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Page 4 Langan et al. Art Int Surg 2023;3:140-6 https://dx.doi.org/10.20517/ais.2023.13

progression in the pancreatic remnant following segmental resection of noninvasive or microinvasive
[15]
IPMN . Progression was defined as the development of cancer, a new IPMN cystic lesion over 10 mm or
more than a 50% increase in the diameter of residual IPMN lesions. Out of 319 patients analyzed, there was
a 22% progression over time which included the formation of invasive cancer in 16%. The cumulative
incidence of progression was 10% at two years and 26% at five years. IPMN patients, therefore, represent a
[15]
high-risk group and should undergo long-term radiologic surveillance .
Due to the data presented above, mucinous cysts of the pancreas are pre-malignant and require appropriate
identification and longitudinal surveillance. However, this disease remains one with a paucity of knowledge
within the medical community. Nationally, innumerable patients each year develop PDAC in the setting of
pancreatic cystic disease due to a lack of identification and evidence-based follow-up. The challenges with
caring for pancreatic cyst patients start at the initial scan. Given that the majority of pancreatic cysts are
incidentally identified, most patients are never referred for further surveillance. Moreover, even in the select
patients who are referred, demographic and clinical data is manually entered into Excel spreadsheets which
lack patient demographics and clinical correlates; patient compliance for the return for screening can be
low; the clinical data are fragmented and an individual office lacks the ability to submit this data to a
national registry. Despite 2.2% of upper abdominal CTs and 19.6% of MRI exams reporting pancreatic cysts,
only 30% of these incidental patients receive follow-up care. In summary, patients with incidental pancreatic
cysts are not identified, identified patients are not referred, referred patients are manually tracked, and
patient data are not entered into a database repository to ask quality and research questions on a population
level.

Artificial intelligence and computational linguistics models
Artificial intelligence has entered many arenas of healthcare, and more recently, its role has become more
formalized in the pancreas space . Currently, AI has the ability to aid in the detection and diagnosis of pre-
[16]
malignant or malignant processes within the pancreas. Due to the anatomic location of the pancreas and the
stomach, Endoscopic Ultrasound (EUS) has become paramount in pancreatic cyst risk stratification. Not
only are the images clinically relevant for the care team, but pancreatic cyst fluid can also be aspirated and
diagnostically evaluated. To date, numerous studies have found that deep learning and convoluted neural
network AI models can differentiate normal pancreas from autoimmune pancreatitis, chronic pancreatitis,
and pancreatic ductal cancer [16-20] .

With respect to imaging detection, Vilas-Boas et al. recently published a pilot study assessing deep learning
for automatic differentiation between mucinous versus non-mucinous pancreatic cysts . Utilizing EUS
[21]
videos from 28 patients, this group assess 5,505 images (3,725 from mucinous lesions and 1780 from non-
mucinous lesions). Utilizing a convoluted neural network of deep learning, they found an overall accuracy
of 98.5%, sensitivity of 98.3%, and specificity of 98.9% . Although this was only a pilot study and
[21]
hypothesis-generating, AI may play a significant role in future risk stratifying pre-malignancy.

Regarding AI in imaging, Eon (https://eonhealth.com-Denver CO) has designed a highly flexible software
system called Eon Patient Management or EPM, utilizing computational linguistics models and a codebook
specific to pancreatic imaging. The EPM system integrates with the electronic health record and facilitates
patient identification, risk assessment, care plan setting, care plan tracking, patient and provider
communication, outcome recording, and registry functionality. The EPM system covers incidentally and
screening-detected findings across a broad array of organ systems. Using computational linguistics (CL)
data models, the EPM pancreas solution can accurately identify and capture pancreatic abnormalities from
radiology reports with 93.9% accuracy on multiple radiology modalities including CT, MRI and US. Also,