| Autor: |
Liu HC; Section of Thoracic Surgery, Mackay Memorial Hospital, Taipei City 10449, Taiwan.; Intensive Care Unit, Mackay Memorial Hospital, Taipei City 10449, Taiwan.; Department of Medicine, Mackay Medical College, New Taipei City 25245, Taiwan.; Department of Optometry, Mackay Junior College of Medicine, Nursing, and Management, Taipei City 11260, Taiwan., Lin MH; Biomedical Optical Imaging Lab, Department of Photonics, College of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu City 30010, Taiwan., Chang WC; Department of Pathology, Mackay Memorial Hospital, New Taipei City 25160, Taiwan.; Department of Pathology, Taipei Medical University Hospital, Taipei City 11030, Taiwan.; Department of Pathology, School of Medicine, College of Medicine, Taipei Medical University, Taipei City 11030, Taiwan., Zeng RC; Biomedical Optical Imaging Lab, Department of Photonics, College of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu City 30010, Taiwan., Wang YM; Biomedical Optical Imaging Lab, Department of Photonics, College of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu City 30010, Taiwan., Sun CW; Biomedical Optical Imaging Lab, Department of Photonics, College of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu City 30010, Taiwan.; Institute of Biomedical Engineering, College of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu City 30010, Taiwan.; Medical Device Innovation and Translation Center, National Yang Ming Chiao Tung University, Taipei City 11259, Taiwan. |
| Abstrakt: |
The determination of resection extent traditionally relies on the microscopic invasiveness of frozen sections (FSs) and is crucial for surgery of early lung cancer with preoperatively unknown histology. While previous research has shown the value of optical coherence tomography (OCT) for instant lung cancer diagnosis, tumor grading through OCT remains challenging. Therefore, this study proposes an interactive human-machine interface (HMI) that integrates a mobile OCT system, deep learning algorithms, and attention mechanisms. The system is designed to mark the lesion's location on the image smartly and perform tumor grading in real time, potentially facilitating clinical decision making. Twelve patients with a preoperatively unknown tumor but a final diagnosis of adenocarcinoma underwent thoracoscopic resection, and the artificial intelligence (AI)-designed system mentioned above was used to measure fresh specimens. Results were compared to FSs benchmarked on permanent pathologic reports. Current results show better differentiating power among minimally invasive adenocarcinoma (MIA), invasive adenocarcinoma (IA), and normal tissue, with an overall accuracy of 84.9%, compared to 20% for FSs. Additionally, the sensitivity and specificity, the sensitivity and specificity were 89% and 82.7% for MIA and 94% and 80.6% for IA, respectively. The results suggest that this AI system can potentially produce rapid and efficient diagnoses and ultimately improve patient outcomes. |
