Commentary on: Artificial Intelligence in Surgical Evaluation: A Study of Facial Rejuvenation Techniques.
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ABSTRACT:
SUBMITTER: Aly A
PROVIDER: S-EPMC10371035 | biostudies-literature | 2023
REPOSITORIES: biostudies-literature
Publications
Commentary on: Artificial Intelligence in Surgical Evaluation: A Study of Facial Rejuvenation Techniques.
Aly Al A Wen Y Edward YE Steppe Cyrus C
Aesthetic surgery journal. Open forum 20230614
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Project description:BackgroundAlthough radical gastrectomy with lymph node dissection is the standard treatment for gastric cancer, the complication rate remains high. Thus, estimation of surgical complexity is required for safety. We aim to investigate the association between the surgical process and complexity, such as a risk of complications in robotic distal gastrectomy (RDG), to establish an artificial intelligence (AI)-based automated surgical phase recognition by analyzing robotic surgical videos, and to investigate the predictability of surgical complexity by AI.MethodThis study assessed clinical data and robotic surgical videos for 56 patients who underwent RDG for gastric cancer. We investigated (1) the relationship between surgical complexity and perioperative factors (patient characteristics, surgical process); (2) AI training for automated phase recognition and model performance was assessed by comparing predictions to the surgeon-annotated reference; (3) AI model predictability for surgical complexity was calculated by the area under the curve.ResultSurgical complexity score comprised extended total surgical duration, bleeding, and complications and was strongly associated with the intraoperative surgical process, especially in the beginning phases (area under the curve 0.913). We established an AI model that can recognize surgical phases from video with 87% accuracy; AI can determine intraoperative surgical complexity by calculating the duration of beginning phases from phases 1-3 (area under the curve 0.859).ConclusionSurgical complexity, as a surrogate of short-term outcomes, can be predicted by the surgical process, especially in the extended duration of beginning phases. Surgical complexity can also be evaluated with automation using our artificial intelligence-based model.
| S-EPMC9949687 | biostudies-literature
Project description:ImportanceSurgeons make complex, high-stakes decisions under time constraints and uncertainty, with significant effect on patient outcomes. This review describes the weaknesses of traditional clinical decision-support systems and proposes that artificial intelligence should be used to augment surgical decision-making.ObservationsSurgical decision-making is dominated by hypothetical-deductive reasoning, individual judgment, and heuristics. These factors can lead to bias, error, and preventable harm. Traditional predictive analytics and clinical decision-support systems are intended to augment surgical decision-making, but their clinical utility is compromised by time-consuming manual data management and suboptimal accuracy. These challenges can be overcome by automated artificial intelligence models fed by livestreaming electronic health record data with mobile device outputs. This approach would require data standardization, advances in model interpretability, careful implementation and monitoring, attention to ethical challenges involving algorithm bias and accountability for errors, and preservation of bedside assessment and human intuition in the decision-making process.Conclusions and relevanceIntegration of artificial intelligence with surgical decision-making has the potential to transform care by augmenting the decision to operate, informed consent process, identification and mitigation of modifiable risk factors, decisions regarding postoperative management, and shared decisions regarding resource use.
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Project description:Masseteric-facial anastomosis has gained popularity in recent days compared to the facial-hypoglossal anastomosis. Masseteric nerve has numerous advantages like its proximity to the facial nerve, stronger motor impulse, its reliability, low morbidity in harvesting and sacrificing the nerve and faster re-innervation that is achievable in most patients. The present case series demonstrate the surgical technique and the effectiveness of the masseteric nerve as donor for early facial reanimation. Between January 2017 and February 2019, 6 patients (2 male, 4 female) with iatrogenic unilateral complete facial paralysis (grade VI, House Brackmann scale) who underwent masseteric-facial nerve anastomosis were included in the study. The time interval between the onset of paralysis and surgery ranged from 4 to 18 months (mean 8.5 months). In all patients pre-operative electromyography had facial mimetic muscle fibrillation potentials. All patients underwent end to end anastomosis except for one patient where greater auricular interposition graft was used. In all cases, the facial muscles showed earliest sign of recovery at 2-5 months. These movements were first noticed on the cheek musculature when the patients activated their masseter muscle. Eye movements started appearing at 6-9 months (in 3 cases) and forehead movements at 18 months (in 1 case). According to the modified House-Brackmann grading scale, one patient had Grade I function, two patients had Grade II function, and three had Grade V function. There was no morbidity except one patient who underwent interposition graft had numbness in the ear lobule. None of the patients could feel the loss of masseteric nerve function. Masseteric facial nerve anastomosis is a versatile, powerful early facial dynamic reanimation tool with almost negligible morbidity compared to other neurotization procedures for patients with complete facial nerve paralysis.
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Project description:BackgroundArtificial intelligence (AI) methods and AI-enabled metrics hold tremendous potential to advance surgical education. Our objective was to generate consensus guidance on specific needs for AI methods and AI-enabled metrics for surgical education.Study designThe study included a systematic literature search, a virtual conference, and a 3-round Delphi survey of 40 representative multidisciplinary stakeholders with domain expertise selected through purposeful sampling. The accelerated Delphi process was completed within 10 days. The survey covered overall utility, anticipated future (10-year time horizon), and applications for surgical training, assessment, and feedback. Consensus was agreement among 80% or more respondents. We coded survey questions into 11 themes and descriptively analyzed the responses.ResultsThe respondents included surgeons (40%), engineers (15%), affiliates of industry (27.5%), professional societies (7.5%), regulatory agencies (7.5%), and a lawyer (2.5%). The survey included 155 questions; consensus was achieved on 136 (87.7%). The panel listed 6 deliverables each for AI-enhanced learning curve analytics and surgical skill assessment. For feedback, the panel identified 10 priority deliverables spanning 2-year (n = 2), 5-year (n = 4), and 10-year (n = 4) timeframes. Within 2 years, the panel expects development of methods to recognize anatomy in images of the surgical field and to provide surgeons with performance feedback immediately after an operation. The panel also identified 5 essential that should be included in operative performance reports for surgeons.ConclusionsThe Delphi panel consensus provides a specific, bold, and forward-looking roadmap for AI methods and AI-enabled metrics for surgical education.
| S-EPMC10634198 | biostudies-literature
Project description:AimTo perform a systematic review on the application of artificial intelligence (AI) based knowledge discovery techniques in pharmacoepidemiology.Study eligibility criteriaClinical trials, meta-analyses, narrative/systematic review, and observational studies using (or mentioning articles using) artificial intelligence techniques were eligible. Articles without a full text available in the English language were excluded.Data sourcesArticles recorded from 1950/01/01 to 2019/05/06 in Ovid MEDLINE were screened.ParticipantsStudies including humans (real or simulated) exposed to a drug.ResultsIn total, 72 original articles and 5 reviews were identified via Ovid MEDLINE. Twenty different knowledge discovery methods were identified, mainly from the area of machine learning (66/72; 91.7%). Classification/regression (44/72; 61.1%), classification/regression + model optimization (13/72; 18.0%), and classification/regression + features selection (12/72; 16.7%) were the three most frequent tasks in reviewed literature that machine learning methods has been applied to solve. The top three used techniques were artificial neural networks, random forest, and support vector machines models.ConclusionsThe use of knowledge discovery techniques of artificial intelligence techniques has increased exponentially over the years covering numerous sub-topics of pharmacoepidemiology.Systematic review registrationSystematic review registration number in PROSPERO: CRD42019136552.
| S-EPMC7378532 | biostudies-literature
Project description:The genome of the novel coronavirus (COVID-19) disease was first sequenced in January 2020, approximately a month after its emergence in Wuhan, capital of Hubei province, China. COVID-19 genome sequencing is critical to understanding the virus behavior, its origin, how fast it mutates, and for the development of drugs/vaccines and effective preventive strategies. This paper investigates the use of artificial intelligence techniques to learn interesting information from COVID-19 genome sequences. Sequential pattern mining (SPM) is first applied on a computer-understandable corpus of COVID-19 genome sequences to see if interesting hidden patterns can be found, which reveal frequent patterns of nucleotide bases and their relationships with each other. Second, sequence prediction models are applied to the corpus to evaluate if nucleotide base(s) can be predicted from previous ones. Third, for mutation analysis in genome sequences, an algorithm is designed to find the locations in the genome sequences where the nucleotide bases are changed and to calculate the mutation rate. Obtained results suggest that SPM and mutation analysis techniques can reveal interesting information and patterns in COVID-19 genome sequences to examine the evolution and variations in COVID-19 strains respectively.
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Project description:Advances in neuroimaging have permitted the non-invasive examination of the human brain in pain. However, a persisting challenge is in the objective differentiation of neuropathic facial pain subtypes, as diagnosis is based on patients' symptom descriptions. We use artificial intelligence (AI) models with neuroimaging data to distinguish subtypes of neuropathic facial pain and differentiate them from healthy controls. We conducted a retrospective analysis of diffusion tensor and T1-weighted imaging data using random forest and logistic regression AI models on 371 adults with trigeminal pain (265 classical trigeminal neuralgia (CTN), 106 trigeminal neuropathic pain (TNP)) and 108 healthy controls (HC). These models distinguished CTN from HC with up to 95% accuracy, and TNP from HC with up to 91% accuracy. Both classifiers identified gray and white matter-based predictive metrics (gray matter thickness, surface area, and volume; white matter diffusivity metrics) that significantly differed across groups. Classification of TNP and CTN did not show significant accuracy (51%) but highlighted two structures that differed between pain groups-the insula and orbitofrontal cortex. Our work demonstrates that AI models with brain imaging data alone can differentiate neuropathic facial pain subtypes from healthy data and identify regional structural indicates of pain.
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Project description:The world urgently needs new sources of clean energy due to a growing global population, rising energy use, and the effects of climate change. Nuclear energy is one of the most promising solutions for meeting the world's energy needs now and in the future. One type of nuclear energy, Low Energy Nuclear Reactions (LENR), has gained interest as a potential clean energy source. Recent AI advancements create new ways to help research LENR and to comprehensively analyze the relationships between experimental parameters, materials, and outcomes across diverse LENR research endeavors worldwide. This study explores and investigates the effectiveness of modern AI capabilities leveraging embedding models and topic modeling techniques, including Latent Dirichlet Allocation (LDA), BERTopic, and Top2Vec, in elucidating the underlying structure and prevalent themes within a large LENR research corpus. These methodologies offer unique perspectives on understanding relationships and trends within the LENR research landscape, thereby facilitating advancements in this crucial energy research area. Furthermore, the study presents LENRsim, an experimental machine learning tool to identify similar LENR studies, along with a user-friendly web interface for widespread adoption and utilization. The findings contribute to the understanding and progression of LENR research through data-driven analysis and tool development, enabling more informed decision-making and strategic planning for future research in this field. The insights derived from this study, along with the experimental tools we developed and deployed, hold the potential to significantly aid researchers in advancing their studies of LENR.
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Project description:Predictive models for determining coronavirus disease 2019 (COVID-19) severity have been established; however, the complexity of the interactions among factors limits the use of conventional statistical methods. This study aimed to establish a simple and accurate predictive model for COVID-19 severity using an explainable machine learning approach. A total of 3,301 patients ≥ 18 years diagnosed with COVID-19 between February 2020 and October 2022 were included. The discovery cohort comprised patients whose disease onset fell before October 1, 2020 (N = 1,023), and the validation cohort comprised the remaining patients (N = 2,278). Pointwise linear and logistic regression models were used to extract 41 features. Reinforcement learning was used to generate a simple model with high predictive accuracy. The primary evaluation was the area under the receiver operating characteristic curve (AUC). The predictive model achieved an AUC of ≥ 0.905 using four features: serum albumin levels, lactate dehydrogenase levels, age, and neutrophil count. The highest AUC value was 0.906 (sensitivity, 0.842; specificity, 0.811) in the discovery cohort and 0.861 (sensitivity, 0.804; specificity, 0.675) in the validation cohort. Simple and well-structured predictive models were established, which may aid in patient management and the selection of therapeutic interventions.
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Project description:Artificial intelligence has been advancing in fields including anesthesiology. This scoping review of the intersection of artificial intelligence and anesthesia research identified and summarized six themes of applications of artificial intelligence in anesthesiology: (1) depth of anesthesia monitoring, (2) control of anesthesia, (3) event and risk prediction, (4) ultrasound guidance, (5) pain management, and (6) operating room logistics. Based on papers identified in the review, several topics within artificial intelligence were described and summarized: (1) machine learning (including supervised, unsupervised, and reinforcement learning), (2) techniques in artificial intelligence (e.g., classical machine learning, neural networks and deep learning, Bayesian methods), and (3) major applied fields in artificial intelligence.The implications of artificial intelligence for the practicing anesthesiologist are discussed as are its limitations and the role of clinicians in further developing artificial intelligence for use in clinical care. Artificial intelligence has the potential to impact the practice of anesthesiology in aspects ranging from perioperative support to critical care delivery to outpatient pain management.
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