This review applied the suggestions of the PRISMA methodology for scoping studies (8). It contains 20 mandatory items and two voluntary variables the researcher conducting a scoping analysis must integrate into their manuscript. The eligibility criteria were: 1) Relevant articles published between 2016 and May 2022. Such papers were beneficial since they had a high chance of containing information pertinent to the research topic. 2) Articles published in English.

Choosing the appropriate files for the study required a comprehensive literature search. We searched various databases (PubMed, Cochrane, and IEEE) and search engines namely Google Scholar for articles published from 2016 to the current date. The literature search occured on May 8, 2022. Our team purified the search methods integrated through a group discussion. Furthermore, the authors scanned the references found in various articles to acquire additional documents for the study.

The search terms were “machine learning,” and “machine learning algorithms in bariatric surgery.” Also, we used phrases like “the development of machine learning in bariatric surgery,” “ML application in bariatric surgery,” and “ML in weight-loss surgery during the search process. On Pubmed, the applied filters were “best match” and “five years,” while on Cochrane and Google Scholar, the applied filters were “2016 to 2022”. All the researchers were involved in drafting the manuscript peer-reviewed the search plan by applying the PRESS checklist. This checklist facilitated the thorough evaluation of the consistency demonstrated during the process. The translation of the study questions ensured that the articles gathered were relevant to the study (see Figure 1).

We generated a data logging tool highlighting the various features to consider when collecting data. The researchers examined contextual factors such as the type of surgery addressed by the articles. Also, we evaluated the topic of the publications to ensure that they were consistent with the research questions. The recency of articles guaranteed the collection of accurate information.

The study spearheaded by Cao et al. revealed that ensemble ML algorithms demonstrated a better performance than base algorithms in predicting postoperative complications among patients who have undergone bariatric surgery (9). Johnston et al. tested the predictive ability of ML in bariatric surgery forecasting successful type 2 diabetes treatment. The results indicated that the model was successful in predicting and assisting patient assortment (10). Assaf et al. argued that ML model applications improved pre-bariatric surgery diagnosis of hiatal hernia using contrast swallow studies and improved diagnostic sensitivity by 1.5 times baseline (11). Chiong et al. developed a support vector machine (SVM) model and that predicted the level of body fat percentage among patients with higher accuracy than other model compared (12). Nudel et al., developed an artificial neural network (ANN) capable of predicting postoperative gastrointestinal leakage better than traditional regression models (13). Razzaghi et al. posited that ensemble ML algorithms when applied with synthetic minority oversampling technique (SMOTE) have the greatest accuracy when predicting postoperative outcomes in bariatric surgery (14). Thomas et al. showed that four out of eight neural networks successfully predicted which patients had successful weight loss postoperatively after one year (15). Cao et al. argued that applying three unsupervised deep-learning neural networks led to improved but limited outcomes when predicting the occurrence of severe postoperative complications (16). The convolutional neural network algorithm also successfully predicted long-term health-related life quality after bariatric surgery (17).

Modaresnezhad et al., presented a semantic data integration, standardization and dimensionality reduction method that allowed for fast and efficient application of data mining techniques to large clinical datasets (18). Zhang et al. developed the Siamese-KNN ML model capable of predicting eventual weight loss six months after bariatric surgery, scoring close to 84% on accuracy (19). Torquati et al. posited that the Super learner ML algorithm outperformed traditional approaches like logistic regression in predicting thirty-day readmission risk after bariatric operations (20). Cao et al. revealed that Bayesian networks were appropriate tools for predicting long-term health-related life quality and comorbidities after bariatric surgery (21).

A different study spearheaded by Sheikhtaheri et al. employed a Clinical Decision Support system comprising MLP networks and predicted complications within ten days, one month, and three months after bariatric surgery with good accuracy and sensitivity (22). Weerakoon et al., argued that ML application in bariatric surgery could assist in weight prediction by tracking the pre-and post-surgery weight of patients with high accuracy (23). Moreover, Dimeglio et al. claimed that applying ML algorithms in bariatric surgery permitted the accurate categorization of individuals and predicted postoperative weight gain potential among patients (24). Sheidaei et al. indicated that the decision tree ML algorithm performed efficiently in predicting the various types of bariatric surgery based on the information patients present during their first physical exam (25).

One of the best ways to achieve significant, persistent weight loss, improved glycemic control, and in many cases, remission of type 2 diabetes (DMII) is through bariatric surgery (26). Patients' ability to attain these results may be impacted by variables such as age, sex, medications, comorbidities, type of procedure, and prior weight-loss procedures (10). Johnston's Patient Prediction Model was able to allocate patients a probability of cessation of antihyperglycemic medications after bariatric surgery based on the preoperative factors above with high internal and external validity (10). To date, it has been difficult to use standard regression to predict long-term success in bariatric surgery patients (24). However, Weerakoon's model was able to predict the final weight of patients with 85% accuracy and monthly weight changes with 75% accuracy (23). Thomas et al. showed that using only pre-operative demographic, anthropometric, and comorbidity information, their neural networks determined which patients will have successful weight loss over a year postoperatively with 78% accuracy (15). These models can inform the clinicians on the selection of patients and allow clinicians to set more accurate expectations for the patients (10). Additionally, monthly weight prediction can allow for more consistent follow-up with patients, which is one of the main predictors of persistent weight loss (23, 27). DiMeglio's model was able to predict weight loss trajectories in a subset of patients with very high accuracy (24). If used during follow-up visits, this model can allow for the early identification of suboptimal weight trajectory and can allow for early and improved second-line physical, psychological, and nutritional management (24).

These models however are limited by their inability to predict multiple outcomes (10). This would mean the need for multiple models to provide a complete clinical application. An easily accessible application incorporating several of these ML models would be required (15). Another limiting factor is the difficulty encountered incorporating data from large databases due to logistical complexities and lack of standardization of heterogenous patient information (28, 29). To tackle these issues, Modersnezhad et al., developed the RxSem model, which is a system that integrates, standardizes, and mines data in medical databases by utilizing semantic networks for reducing data dimensionality and thus, making predictive analytics using large datasets feasible and efficient (18). Another limitation that must be addressed is how clinicians would incorporate the statistical predictivity of ML into their decision-making process. What would the threshold of predicted probability be at which a patient would be selected to undergo a procedure as opposed to below which they would not? Preference studies and benefit-risk analysis when paired with ML may provide a useful answer to this question (10). From an economic standpoint, combining ML and cost-benefit analysis can produce a “target efficiency” threshold at which limiting the intervention to those who meet the threshold will produce the greatest expected economic net benefit (10, 30, 31). A payer who wants to allocate limited resources while promoting economic efficiency could find this strategy helpful.

Although bariatric surgery has lower mortality than other elective surgical procedures, its complications can be costly and severe (13, 32, 33). The stratification of postoperative complication risk can aid in patient selection, referral strategies, and patient counseling (13). It may also help detect high-risk patients for follow-up and management (13). Although traditional linear regression models can provide rather straightforward and understandable inferences, they have not yet been proven to be accurate and cannot thus be employed in clinical practice (9, 34, 35). Torquati et al., compared their “Super learner” algorithm outperformed traditional statistical models and demonstrated a higher AUC and sensitivity at predicting 30-day readmission risk postoperatively. With a large sample size of 393,833 patients, they showed that ML may be used to create tools that could aid clinicians to create targeted strategies that could minimize unnecessary readmission (20). Several other ML models were developed to predict postoperative outcomes, but which provided the most accurate results? Razzhagi et al., and Cao et al., were interested in answering this question. They tested several algorithms and ML models. They came to a similar conclusion that the ensemble algorithm when equipped with several classifiers and SMOTE provided the highest accuracy (9, 14). It is important to note that accuracy was high using this model but the sensitivity was low (9). Accuracy in and of itself is a function of incidence. If the incidence of postoperative complications is low, then accuracy will be high by default, and thus sensitivity is of more importance in rare outcomes (9). Further development and research are required before ML models can be applied to larger populations. Similarly, Nudel et al. developed an artificial neural network (ANN) that outperformed traditional regression models in predicting postoperative gastrointestinal leaks and similarly had high AUC and specificity, but its sensitivity was low (13). On the other hand, Sheikhtaheri et al., showed that their ANN when equipped with SMOTE can predict early complications of gastric bypass surgery with high sensitivity, and accuracy (22). Although their 89% sensitivity was high, it is important to note that they just considered total and not individual complications (22). This is due to low sample size and limited complications in their database (22). The rare nature of the complications and the issue of noncompliance with follow-up adds to the difficulty of creating informative models (36). It is important to note that from the studies above, only Cao and Torquati (9, 20) externally validated their results (9, 13, 14, 20, 22). This means that these models cannot yet be applied clinically until further research and external validation is demonstrated.

Cao et al., would later develop 2 deep level neural network models (DLNN) equipped with SMOTE capable of predicting outcomes with an AUC of 0.85 in the training dataset, however, it failed to predict them in the testing dataset with AUCs barely higher than a random guess at 0.57 (16). This would indicate that DLNNs are still far from being clinically applicable in everyday practice (16). The authors stated that the main benefit of DLNNs is they attempt to incrementally learn high-level characteristics from data (16). Hard-core feature extraction requires less human domain expertise, in contrast to classic ML techniques, and reduce the complexity of the data making patterns easier to see (16, 37, 38).

The HRQoL is a broad, multifaceted term that encompasses important everyday functioning and subjective experience categories such as somatic sensation, physical functioning, social role functioning, and subjective well-being (39, 40). Cao et al. demonstrated that the DLNN “convolution neural network” (CNN) model showed an overwhelming advantage in predicting all the HRQoL measures when compared to multivariate linear regression models (MLR) in the context of postoperative bariatric surgery (17). These findings may be very helpful to patients' postoperative care and rehabilitation (17). Cao et al., later compared the use of Bayesian networks (BN) to their previous CNN model and found that the Gaussian BN outperformed both CNN and MRL in predictive accuracy (21). The authors assert that the BN model deserves future investigation in the future (21).

Machine learning models in radiological applications have been successfully used in the diagnosis and management of several medical fields related to the brain, breast, lung, and thyroid (41–44). In bariatric surgery, Assaf et al. utilized ML algorithms to increase the sensitivity of preoperative contrast swallow studies when evaluating patients for the presence of hiatal hernias (11). This ability can enhance conventional medical diagnosis and could reduce the number of patients needing hiatal exploration during bariatric surgery (11). Zhang et al., used functional magnetic resonance imaging (fMRI) with baseline whole-brain resting-state functional connectivity (RSFC) to develop a multivariate prediction framework “K-nearest neighbor (KNN)” (19). The Siamese-KNN achieved an accuracy of 83.78% and showed that neuroimaging biomarkers can be used to predict individual weight loss post-surgery and assist in personalized diagnosis for treatment of obesity (19). These applications are still new and require further investigation and large prospective research to confirm their findings.

Multiple research gaps remain evident in this review. For instance, there is a scarcity of publications addressing the integration of ML algorithms in bariatric surgery. The potential cause for this situation is the newness of the concept. Moreover, there is a lack of articles addressing the challenges of ML integration in bariatric surgery. Hence, it becomes challenging to understand the various shortcomings that trigger the minimal incorporation of ML algorithms into bariatric surgeries. Handling the identified research gaps is necessary to ensure the availability of more supportive information.

The potential of ML algorithms to reduce surgical complications and improve patient care showcases that their development is inevitable and the benefits of ML in bariatric surgery will trigger its wide application and development. The improvement of ML integration in bariatric surgery mainly depends on the efforts of researchers to conduct more studies and highlight the algorithms that are most appropriate to apply in the sector. This would involve improving techniques that facilitate the extraction of more granular data from various medical records (13). An easily accessible application incorporating several of these ML models would also be required for everyday use (15). ML is still far from being clinically applicable (16), however, the future is certainly promising.