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Grillo R, Quinta Reis BA, Lima BC, Peral Ferreira Pinto LA, Cruz Meira JB, Melhem-Elias F. The butterfly effect in oral and maxillofacial surgery: Understanding and applying chaos theory and complex systems principles. J Craniomaxillofac Surg 2024; 52:652-658. [PMID: 38582679 DOI: 10.1016/j.jcms.2024.03.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 03/12/2024] [Indexed: 04/08/2024] Open
Abstract
The present paper provides a historical context for chaos theory, originating in the 1960s with Edward Norton Lorenz's efforts to predict weather patterns. It introduces chaos theory, fractal geometry, nonlinear dynamics, and the butterfly effect, highlighting their exploration of complex systems. The authors aim to bridge the gap between chaos theory and oral and maxillofacial surgery (OMFS) through a literature review, exploring its applications and emphasizing the prevention of minor deviations in OMFS to avoid significant consequences. A comprehensive literature review was conducted on PubMed, Web of Science, and Google Scholar databases. The selection process adhered to the PRISMA-ScR guidelines and Leiden Manifesto principles. Articles focusing on chaos theory principles in health sciences, published in the last two decades, were included. The review encompassed 37 articles after screening 386 works. It revealed applications in outcome variation, surgical planning, simulations, decision-making, and emerging technologies. Potential applications include predicting infections, malignancies, dental fractures, and improving decision-making through disease prediction systems. Emerging technologies, despite criticisms, indicate advancements in AI integration, contributing to enhanced diagnostic accuracy and personalized treatment strategies. Chaos theory, a distinct scientific framework, holds potential to revolutionize OMFS. Its integration with advanced techniques promises personalized, less traumatic surgeries and improved patient care. The interdisciplinary synergy of chaos theory and emerging technologies presents a future in which OMFS practices become more efficient, less traumatic, and achieve a level of precision never seen before.
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Affiliation(s)
- Ricardo Grillo
- Department of Oral and Maxillofacial Surgery, University of São Paulo School of Dentistry, São Paulo-SP, Brazil; Department of Oral and Maxillofacial Surgery, Faculdade Patos de Minas, Brasília-DF, Brazil.
| | | | - Bernardo Correia Lima
- Department of Oral and Maxillofacial Surgery, University of São Paulo School of Dentistry, São Paulo-SP, Brazil; Department of Oral and Maxillofacial Surgery and Diagnosis, Hospital da Boca, Santa Casa da Misericórdia do Rio de Janeiro, RJ, Brazil
| | | | - Josete Barbosa Cruz Meira
- Department of Biomaterials and Oral Biology, University of São Paulo School of Dentistry, São Paulo-SP, Brazil
| | - Fernando Melhem-Elias
- Department of Oral and Maxillofacial Surgery, University of São Paulo School of Dentistry, São Paulo-SP, Brazil; Private Practice in Oral and Maxillofacial Surgery, São Paulo-SP, Brazil
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Deep Learning Forecasts the Occurrence of Sleep Apnea from Single-Lead ECG. Cardiovasc Eng Technol 2022; 13:809-815. [PMID: 35301676 DOI: 10.1007/s13239-022-00615-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 02/15/2022] [Indexed: 01/27/2023]
Abstract
OBJECTIVES Sleep apnea is the most common sleep disorder that leads to serious health complications if not treated early. Forecasting apnea occurrence ahead in time provides the opportunity to take appropriate actions to control and manage it. METHODS A novel framework for forecasting the occurrence of apnea from single-lead electrocardiogram (ECG) based on deep recurrent neural networks is proposed. ECG R-peak amplitudes and R-R intervals are extracted and aligned using power spectral analysis, and recurrent deep learning models are developed to extract the most predictive ECG features and forecast the occurrence of apnea. RESULTS The performance of the proposed approach was validated in forecasting apnea events up to five minutes in future on a dataset of 70 sleep recordings. A forecasting accuracy of up to 94.95% was achieved which was higher than the performance of conventional multilayer perceptron (p < 0.05) and other state-of-the-art techniques. CONCLUSIONS The proposed deep learning approach was successful in forecasting the occurrence of sleep apnea from single-lead ECG. It can therefore be adopted in wearable sleep monitors for the management of sleep apnea. Our developed algorithms are publicly available on GitHub.
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Richter B, Mace Z, Hays ME, Adhikari S, Pham HQ, Sclabassi RJ, Kolber B, Yerneni SS, Campbell P, Cheng B, Tomycz N, Whiting DM, Le TQ, Nelson TL, Averick S. Development and Characterization of Novel Conductive Sensing Fibers for In Vivo Nerve Stimulation. SENSORS (BASEL, SWITZERLAND) 2021; 21:7581. [PMID: 34833660 PMCID: PMC8619502 DOI: 10.3390/s21227581] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 11/03/2021] [Accepted: 11/07/2021] [Indexed: 12/11/2022]
Abstract
Advancements in electrode technologies to both stimulate and record the central nervous system's electrical activities are enabling significant improvements in both the understanding and treatment of different neurological diseases. However, the current neural recording and stimulating electrodes are metallic, requiring invasive and damaging methods to interface with neural tissue. These electrodes may also degrade, resulting in additional invasive procedures. Furthermore, metal electrodes may cause nerve damage due to their inherent rigidity. This paper demonstrates that novel electrically conductive organic fibers (ECFs) can be used for direct nerve stimulation. The ECFs were prepared using a standard polyester material as the structural base, with a carbon nanotube ink applied to the surface as the electrical conductor. We report on three experiments: the first one to characterize the conductive properties of the ECFs; the second one to investigate the fiber cytotoxic properties in vitro; and the third one to demonstrate the utility of the ECF for direct nerve stimulation in an in vivo rodent model.
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Affiliation(s)
- Bertram Richter
- System Department of Neurosurgery, Allegheny Health Network, Pittsburgh, PA 15212, USA; (B.R.); (Z.M.); (R.J.S.); (B.C.); (N.T.); (D.M.W.)
| | - Zachary Mace
- System Department of Neurosurgery, Allegheny Health Network, Pittsburgh, PA 15212, USA; (B.R.); (Z.M.); (R.J.S.); (B.C.); (N.T.); (D.M.W.)
- Computational Diagnostics, Inc., Pittsburgh, PA 15213, USA
| | - Megan E. Hays
- Department of Chemistry, Oklahoma State University, Stillwater, OK 74078, USA; (M.E.H.); (S.A.); (T.L.N.)
| | - Santosh Adhikari
- Department of Chemistry, Oklahoma State University, Stillwater, OK 74078, USA; (M.E.H.); (S.A.); (T.L.N.)
| | - Huy Q. Pham
- Department of Biomedical Engineering, North Dakota State University, Fargo, ND 58102, USA;
| | - Robert J. Sclabassi
- System Department of Neurosurgery, Allegheny Health Network, Pittsburgh, PA 15212, USA; (B.R.); (Z.M.); (R.J.S.); (B.C.); (N.T.); (D.M.W.)
- Computational Diagnostics, Inc., Pittsburgh, PA 15213, USA
| | - Benedict Kolber
- Department of Neuroscience, University of Texas at Dallas, Richardson, TX 75080, USA;
| | - Saigopalakrishna S. Yerneni
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15217, USA; (S.S.Y.); (P.C.)
| | - Phil Campbell
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15217, USA; (S.S.Y.); (P.C.)
| | - Boyle Cheng
- System Department of Neurosurgery, Allegheny Health Network, Pittsburgh, PA 15212, USA; (B.R.); (Z.M.); (R.J.S.); (B.C.); (N.T.); (D.M.W.)
| | - Nestor Tomycz
- System Department of Neurosurgery, Allegheny Health Network, Pittsburgh, PA 15212, USA; (B.R.); (Z.M.); (R.J.S.); (B.C.); (N.T.); (D.M.W.)
| | - Donald M. Whiting
- System Department of Neurosurgery, Allegheny Health Network, Pittsburgh, PA 15212, USA; (B.R.); (Z.M.); (R.J.S.); (B.C.); (N.T.); (D.M.W.)
| | - Trung Q. Le
- Department of Industrial and Manufacturing Engineering, North Dakota State University, Fargo, ND 58102, USA
| | - Toby L. Nelson
- Department of Chemistry, Oklahoma State University, Stillwater, OK 74078, USA; (M.E.H.); (S.A.); (T.L.N.)
| | - Saadyah Averick
- System Department of Neurosurgery, Allegheny Health Network, Pittsburgh, PA 15212, USA; (B.R.); (Z.M.); (R.J.S.); (B.C.); (N.T.); (D.M.W.)
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Huynh PK, Setty A, Phan H, Le TQ. Probabilistic domain-knowledge modeling of disorder pathogenesis for dynamics forecasting of acute onset. Artif Intell Med 2021; 115:102056. [PMID: 34001316 PMCID: PMC8493977 DOI: 10.1016/j.artmed.2021.102056] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 03/01/2021] [Accepted: 03/22/2021] [Indexed: 11/18/2022]
Abstract
Disease pathogenesis, a type of domain knowledge about biological mechanisms leading to diseases, has not been adequately encoded in machine-learning-based medical diagnostic models because of the inter-patient variabilities and complex dependencies of the underlying pathogenetic mechanisms. We propose 1) a novel pathogenesis probabilistic graphical model (PPGM) to quantify the dynamics underpinning patient-specific data and pathogenetic domain knowledge, 2) a Bayesian-based inference paradigm to answer the medical queries and forecast acute onsets. The PPGM model consists of two components: a Bayesian network of patient attributes and a temporal model of pathogenetic mechanisms. The model structure was reconstructed from expert knowledge elicitation, and its parameters were estimated using Variational Expectation-Maximization algorithms. We benchmarked our model with two well-established hidden Markov models (HMMs) - Input-output HMM (IO-HMM) and Switching Auto-Regressive HMM (SAR-HMM) - to evaluate the computational costs, forecasting performance, and execution time. Two case studies on Obstructive Sleep Apnea (OSA) and Paroxysmal Atrial Fibrillation (PAF) were used to validate the model. While the performance of the parameter learning step was equivalent to those of IO-HMM and SAR-HMM models, our model forecasting ability was outperforming those two models. The merits of the PPGM model are its representation capability to capture the dynamics of pathogenesis and perform medical inferences and its interpretability for physicians. The model has been used to perform medical queries and forecast the acute onset of OSA and PAF. Additional applications of the model include prognostic healthcare and preventive personalized treatments.
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Affiliation(s)
- Phat K Huynh
- Department of Industrial and Manufacturing Engineering, North Dakota State University at Fargo, ND, USA
| | | | - Hao Phan
- Pham Ngoc Thach University of Medicine at Ho Chi Minh City, Viet Nam
| | - Trung Q Le
- Department of Industrial and Manufacturing Engineering, North Dakota State University at Fargo, ND, USA; Department of Biomedical Engineering, North Dakota State University at Fargo, ND, USA.
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Apache Spark SVM for Predicting Obstructive Sleep Apnea. BIG DATA AND COGNITIVE COMPUTING 2020. [DOI: 10.3390/bdcc4040025] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Obstructive sleep apnea (OSA), a common form of sleep apnea generally caused by a collapse of the upper respiratory airway, is associated with one of the leading causes of death in adults: hypertension, cardiovascular and cerebrovascular disease. In this paper, an algorithm for predicting obstructive sleep apnea episodes based on a spark-based support vector machine (SVM) is proposed. Wavelet decomposition and wavelet reshaping were used to denoise sleep apnea data, and cubic B-type interpolation wavelet transform was used to locate the QRS complex in OSA data. Twelve features were extracted, and SVM was used to predict OSA onset. Different configurations of SVM were compared with the regular, as well as Spark Big Data, frameworks. The results showed that Spark-based kernel SVM performs best, with an accuracy of 90.52% and specificity of 93.4%. Overall, Spark-SVM performed better than regular SVM, and polynomial SVM performed better than linear SVM, both for regular SVM and Spark-SVM.
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