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: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.
| S-EPMC7286802 | biostudies-literature
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: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.
| S-EPMC7040122 | biostudies-literature
Project description:Aim:To perform a systematic review on the application of artificial intelligence (AI) based knowledge discovery techniques in pharmacoepidemiology. Study Eligibility Criteria:Clinical 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 Sources:Articles recorded from 1950/01/01 to 2019/05/06 in Ovid MEDLINE were screened. Participants:Studies including humans (real or simulated) exposed to a drug. Results:In 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. Conclusions:The use of knowledge discovery techniques of artificial intelligence techniques has increased exponentially over the years covering numerous sub-topics of pharmacoepidemiology. Systematic Review Registration:Systematic 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.
| S-EPMC7888282 | biostudies-literature
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.
| S-EPMC7643051 | biostudies-literature
Project description:Debilitating hearing loss (HL) affects ~6% of the human population. Only 20% of the people in need of a hearing assistive device will eventually seek and acquire one. The number of people that are satisfied with their Hearing Aids (HAids) and continue using them in the long term is even lower. Understanding the personal, behavioral, environmental, or other factors that correlate with the optimal HAid fitting and with users' experience of HAids is a significant step in improving patient satisfaction and quality of life, while reducing societal and financial burden. In SMART BEAR we are addressing this need by making use of the capacity of modern HAids to provide dynamic logging of their operation and by combining this information with a big amount of information about the medical, environmental, and social context of each HAid user. We are studying hearing rehabilitation through a 12-month continuous monitoring of HL patients, collecting data, such as participants' demographics, audiometric and medical data, their cognitive and mental status, their habits, and preferences, through a set of medical devices and wearables, as well as through face-to-face and remote clinical assessments and fitting/fine-tuning sessions. Descriptive, AI-based analysis and assessment of the relationships between heterogeneous data and HL-related parameters will help clinical researchers to better understand the overall health profiles of HL patients, and to identify patterns or relations that may be proven essential for future clinical trials. In addition, the future state and behavioral (e.g., HAids Satisfiability and HAids usage) of the patients will be predicted with time-dependent machine learning models to assist the clinical researchers to decide on the nature of the interventions. Explainable Artificial Intelligence (XAI) techniques will be leveraged to better understand the factors that play a significant role in the success of a hearing rehabilitation program, constructing patient profiles. This paper is a conceptual one aiming to describe the upcoming data collection process and proposed framework for providing a comprehensive profile for patients with HL in the context of EU-funded SMART BEAR project. Such patient profiles can be invaluable in HL treatment as they can help to identify the characteristics making patients more prone to drop out and stop using their HAids, using their HAids sufficiently long during the day, and being more satisfied by their HAids experience. They can also help decrease the number of needed remote sessions with their Audiologist for counseling, and/or HAids fine tuning, or the number of manual changes of HAids program (as indication of poor sound quality and bad adaptation of HAids configuration to patients' real needs and daily challenges), leading to reduced healthcare cost.
| S-EPMC9459083 | biostudies-literature
Project description:Stroke is a major threat to life and health in modern society, especially in the aging population. Stroke may cause sudden death or severe sequela-like hemiplegia. Although computed tomography (CT) and magnetic resonance imaging (MRI) are standard diagnosis methods, and artificial intelligence models have been built based on these images, shortage in medical resources and the time and cost of CT/MRI imaging hamper fast detection, thus increasing the severity of stroke. Here, we developed a convolutional neural network model by integrating four networks, Xception, ResNet50, VGG19, and EfficientNetb1, to recognize stroke based on 2D facial images with a cross-validation area under curve (AUC) of 0.91 within the training set of 185 acute ischemic stroke patients and 551 age- and sex-matched controls, and AUC of 0.82 in an independent data set regardless of age and sex. The model computed stroke probability was quantitatively associated with facial features, various clinical parameters of blood clotting indicators and leukocyte counts, and, more importantly, stroke incidence in the near future. Our real-time facial image artificial intelligence model can be used to rapidly screen and prediagnose stroke before CT scanning, thus meeting the urgent need in emergency clinics, potentially translatable to routine monitoring.
| S-EPMC11320352 | biostudies-literature
Project description:BackgroundThe efficacy of facial muscle exercises (FMEs) for facial rejuvenation is controversial. In the majority of previous studies, nonquantitative assessment tools were used to assess the benefits of FMEs.ObjectivesThis study examined the effectiveness of FMEs using a Pao (MTG, Nagoya, Japan) device to quantify facial rejuvenation.MethodsFifty females were asked to perform FMEs using a Pao device for 30 seconds twice a day for 8 weeks. Facial muscle thickness and cross-sectional area were measured sonographically. Facial surface distance, surface area, and volumes were determined using a laser scanning system before and after FME. Facial muscle thickness, cross-sectional area, midfacial surface distances, jawline surface distance, and lower facial surface area and volume were compared bilaterally before and after FME using a paired Student t test.ResultsThe cross-sectional areas of the zygomaticus major and digastric muscles increased significantly (right: P < 0.001, left: P = 0.015), while the midfacial surface distances in the middle (right: P = 0.005, left: P = 0.047) and lower (right: P = 0.028, left: P = 0.019) planes as well as the jawline surface distances (right: P = 0.004, left: P = 0.003) decreased significantly after FME using the Pao device. The lower facial surface areas (right: P = 0.005, left: P = 0.006) and volumes (right: P = 0.001, left: P = 0.002) were also significantly reduced after FME using the Pao device.ConclusionsFME using the Pao device can increase facial muscle thickness and cross-sectional area, thus contributing to facial rejuvenation.Level of evidence 4
| S-EPMC5888959 | biostudies-literature
Project description:It is very important to keep structures and constructional elements in service during and after exposure to elevated temperatures. Investigation of the structural behaviour of different components and structures at elevated temperatures is an approach to manipulate the serviceability of the structures during heat exposure. Channel connectors are widely used shear connectors not only for their appealing mechanical properties but also for their workability and cost-effective nature. In this study, a finite element (FE) evaluation was performed on an authentic composite model, and the behaviour of the channel shear connector at elevated temperature was examined. Furthermore, a novel hybrid intelligence algorithm based on a feature-selection trait with the incorporation of particle swarm optimization (PSO) and multi-layer perceptron (MLP) algorithms has been developed to predict the slip response of the channel. The hybrid intelligence algorithm that uses artificial neural networks is performed on derived data from the FE study. Finally, the obtained numerical results are compared with extreme learning machine (ELM) and radial basis function (RBF) results. The MLP-PSO represented dramatically accurate results for slip value prediction at elevated temperatures. The results proved the active presence of the channels, especially to improve the stiffness and loading capacity of the composite beam. Although the height enhances the ductility, stiffness is significantly reduced at elevated temperatures. According to the results, temperature, failure load, the height of connector and concrete block strength are the key governing parameters for composite floor design against high temperatures.
| S-EPMC8877472 | biostudies-literature