Multi‐Parameter Deep Learning Combined With Fluorescence Lifetime Imaging Microscopy for Non‐Invasive and Label‐Free Endometrial Cancer Screening

ABSTRACT This study presents a noninvasive and label‐free endometrial cancer screening approach combining fluorescence lifetime imaging microscopy (FLIM) with a multi‐parameter deep learning algorithm. The autofluorescence signals of NAD(P)H in cervical exfoliated cells of participants ( n = 71) were detected by FLIM. Two key metabolic parameters—mean fluorescence lifetime ( t m ) and protein‐bound fraction ( a 2 )—were extracted using FLIM data, which reflect malignancy‐associated biochemical changes. Single‐parameter models based on individual parameters ( t m or a 2 ) and a multi‐parameter model integrating both parameters were constructed. The results showed that single‐parameter models suffered from sensitivity‐specificity imbalance while the multi‐parameter model leveraging feature complementarity significantly improved predictive performance—achieving 100% sensitivity and 92% specificity in external testing, with an AUC of 0.92, representing an improvement of 0.17–0.31 over single‐parameter models. These findings suggest that combining multi‐parameter deep learning strategies with FLIM holds strong potential for risk prediction of endometrial cancer, offering a new approach for noninvasive clinical screening.