optical coherence tomography (DOCT) as a label-free, non-invasive approach to monitor the viability ofex vivomouse kidneys during static cold storage over 48 hours. The dynamic metrics logarithmic intensity variance (LIV), early OCT correlation decay speed (OCDSe), and late OCT correlation decay speed (OCDSl) were extracted from OCT signal fluctuations to quantify temporal and spatial tissue activity and deterioration. Our results demonstrate that DOCT provides complementary information relevant to tissue viability, in addition to the morphological assessment offered by conventional OCT imaging, showing potential to improve pre-transplant organ evaluation and clinic decision-making.more » « less
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Purpose: The equitable distribution of donor kidneys is crucial to maximizing transplant success rates and addressing disparities in healthcare data. This study examines potential gender bias in the Deceased Donor Organ Allocation Model (DDOA) by using machine learning and AI to analyze its impact on kidney discard decisions to ensure fairness in accordance with medical ethics. Methods: The study employs the Deceased Donor Organ Allocation Model (DDOA) model (https://ddoa.mst.hekademeia.org/#/kidney) to predict the discard probability of deceased donor kidneys using donor characteristic from the OPTN Deceased Donor Dataset (2016-2023). Using the SRTR SAF dictionary, the dataset consists of 18,029 donor records, where gender was assessed for its effect on discard probability. ANOVA and t-test determines whether there is a statistically significant difference between the discard percentages for female and male donors by changing the donor gender data alone. If the p-value obtained from the t-test is less than the significance level (typically 0.05), we reject the null hypothesis and conclude that there is a significant difference. Otherwise, we fail to reject the null hypothesis. Results: Figure 1 visualizes the differences in discard percentages between female and male donor kidneys, with an unbiased allocation system expected to show no difference (i.e., a value of zero). To assess the presence of gender bias, statistical analyses, including t-tests and ANOVA were performed. The t-test comparing female and male kidney discard rates yielded a t-statistic of 29.690228, with a p-value of 3.586956e-189 < 0.05 significance threshold. This result leads to the rejection of the null hypothesis, indicating a significant difference was found between the mean when altering only the donor gender attribute in the DDOA model making it play a significant role in discard decisions. Conclusions: The study highlights that a significant difference was found between the mean by altering only the donor gender attribute, contributing to kidney discard rates in the DDOA model. These findings reinforce the need for greater transparency in organ allocation models and a reconsideration of the demographic criteria used in the evaluation process. Future research should refine algorithms to minimize biases in organ allocation and investigate kidney discard disparities in transplantation.more » « less
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