Improving precision of laparoscopic cholecystectomy: A review on the role of newer intraoperative imaging aids and artificial intelligence

Table 1 Technical process and typical use of common artificial intelligence architectures used in laparoscopic cholecystectomy

AI architecture	Technical process	Primary logic	Thinking style	Typical use
CNN	CV and DL	Spatial. Image analysis	What is the object?	Sees pixels and identify shapes-organ/tool detection/segmentation
Transformer	DL and video analytics	Temporal dependency. Sequence. Video analysis	What happens next?	Looks at the sequence of events over time-video analysis, workflow assessment and phase recognition
GNN	DL and Topological AI	Relational/topological	How are these connected?	Goes beyond seeing pixels to structure-CVS and anatomy mapping
Foundational	Generalist AI/MLLMs	Knowledge	I have seen this before	Generalizable feature extraction and cross-procedural understanding

CVS: Critical view of safety; CNN: Convolutional neural network; GNN: Graph neural network; CV: Computer vision; DL: Deep learning; MLLMs: Multimodal large language models; AI: Artificial intelligence.

Table 2 Detection rate of cystic duct and common bile duct across Nassar grades in low dose indocyanine green near infrared vs artificial intelligence integrated indocyanine green near infrared modes (the two studies are independent non comparative studies)

Anatomical structure	ICG NIR FC[52]		AI enabled ICG NIR FC[51]
	Pre-dissection detection percentage	Post-dissection detection percentage	Video enabled detection percentage (sensitivity)
Nassar grade 1	CBD 39, CD 0	CBD 100, CD 70	CBD 100, CD 94.12
Nassar grade 2			CBD 98.61, CD 87.50
Nassar grade 3	CBD 0, CD 0	CBD 100, CD 73	CBD 100, CD 92.50
Nassar grade 4			CBD 98.25, CD 60.61

CBD: Common bile duct; CD: Cystic duct; ICG NIR FC: Indocyanine green near infrared fluorescence cholangiography; AI: Artificial intelligence.

Table 3 Utility and limitations of various imaging aids, endo vision systems and artificial intelligence in detection of biliary anatomy and pathology in laparoscopic cholecystectomy

Mode	CBD detection rates	Benefits	Limitations
WLI	49.7%[17]	No additional training or equipment is needed	Low sensitivity in difficult grades
IOC	87% to 95%[10]	Good for biliary anatomy particularly aberrant ducts. Good visualization of entire biliary tree. Detection of simultaneous CBD stones. Simultaneous intervention possible for stones	Learning curve, need for CD cannulation, radiation exposure, visualization is not real time, not for vascular anatomy delineation, false positives, routine use advocated can increase time and cost, not repeatable
IOU	92%-100%[13]	Good for biliary anatomy particularly aberrant ducts. Detection of simultaneous CBD stones. Good for vascular anatomy delineation. No CD cannulation needed. No risk of radiation or dye allergies. Can be repeated easily	Steep learning curve. Operator dependent. Difficulty visualizing distal CBD. Inflammatory interference. Limited “Map” of injury
ICG NIR	96% vs 78%-86% in easy vs difficult cases[14,25,26]	Short learning curve, no radiation exposure, real time visualization, no CD cannulation, intraoperative navigation of safe dissection zones	Dye spillage from bile leak/GB perforation can impede visualization. Non-visualization-blocked CD/stones. Hepatic fluorescence interference. Penetration limited in fat/inflammation. Not useful for CBD stones. Small chance of dye allergy
YE and other spectral modes	No concrete data available presumed to be same as WLI	Improved vascular anatomy detection particularly cystic artery. Detection of cystic plate. Rouviere’s sulcus and other surface topography	Role still evolving, limited evidence only few case reports
AI in WLI	71% anatomy detection 92% CVS detection 92% accuracy for safe zones[44,46,47]	Moderate sensitivity for anatomy detection. High sensitivity for CVS detection	Needs validation in further studies. Requires AI enabled endo vision systems for real time mapping
AI and ICG integration	94.39% with YOLO. 82%-100% depending on difficulty grades[51]	High sensitivity in difficult grades. May be helpful for real time definition of safe dissection zones	Needs validation in further studies. Requires AI enabled endo vision systems for real time mapping

CBD: Common bile duct; CD: Cystic duct; GB: Gallbladder; CVS: Critical view of safety; WLI: White light imaging; IOC: Intraoperative cholangiography; IOU: Intraoperative ultrasound; ICG NIR: Indocyanine green near infrared fluorescence; YE: Yellow enhancement; AI: Artificial intelligence; YOLO: You only look once.