Artificial intelligence, machine learning and the pediatric airway

Matava, Clyde; Pankiv, Evelina; Ahumada, Luis M.; Weingarten, Benjamin; Simpao, Allan F.

doi:10.1111/pan.13792

Cited by 33 publications

(30 citation statements)

References 25 publications

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“…Lidocaine is a membrane stabilizer that inhibits neutrophil (PMN) adhesion and aggregation, reduces oxygen radical and protein hydrolase release, stabilizes cell membranes, regulates cytokines, and suppresses excessive inflammatory responses. Inflammatory mediators LB4 and interleukin 1 α (IL-1 α ) are strong PMN chemotactic agents, inducing PMN edge, degranulation, exudation, superoxide production, and increasing vascular permeability in concert with prostaglandin E2 [ 24 , 26 ]. In vitro incubation of monocytes with different concentrations (2–20 mol/L) of lidocaine significantly inhibited the release of LB4 and IL-1 α , and micromolar concentration levels of lidocaine inhibited the release of histamine from leukocytes, mast cells, and basophils, suggesting that lidocaine can inhibit the release of some key inflammatory mediators and exert anti-inflammatory effects.…”

Section: Related Workmentioning

confidence: 99%

“…By incorporating AI algorithms and intelligent medical assistants, the system proposes a shift from a reactive to a preventive model of care. The AI-based model proposed in [ 26 ] can help improve patient care and reduce healthcare spending. Researchers at the Massachusetts Institute of Technology have developed a system to predict the characteristics of vocal cord disease [ 31 ].…”

Section: Related Workmentioning

confidence: 99%

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Clinical Application of Artificial Intelligence: Auto-Discerning the Effectiveness of Lidocaine Concentration Levels in Osteosarcoma Femoral Tumor Segment Resection

2022

Journal of Healthcare Engineering

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Adolescents and children worldwide are threatened by osteosarcoma, a tumor that predominantly affects the long bone epiphysis. Osteosarcoma is the most common and highly malignant bone tumor in youngsters. Early tumor detection is the key to effective treatment of this disease. The discovery of biomarkers and the growing understanding of molecules and their complex interactions have improved the outcome of clinical trials in osteosarcoma. This article describes biomarkers of osteosarcoma with the aim of positively influencing the progress of clinical treatment of osteosarcoma. Femoral bone tumor is a typical condition of osteosarcoma. Due to the wide range of femoral stem types, complexities in the distal femur, and tumors in the rotor part of femur, physicians following the traditional clinical approach face difficulties in removing the lesion and fixing the femur with resection of the tumor segment. In this paper, the effect of small doses of different concentrations of lidocaine anesthesia in patients undergoing lumpectomy for osteosarcoma femoral tumor segments is investigated. A computer-based artificial intelligence method for automated determination of different concentration levels of lidocaine anesthesia and amputation of osteosarcoma femoral tumor segment is proposed. Statistical analysis is carried on the empirical data including intraoperative bleeding, intraoperative and postoperative pain scores, surgical operation time, postoperative complications, patient satisfaction, and local anesthetic dose. The results showed that the patients in the study group had low intraoperative bleeding, short operation time, low postoperative hematoma formation rate, high patient satisfaction, higher dosage of anesthetic solution, and low dosage of lidocaine. Results revealed that mean arterial pressure and heart rate in extubating and intubating were significantly lower in the observation group than in the control group, and a significant difference ( P < 0.05 ) was observed between the two groups. This proves that the proposed algorithm can adequately reduce bleeding, alleviate postoperative pain, shorten operation time, reduce complications, accelerate recovery, and ensure better treatment results.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Clinical Application of Artificial Intelligence: Auto-Discerning the Effectiveness of Lidocaine Concentration Levels in Osteosarcoma Femoral Tumor Segment Resection

2022

Journal of Healthcare Engineering

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“…AI has also been used in pediatric anesthesia airway management, assisting pediatric patients with endotracheal intubation in the form of SmartScope. The machine-learning-based algorithm that Matava C. et al developed [ 179 ] can identify the position of the vocal cords and the airway/tracheal anatomy, helping with confirmation of intubation using bronchoscopy and video-laryngoscopy. As the study mentions, the localization of the tracheal rings is possible using segmentation module output as a tracheal GPS.…”

Section: Resultsmentioning

confidence: 99%

Where Is the Artificial Intelligence Applied in Dentistry? Systematic Review and Literature Analysis

et al. 2022

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This literature research had two main objectives. The first objective was to quantify how frequently artificial intelligence (AI) was utilized in dental literature from 2011 until 2021. The second objective was to distinguish the focus of such publications; in particular, dental field and topic. The main inclusion criterium was an original article or review in English focused on dental utilization of AI. All other types of publications or non-dental or non-AI-focused were excluded. The information sources were Web of Science, PubMed, Scopus, and Google Scholar, queried on 19 April 2022. The search string was “artificial intelligence” AND (dental OR dentistry OR tooth OR teeth OR dentofacial OR maxillofacial OR orofacial OR orthodontics OR endodontics OR periodontics OR prosthodontics). Following the removal of duplicates, all remaining publications were returned by searches and were screened by three independent operators to minimize the risk of bias. The analysis of 2011–2021 publications identified 4413 records, from which 1497 were finally selected and calculated according to the year of publication. The results confirmed a historically unprecedented boom in AI dental publications, with an average increase of 21.6% per year over the last decade and a 34.9% increase per year over the last 5 years. In the achievement of the second objective, qualitative assessment of dental AI publications since 2021 identified 1717 records, with 497 papers finally selected. The results of this assessment indicated the relative proportions of focal topics, as follows: radiology 26.36%, orthodontics 18.31%, general scope 17.10%, restorative 12.09%, surgery 11.87% and education 5.63%. The review confirms that the current use of artificial intelligence in dentistry is concentrated mainly around the evaluation of digital diagnostic methods, especially radiology; however, its implementation is expected to gradually penetrate all parts of the profession.

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“…[20] Other studies have approached the role of machine learning as a complement to the physical exam, performing automatic facial analysis and detecting morphological traits related to difficult airways. [21,22] It has also been applied to monitoring pediatric airways, enhancing the detection of critical incidents and providing early warnings to the clinician. [22]…”

Section: Artificial Intelligence In Airway Managementmentioning

confidence: 99%

New technologies in airway management: A review

et al. 2022

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The evolution of medical knowledge and technological growth have contributed to the development of different techniques and devices for airway management. These appear to play a role in optimizing the number of attempts and overall success, ultimately reducing the negative consequences of airway manipulation. In this literature review, we highlight the recent evidence regarding new technologies applied to airway management. Before intubation, every patient should have an individualized structured airway management plan. Technology can help with both airway evaluation and tracheal intubation. Point-of-care cervical ultrasound and artificial intelligence models with automated facial analysis have been used to predict difficult airways. Various devices can be used in airway management. This includes a robotic video endoscope that guides intubation based on real image recognition, a laryngeal mask with a non-inflatable cuff that tries to reduce local complications, video laryngeal masks that are able to confirm the correct position and facilitate intubation, Viescope™, a videolaryngoscope developed for combat medicine with a unique circular blade, a system that uses cervical transillumination for glottis identification in difficult airways and Vivasight SL™ tracheal tube, which has a high-resolution camera at its tip guaranteeing visual assurance of tube position as well as guiding bronchial blocker position. To conclude, we detailed the challenges in airway management outside the operating room as well as described suction-assisted laryngoscopy and airway decontamination technique for contaminated airways. Further research in the clinical setting is recommended to better support the use of these technologies

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Artificial intelligence, machine learning and the pediatric airway

Cited by 33 publications

References 25 publications

Clinical Application of Artificial Intelligence: Auto-Discerning the Effectiveness of Lidocaine Concentration Levels in Osteosarcoma Femoral Tumor Segment Resection

Clinical Application of Artificial Intelligence: Auto-Discerning the Effectiveness of Lidocaine Concentration Levels in Osteosarcoma Femoral Tumor Segment Resection

Where Is the Artificial Intelligence Applied in Dentistry? Systematic Review and Literature Analysis

New technologies in airway management: A review

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