The usefulness of artificial intelligence in breast reconstruction: a systematic review.

BACKGROUND: Artificial Intelligence (AI) offers an approach to predictive modeling. The model learns to determine specific patterns of undesirable outcomes in a dataset. Therefore, a decision-making algorithm can be built based on these patterns to prevent negative results. This systematic review aimed to evaluate the usefulness of AI in breast reconstruction. METHODS: A systematic review was conducted in August 2022 following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. MEDLINE, EMBASE, SCOPUS, and Google Scholar online databases were queried to capture all publications studying the use of artificial intelligence in breast reconstruction. RESULTS: A total of 23 studies were full text-screened after removing duplicates, and twelve articles fulfilled our inclusion criteria. The Machine Learning algorithms applied for neuropathic pain, lymphedema diagnosis, microvascular abdominal flap failure, donor site complications associated to muscle sparing Transverse Rectus Abdominis flap, surgical complications, financial toxicity, and patient-reported outcomes after breast surgery demonstrated that AI is a helpful tool to accurately predict patient results. In addition, one study used Computer Vision technology to assist in Deep Inferior Epigastric Perforator Artery detection for flap design, considerably reducing the preoperative time compared to manual identification. CONCLUSIONS: In breast reconstruction, AI can help the surgeon by optimizing the perioperative patients' counseling to predict negative outcomes, allowing execution of timely interventions and reducing the postoperative burden, which leads to obtaining the most successful results and improving patient satisfaction.

Predicting Cardiopulmonary Arrest with Digital Biomarkers: A Systematic Review.

(1) Background: Telemetry units allow the continuous monitoring of vital signs and ECG of patients. Such physiological indicators work as the digital signatures and biomarkers of disease that can aid in detecting abnormalities that appear before cardiac arrests (CAs). This review aims to identify the vital sign abnormalities measured by telemetry systems that most accurately predict CAs. (2) Methods: We conducted a systematic review using PubMed, Embase, Web of Science, and MEDLINE to search studies evaluating telemetry-detected vital signs that preceded in-hospital CAs (IHCAs). (3) Results and Discussion: Out of 45 studies, 9 met the eligibility criteria. Seven studies were case series, and 2 were case controls. Four studies evaluated ECG parameters, and 5 evaluated other physiological indicators such as blood pressure, heart rate, respiratory rate, oxygen saturation, and temperature. Vital sign changes were highly frequent among participants and reached statistical significance compared to control subjects. There was no single vital sign change pattern found in all patients. ECG alarm thresholds may be adjustable to reduce alarm fatigue. Our review was limited by the significant dissimilarities of the studies on methodology and objectives. (4) Conclusions: Evidence confirms that changes in vital signs have the potential for predicting IHCAs. There is no consensus on how to best analyze these digital biomarkers. More rigorous and larger-scale prospective studies are needed to determine the predictive value of telemetry-detected vital signs for IHCAs.

A Review of Voice-Based Pain Detection in Adults Using Artificial Intelligence.

Pain is a complex and subjective experience, and traditional methods of pain assessment can be limited by factors such as self-report bias and observer variability. Voice is frequently used to evaluate pain, occasionally in conjunction with other behaviors such as facial gestures. Compared to facial emotions, there is less available evidence linking pain with voice. This literature review synthesizes the current state of research on the use of voice recognition and voice analysis for pain detection in adults, with a specific focus on the role of artificial intelligence (AI) and machine learning (ML) techniques. We describe the previous works on pain recognition using voice and highlight the different approaches to voice as a tool for pain detection, such as a human effect or biosignal. Overall, studies have shown that AI-based voice analysis can be an effective tool for pain detection in adult patients with various types of pain, including chronic and acute pain. We highlight the high accuracy of the ML-based approaches used in studies and their limitations in terms of generalizability due to factors such as the nature of the pain and patient population characteristics. However, there are still potential challenges, such as the need for large datasets and the risk of bias in training models, which warrant further research.

Diagnostic implications of neuroimaging in epilepsy and other seizure disorders.

Epilepsy is the most common neurological disorder that affects ~1-2% of the global population, leading to presentation in the emergency room. The neuroimaging modalities have an important application in diagnosing new onset unprovoked seizures and epilepsy. This article discusses the various neuroimaging modalities for diagnosing seizures and epilepsy and addresses that the MRI is the investigation of choice, and urgent imaging is more commonly done by computed tomography in patients with new-onset seizures. The goal of the article was to diagnose seizures and epilepsy for early intervention to prevent complications or damage to the brain. MRI detects even small cortical epileptogenic lesions, whereas computed tomography is used in screening, diagnosis, evaluation, and monitoring of the prognosis of seizures in children. Magnetic resonance spectroscopy provides biochemical measurements of reduced N-acetyl aspartate and increased creatinine and choline in dysfunctioning epileptic zones. Volumetric MRI is very sensitive and specific in determining seizures originating in extratemporal and extrahippocampal sites. Even though diffusion tensor magnetic resonance imaging has a limited role, it is used in specific pediatric patient groups with temporal lobe epilepsy. Functional radionuclide imaging modalities (positron emission tomography and single-photon emission computerized tomography) are increasingly significant for the identification of the epileptic region. Furthermore, the authors recommend the use of artificial intelligence and further research on imaging modalities for early diagnosis of seizures and epilepsy.