**6. AI as a means of improving quality of life assessment following bariatric metabolic surgery**

Quality of life (QoL) after BMS is a parameter with increasing interest in literature because it is perhaps more patient-related and less of a technicality, as compared with safety, effectiveness, and resolution of associated health problems. There are several scores for evaluating QoL after BMS and their applicability has been implemented after various procedures [89–92]. In the realm of AI, the group of Cao et al. has conducted two studies based on the SOReg with the use of CNN, Gaussian Bayesian Network (BN), and LR for predicting 5-year health-related QoL after BMS [72, 93]. GBN showed better predictive accuracy as compared with the other methods. In another publication, BN was implemented for a network meta-analysis of studies referring to QoL after BMS [94]. The analysis involved 26,629 patients in total and 11 different procedures.

*Current and Potential Applications of Artificial Intelligence in Metabolic Bariatric Surgery DOI: http://dx.doi.org/10.5772/intechopen.106365*

### **7. AI for evaluating intraoperative aspects of bariatric metabolic surgery**

One of the advantages of laparoscopic surgery (and video-assisted surgery in general) is the continuous recording of the procedure. In the digital era, these recordings can be transformed into captions, which subsequently may be stored, transferred, processed, etc. Another usage that has recently been highlighted is technical skill assessment. In comparison to crude measures of surgical performance, such as operative time, postoperative outcomes, and complications, video-assisted operative evaluation offers better opportunities for constructive feedback and progressive improvement of technique. The rating may be performed by human peers and supervisors, but lately ML has shown promising results in objective assessment of surgical skills [95].

In the field of BMS specifically, Twinanda et al. have pioneered AI techniques in laparoscopic videos with two discrete applications: retrieval of a specific fraction of the video (i.e., suturing of an anastomosis) and prediction of remaining time. In the former example, the researchers used Fisher kernel encoding, a precursor of deep learning techniques for managing large-scale object categorization, and applied it on 49 bypass and seven LSG videos [96]. In the latter case, remaining operative time in 170 RYGB videos was predicted by RSDNet, a DL-based algorithm that depends only on visual data for training rather than manual annotations [97]. Other pioneers in computer vision analysis of operative steps are Hashimoto et al., who implemented DNN to analyze LSG videos [98]. In this case, laparoscopic videos were segmented into seven steps: port placement, liver retraction, liver biopsy, gastrocolic ligament dissection, stapling of the stomach along the greater curvature, bagging specimen, and final inspection of the staple line. AI could extract quantitative data from video with an accuracy of >85%, a feature that allows quantification of operative capacity and objective evaluation for the purposes of both training and self-development. Similarly, Derathé et al. utilized annotated spatial and procedural data and processed them with SVM in order to predict surgical exposure [99].

In a totally different approach, Heremans et al. implemented ANN-based automated detection of food intake after neuromodulation by analyzing heart rate variability in electrocardiograms [100]. This is another example of intraoperative application of AI in a different kind of surgery for weight loss (neuromodulation).

### **8. Cost analysis of bariatric metabolic surgery with the use of AI**

We are living in an era that cost-effectiveness is paramount in medicine for every intervention, either conservative or surgical. It has been estimated that the cost of BMS is approximately 14,000 euros and 3600 euros annually ever after. In comparison, the cost for the non-surgical treatment of T2DM is about 12,200 euros per annum [101].

Cost analyses are considered dynamic systems that are affected by various, often non-predicable parameters. Many cost analyses studies are based on Markov models. Markov models are stochastic models designed for systems that change over time (i.e., dynamic ones) and change their parameters randomly. Using decision analysis with the implementation of a Markov process, Borisenko et al. calculated that the annual savings for a cohort of bariatric patients from the SOReg was 66 million euros, whereas over a lifetime bariatric surgery produced savings of 9332 euros [102, 103].

Similarly, Faria et al. compared different bariatric interventions and calculated that RYGB saves an average of 13,244 euros per patient as compared with best medical management [104].
