By Dr. Luciano Potena, ESOT President-Elect; Devi Mey, Director of Business Development, European Society for Organ Transplantation
Solid-organ transplantation (SOT) has developed from an experimental approach in the 20th century to become an established and practical definitive treatment choice for patients with end-stage organ failure (Jandovitz et al., 2018).
Patients requiring an organ transplant are a complex population; their journey includes pre-transplant evaluation and preparation, the identification of a suitable donor, and followed by post-transplant lifelong monitoring, all managed by a multidisciplinary team of healthcare professionals. The embracement and integration of digital health (DH) in such a setting are fundamental to improve access to transplant, quality of life and patient outcomes.
We can recognise several areas of DH development, which may significantly impact organ procurement, allocation and transplantation in the near future. For example, the creation of mobile networks of caregivers, e-consultation and shared electronic imaging and health record may improve donor selection by favouring the communication between the transplant team and the donor team to improve donor management (Barbero et al., 2019).
A similar infrastructure enriched with digital diagnostic tools with data transmission capabilities may enhance clinical monitoring with the remote collection of multi-parametric data, including blood pressure, urine output, sleep, steps and weight, and improving medication adherence by helping patients to monitor and manage their daily medicine doses. Furthermore, such an approach may lead to a better quality of life for reduced needs to travel, thus improving equity in access to care by reducing travel discomfort and cost for patients and empower patient self-awareness because patients will have access to daily tracking score to monitor their progress with their transplant goals and take an active role in monitoring their organ health. Ultimately, this approach will lead to a large amount of data that, by machine-learning-based analysis, may develop customised and effective decision-making algorithms, improving overall clinical management.
Artificial Intelligence (AI) and machine learning will indeed play a vital role in managing SOT patients. Transplant clinicians and surgeons are already increasingly using deep learning-based models to support their decisions. Any aspect of organ transplantation (e.g., image processing, prediction of results, diagnostic proposals, therapeutic algorithms, or precision treatments) consists of input variables and a set of output variables.
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