Journal of the American College of Surgeons
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As perioperative care shifts to a more patient-centered model, understanding needs and experiences of patients is vital. Gaining such insight can enhance the alignment of care with patient priorities, encouraging adherence to recovery-oriented interventions. We aimed to explore patient-defined recovery and the elements that modify the recovery process for patients with colorectal disease under enhanced recovery after surgery (ERAS) care. ⋯ Our patient-oriented recovery model may contribute a new dimension to the ERAS framework by capturing patients' recovery experiences. Further research is encouraged to explore its value in enhancing patient-centered care within ERAS.
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The success of parathyroidectomy in primary hyperparathyroidism depends on the intraoperative differentiation of diseased from normal glands. Deep learning can potentially be applied to digitalize this subjective interpretation process that relies heavily on surgeon expertise. In this study, we aimed to investigate whether diseased vs normal parathyroid glands have different near-infrared autofluorescence (NIRAF) signatures and whether related deep learning models can predict normal vs diseased parathyroid glands based on intraoperative in vivo images. ⋯ Normal and diseased parathyroid glands in primary hyperparathyroidism have different intraoperative NIRAF patterns that could be quantified with intensity and heterogeneity analyses. Visual deep learning models relying on these NIRAF signatures could be built to assist surgeons in differentiating normal from diseased parathyroid glands.