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A deep learning algorithm that can teach itself to learn features has been created by researchers from the Mahmood Lab at Brigham and Women’s Hospital, a founding institution of the Mass General Brigham healthcare system. This algorithm can be used to find similar cases in vast pathology image repositories.

Although it has long been known that predicting outcomes in cancer patients necessitates taking into account a variety of characteristics, including patient history, genes, and disease pathology, doctors find it difficult to combine this data when making decisions regarding patient care.

Patients with end-stage heart failure may benefit from a heart transplant. Many patients, unfortunately, suffer from organ transplant rejection, which occurs when the immune system destroys the donated organ. However, diagnosing transplant rejection is difficult.

  Heart transplantation can be lifesaving for patients with end-stage heart failure. However, many patients experience organ transplant rejection, in which the immune system attacks the transplanted organ. But detecting transplant rejection is challenging. In its early stages, patients may not experience symptoms, and experts do not always agree on the degree and severity of the rejection when they examine heart biopsies to diagnose the problem.