Implications of artifical intelligence in Anaesthesia Perioperative Monitoring

In the medical profession, artificial intelligence (AI) and machine learning (ML) are quickly gaining acceptance. These technologies aid in improving decision-making, workflow patterns, event prediction, and operating room logistical optimisation. A recently published narrative review gives an analysis of artificial intelligence (AI) in perioperative monitoring during anaesthesia. AI may assist anesthesiologists in handling a variety of concurrent duties, including pain management, post-anesthesia care unit, medication distribution, perioperative monitoring, and intensive care unit care. ML is an evolutionary branch of AI that solves problems using a variety of learning algorithms, including reinforcement learning, supervised learning, and unsupervised learning.

Three ML algorithms—fuzzy logic, classical ML, neural networks, deep learning, and Bayesian methods—are used by AI to combine different models. While conventional machine learning (ML) employs data attributes to create algorithms for intricate data analysis, fuzzy logic expands upon rule-based systems. Popular techniques include neural networks and deep learning, where the latter generates more meaningful results by learning from a variety of datasets.

The goal of natural language processing is to enable the creation of organised databases and the retrieval of data from free text fields by using computers to interpret human language. Automating the collection, processing, and analysis of visual inputs, including ultrasound pictures, identification during regional blocks for anaesthesia and pain relief, and diagnostic procedures, is possible with computer vision.

In conclusion, by enhancing workflow patterns, decision-making, event prediction, and operating room logistics, AI and ML are completely changing the medical industry.

In anaesthesia, artificial intelligence (AI) may improve judgement and solve clinical problems more quickly. AI is divided into three categories: depth of anaesthesia monitoring, pharmaceutical and mechanical robotic applications, and CDSS. Through the analysis of patient data and procedural knowledge, CDSS plays a critical role in perioperative monitoring by determining the appropriate dosage of anaesthesia medicines and hydration management. Additionally, it keeps patients from declining after anaesthesia and aids with perioperative care such as managing analgesia.

The McSleepy model, which employs response algorithms to maintain the level of anaesthesia around a specified point, was the first automated anaesthesia system developed in 1950. Artificial intelligence (AI) is used in pharmacological robotics, where systems provide patient-specific anaesthesia and interact with alarms and suggestions based on perioperative monitoring data.

AI algorithms are able to be "trained" to carry out tasks and provide the required results by learning from a vast array of inputs. Both knowledge-based and non-knowledge-based CDSS may help with blood pressure monitoring, low-flow anaesthesia, measuring intraoperative blood loss, monitoring postoperative nausea and vomiting, and determining breathing settings. Additionally, it may assist lower pharmaceutical mistakes and adverse events by providing automated suggestions on a range of topics, including the administration of anaesthetic agents, analgesic dose, and hydration management.

The arduous nature of perioperative monitoring poses a significant risk to patient safety, since anesthesiologists may get alarm fatigued during the delivery of anaesthesia. Artificial intelligence (AI)-powered cognitive robots may be included into alarm systems to evaluate many criteria at once, reducing the likelihood of false alarms and operator fatigue. The foundation of AI applications in anaesthesia practice is the monitoring of anaesthesia depth.

Since the brain is the principal organ that anaesthesia targets, an EEG might be used to track the anesthetic's effects. AI deep learning models may help to do away with the necessity for clinical studies including hypnosis-level monitoring. Since the primary organ addressed by anaesthesia is the central nervous system, non-invasive methods such as electroencephalography (EEG) may be useful for investigating brain activity.

An additional monitoring technique that may be used to assess a patient's level of anaesthesia is the mid-latency auditory evoked potential. Research has shown that rather than learning the characteristics recommended by physicians, neural networks are able to auto-update with the features present in the dataset and use the primary qualities that predict the endpoint (e.g., awareness).

AI has also been used to automate the weaning process from mechanical ventilation, among other mechanical ventilation management functions. During general anaesthesia, artificial neural networks (ANN) have been attempted to predict when a neuromuscular block would recover. Lin et al. found that ANNs with accessible data could identify intricate patterns in spinal anaesthesia with a 75.9% sensitivity and a 76% specificity.

In conclusion, artificial intelligence (AI) has shown to be significantly safer, more advanced, and less unpredictable than human decision-making in a wide range of domains, including medicine. Adopting AI in perioperative care requires careful thought since making the wrong inferences might have severe consequences.

Reference –

Garg, Shaloo; Kapoor, Mukul Chandra. Role of artificial intelligence in perioperative monitoring in anaesthesia. Indian Journal of Anaesthesia 68(1):p 87-92, January 2024. | DOI: 10.4103/ija.ija_1198_23