Machine learning to guide clinical decision-making in abdominal surgery—a systematic literature review

Abstract

Purpose An indication for surgical therapy includes balancing benefits against risk, which remains a key task in all surgical disciplines. Decisions are oftentimes based on clinical experience while guidelines lack evidence-based background. Various medical fields capitalized the application of machine learning (ML), and preliminary research suggests promising implications in surgeons’ workflow. Hence, we evaluated ML’s contemporary and possible future role in clinical decision-making (CDM) focusing on abdominal surgery. Methods Using the PICO framework, relevant keywords and research questions were identified. Following the PRISMA guidelines, a systemic search strategy in the PubMed database was conducted. Results were filtered by distinct criteria and selected articles were manually full text reviewed. Results Literature review revealed 4,396 articles, of which 47 matched the search criteria. The mean number of patients included was 55,843. A total of eight distinct ML techniques were evaluated whereas AUROC was applied by most authors for comparing ML predictions vs. conventional CDM routines. Most authors (N = 30/47, 63.8%) stated ML’s superiority in the prediction of benefits and risks of surgery. The identification of highly relevant parameters to be integrated into algorithms allowing a more precise prognosis was emphasized as the main advantage of ML in CDM. Conclusions A potential value of ML for surgical decision-making was demonstrated in several scientific articles. However, the low number of publications with only few collaborative studies between surgeons and computer scientists underpins the early phase of this highly promising field. Interdisciplinary research initiatives combining existing clinical datasets and emerging techniques of data processing may likely improve CDM in abdominal surgery in the future.

Artificial intelligence, machine learning, and deep learning for clinical outcome prediction

“…In Supplementary Table S1 , we have complied recent review articles detailing emerging examples of how statistical and ML methods are being utilized for clinical outcome prediction in major medical specialities. Applications are found in the fields of Anesthesiology [ 32 , 33 , 34 ], Dermatology [ 35 , 36 , 37 ], Emergency Medicine [ 38 , 39 ], Family Medicine [ 40 , 40 ], Internal Medicine [ 41 , 42 , 43 ], Interventional Radiology [ 44 , 45 ], Medical Genetics [ 46 ], Neurological Surgery [ 47 ], Neurology [ 48 , 49 , 50 ], Obstetrics and Gynecology [ 51 , 52 ], Ophthalmology [ 53 , 54 , 55 ], Orthopaedic Surgery [ 56 ], Otorhinolaryngology [ 57 , 58 ], Pathology [ 59 , 60 , 61 ], Pediatrics [ 62 ], Physical Medicine and Rehabilitation [ 63 , 64 ], Plastic and Reconstructive Surgery [ 65 , 66 ], Psychiatry [ 67 , 68 ], Radiation Oncology [ 69 , 70 ], Radiology [ 71 , 72 ], General Surgery [ 73 , 74 ], Cardiothoracic Surgery [ 75 , 76 ], Urology [ 77 , 78 ], Vascular Surgery [ 79 , 80 ]. These papers introduce terms describing ML models as ‘supervised’ or ‘unsupervised’.…”

Gut Microbes Meet Machine Learning: The Next Step towards Advancing Our Understanding of the Gut Microbiome in Health and Disease

“…The use of omics-based methods has generated a large amount of data, which has prompted the development of computational methods, such as machine learning, to aid in processing and to analyze this data related to human gut microbiota research [ 10 , 11 , 12 ]. Machine learning comprises a series of powerful computational tools that have become increasingly important in various fields, including data analysis, computer vision, natural language processing, and predictive modeling [ 13 , 14 ]. Machine learning is a subfield of artificial intelligence that involves the development of algorithms that can learn from data and make predictions or decisions without being explicitly programmed [ 13 , 14 ].…”

“…Machine learning comprises a series of powerful computational tools that have become increasingly important in various fields, including data analysis, computer vision, natural language processing, and predictive modeling [ 13 , 14 ]. Machine learning is a subfield of artificial intelligence that involves the development of algorithms that can learn from data and make predictions or decisions without being explicitly programmed [ 13 , 14 ]. Machine learning algorithms can be divided into two main categories: supervised and unsupervised learning.…”

“…Supervised learning is the most common type of machine learning, and it involves training an algorithm on a labeled dataset to predict the outcome for new, unseen data. In supervised learning, the algorithm learns to identify patterns or relationships in the data that can be used to make predictions [ 13 , 14 ]. Examples of supervised learning algorithms include linear regression, decision trees, and support vector machines [ 13 , 14 ].…”

Artificial intelligence for decision support in surgical oncology - a systematic review

“…A scoping review described the use of ML in metabolic surgery [18] . Other reviews focused on the application of data science methods to surgery [9] and general hurdles for clinical translation [19] , or the use of ML in abdominal surgery [20] and surgical phase recognition [21] .…”