Convolutional neural network deep learning model accurately detects rectal cancer in endoanal ultrasounds.

BACKGROUND: Imaging is vital for assessing rectal cancer, with endoanal ultrasound (EAUS) being highly accurate in large tertiary medical centers. However, EAUS accuracy drops outside such settings, possibly due to varied examiner experience and fewer examinations. This underscores the need for an AI-based system to enhance accuracy in non-specialized centers. This study aimed to develop and validate deep learning (DL) models to differentiate rectal cancer in standard EAUS images. METHODS: A transfer learning approach with fine-tuned DL architectures was employed, utilizing a dataset of 294 images. The performance of DL models was assessed through a tenfold cross-validation. RESULTS: The DL diagnostics model exhibited a sensitivity and accuracy of 0.78 each. In the identification phase, the automatic diagnostic platform achieved an area under the curve performance of 0.85 for diagnosing rectal cancer. CONCLUSIONS: This research demonstrates the potential of DL models in enhancing rectal cancer detection during EAUS, especially in settings with lower examiner experience. The achieved sensitivity and accuracy suggest the viability of incorporating AI support for improved diagnostic outcomes in non-specialized medical centers.

Performance of spectral amplitude coded multiple pulse position modulation with non-uniform energy slots and fading.

Multiple-pulse position modulation (MPPM) is an advanced modulation method for optical wireless communication (OWC), which could provide better performance when channel information is not available. We consider the evaluation of a symbol error rate (SER) expression for MPPM applied within OWC. In the proposed model, MPPM is realized by spectral-amplitude coding (SAC) with non-uniform spectral slot energies. The derived fading channel SER expression is applicable for an arbitrary fading distribution, which may be due to either atmospheric turbulence or transceiver pointing error. A detailed theoretical analysis of the SER is provided and the resulting SER expression is verified by simulation.