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Page 4 of 6 Poulos et al. Mini-invasive Surg. 2025;9:6 https://dx.doi.org/10.20517/2574-1225.2024.42

static images extracted from 20 operative recordings. In each image, the recurrent laryngeal nerve was
labeled by expert thoracic surgeons. Using the Dice coefficient to assess performance, the AI model
outperformed general surgeons in identifying the recurrent laryngeal nerve and was only slightly less
[23]
accurate than expert thoracic surgeons (Dice coefficient 0.58 vs. 0.62) . The previous studies highlight real-
time, autonomous intraoperative anatomical segmentation as a promising application for AI systems. While
further research is undoubtedly necessary, existing literature suggests a strong potential for the expansion of
AI and deep learning applications in the realm of robotic thoracic surgery.

AI has been utilized to minimize the steep learning curve of robotic surgery and bridge the gap between
inexperienced and experienced surgeons. One study, which used Temporal Convolutional Networks for the
[24]
Operating room (TeCNO), sought to develop an AI-based phase recognition system for RAMIE . Video
was incorporated from 31 RAMIE procedures and κ-fold cross-validation to train their model to recognize
nine pre-determined surgical phases. By analyzing intraoperative recordings, their model was able to
identify RAMIE phases with 84% accuracy. For trainees, automatic phase recognition provides objective
data about surgical timing and efficiency for streamlined review and teaching. Intraoperatively, this
information can be used to alert support staff to an operation’s current goals or needs. Automatic phase
recognition is likely the first step in making truly autonomous platforms and sets the foundation for
innovation and future robotic applications.

Postoperatively, there are multiple ML models that have been developed to predict patients at risk for
complications following esophagectomy. ML algorithms can predict anastomotic leak rates with high
sensitivity based on various patient characteristics with an area-under-the-receiver-operator curve (AUC) of
0.72-0.87 [25,26] . Another model based on over 2,000 esophagectomy patients could predict early readmission
[27]
with AUC of 0.72-0.74 . In a study of 864 patients with distal esophageal adenocarcinoma undergoing
Ivor-Lewis Esophagectomy, an ANN was developed to predict clinically significant complications based on
Clavien-Dindo classification. Based on 96 variables encompassing all phases of care, this model could
predict Clavien-Dindo IIIa and above complications with an AUC of 0.67. It was also capable of
[28]
discriminating between medical and surgical complications with AUCs of 0.70 and 0.66, respectively .
CONCLUSION
AI has emerged as a compelling tool for endoscopists, medical oncologists, and thoracic surgeons in the
management of premalignant esophageal conditions and esophageal malignancy. While still in its infancy, it
is clear that it will play an important role in assisting surgeons to complete RAMIEs safely and efficiently.
Additionally, the multidisciplinary nature of esophageal malignancy offers multiple avenues for AI and
ANN implementation including computer-aided detection on screening/surveillance endoscopy, predicting
rates of progression of premalignant esophageal lesions or forecasting responses to medical-oncologic
interventions. AI will be an important adjunct in optimizing patient outcomes by implementing predictive
algorithms regarding preoperative and postoperative care. In concert with ongoing developments in robotic
surgical platforms, current ML systems have set the foundation for future surgical innovation, which will
continue to shape the field of thoracic surgery.

DECLARATIONS
Acknowledgments
The authors would like to thank the Departments of Surgery and Gastroenterology at Tufts Medical Center.