OrganPredict

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Organ-PREDICT using AI

Welcome to a new era in kidney transplant medicine. Organ PREDICT (Precision and Reliability in Estimating Donor Immunological Compatibility for Transplantation) seeks to transform the matching process for solid organ transplants, aiming for the best possible graft outcomes. This project employs advanced AI algorithms, meticulously refined and peer-reviewed to ensure utmost accuracy and reliability. After rigorous evaluation and recognition through acceptance in prestigious international journals, we have initiated further testing of our predictive models. 

Prediction of  Kidney Transplant Graft Survival Using AI

US-LTOP

AI based algorithms developed using UNOS database, helps you choose the Right Living Donor for the Right Recipient. The prediction model can also be used in national allocation schemes for Paired Kidney Exchange Programme. The outcome  is predicted for Death Censored and Overall Graft Survival for up to 14 years.

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US-DTOP

AI based algorithms developed using UNOS database, helps you choose the Right Deceased Donor for the Right Recipient. The prediction model can also be used in national allocation schemes for Paired Kidney Exchange Programme. The outcome  is predicted for Death Censored and Overall Graft Survival for up to 14 years.

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Publications

Our research on ‘Kidney Transplant Graft Survival Prediction’ utilizing artificial intelligence has been accepted for publication in two high-impact international journals. 
 
Below are the titles of the articles, the names of the authors, and the journals where our papers on living and deceased donor transplantation have been accepted.

1. Peer-Reviewed Journal Articles

Article 1:

Ali H, Mohammed M, Molnar MZ, Fülöp T, Burke B, Shroff S, Shroff A, Briggs D, Krishnan N.
Live-Donor Kidney Transplant Outcome Prediction (L-TOP) using Artificial Intelligence: to aid individual patient decisions and facilitate paired exchange schemes. Nephrol Dial Transplant. Published online 2024.
doi:10.1093/ndt/gfae088
URL: https://academic.oup.com/ndt/advance-article-abstract/doi/10.1093/ndt/gfae088/7659818?redirectedFrom=fulltext.

Article 2:

Ali H, Mohamed M, Molnar MZ, Fülöp T, Burke B, Shroff A, Shroff S, Briggs D, Krishnan N.
Deceased-Donor Kidney Transplant Outcome Prediction Using Artificial Intelligence (US-DTOP) that Outperforms KDPI: to Aid Individual Patient Decisions in Kidney Allocation Schemes.
ASAIO J. Published online 2024.
URL: https://journals.lww.com/asaiojournal/abstract/9900/deceased_donor_kidney_transplant_outcome.451.aspx.

Article 3:

Ali H, Sharif A, Fülöp T, Molnar MZ, Burke B, Shroff S, Shroff A, Briggs D, Krishnan N.
Artificial Intelligence Assisted Risk Prediction In Organ Transplantation: A UK Live-Donor Kidney Transplant Outcome Prediction (UK-LTOP) Tool.
Transplantation, Published Online September 2024
DOI: 10.1097/01.tp.0001065572.83581.03
URL:https://journals.lww.com/transplantjournal/fulltext/2024/09001/341_5__artificial_intelligence_assisted_risk.364.aspx

Article 4:

Ali H, Sharif A, Fülöp T, Molnar MZ, Burke B, Shroff S, Shroff A, Briggs D, Krishnan N.
Improved survival prediction for kidney transplant outcome using Artificial Intelligence-based models: Development of a UK Deceased Donor Kidney Transplant Outcome Prediction (UK-DTOP) Tool.
Transplantation. Published September 2024.
DOI: 10.1097/01.tp.0001065148.72869.98
URL: https://journals.lww.com/transplantjournal/fulltext/2024/09001/300_5__improved_survival_prediction_for_kidney.258.aspx

2. Conference Presentations

British Transplant Society (BTS) Congress 2023 & 2024

  1. Ali H, Mohammed M, Molnar MZ, et al. Medawar Medal Presentation. Presented at: British Transplant Society (BTS) Congress 2024; January 2024; Edinburgh, UK.
    URL: https://bts.org.uk/wp-content/uploads/2024/01/BTS-Congress-2024-programme-050124.pdf.

    Ali H, Sharif A, Krishnan N, et al. Development of prediction model for live kidney donor transplant outcomes prior to accepting an offer using the UK data: Oral presentation. Presented at: BTS Congress 2024; January 2024; Edinburgh, UK.
    URL: https://bts.org.uk/wp-content/uploads/2024/01/BTS-Congress-2024-programme-050124.pdf.

    Ali H, Mohamed M, Krishnan N, et al. Development of prediction model for live kidney donor transplant outcomes prior to accepting an offer using the USA data: Oral presentation. Presented at: BTS/NHSBT Conference 2023; March 2023; Newcastle, UK.
    URL: https://bts.org.uk/wp-content/uploads/2023/03/BTS-NHSBT-2023-Abstract-Book.pdf.

European Society for Organ Transplantation (ESOT) Congress 2023

  1. Ali H, Mohammed M, Molnar MZ, et al. Development of prediction models for deceased kidney donor transplants. Presented at: ESOT Congress 2023; September 2023; Athens, Greece.
    URL: https://www.esotcongress.org/wp-content/uploads/2023/09/ESOT_Congress_2023_Abstract_book_18th_September.pdf.

  2. Ali H, Mohammed M, Molnar MZ, et al. Development of prediction models for live kidney donor transplants. Presented at: ESOT Congress 2023; September 2023; Athens, Greece.
    URL: https://www.esotcongress.org/wp-content/uploads/2023/09/ESOT_Congress_2023_Abstract_book_18th_September.pdf.

The Transplantation Society (TTS) Congress 2022

  1. Ali H, Mohammed M, Molnar MZ, et al. Prediction of acute rejection post-kidney transplant: An Artificial Intelligence approach. Presented at: The Transplantation Society (TTS) Congress 2022; September 2022; Buenos Aires, Argentina (and virtual).
    URL: https://cm.tts2022.org/virtual/programme/2022-09-11.

  2. Ali H, Mohammed M, Molnar MZ, et al. Prediction of graft survival among living kidney transplants in the tacrolimus/MMF era: An Artificial Intelligence approach. Presented at: TTS Congress 2022; September 2022; Buenos Aires, Argentina (and virtual).
    URL: https://cm.tts2022.org/virtual/programme/2022-09-11.

  3. Ali H, Mohammed M, Molnar MZ, et al. Prediction of graft survival among deceased transplants: An AI approach. Presented at: TTS Congress 2022; September 2022; Buenos Aires, Argentina (and virtual).
    URL: https://cm.tts2022.org/virtual/programme/2022-09-11.

  4. Ali H, Mohammed M, Molnar MZ, et al. Are HLA-A, B, DR, and DQ mismatching important for kidney allocation schemes? UK registry data—An Artificial Intelligence approach. Presented at: TTS Congress 2022; September 2022; Buenos Aires, Argentina (and virtual).
    URL: https://cm.tts2022.org/virtual/programme/2022-09-11.

 
 

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