Use of Innovative SPECT Techniques in the Presurgical Evaluation of Patients with Nonlesional Extratemporal Drug-Resistant Epilepsy

Yassin, Ahmed; El‐Salem, Khalid; Al-Mistarehi, Abdel-Hameed; Momani, Aiman; Alaabdin, Anas M. Zein; Shah, Palak; Mountz, James M.; Bagić, Anto

doi:10.1155/2021/6614356

Cited by 14 publications

(13 citation statements)

References 72 publications

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“…Smaller studies have found similar positive predictive value [76.9% - ( 165 )]. The ability of SISCOM to accurately localize a seizure focus ranged from 73 to 85% (mean 77.3%) across multiple studies ( 164 – 172 ). A meta-analysis of 11 studies found SISCOM accurately localized seizure focus in 85.9% of cases and was concordant with EEG data in 65.3% of cases ( 166 ).…”

Section: Assessment Of the State Of The Art—spect In Brain Disordersmentioning

confidence: 99%

The Legacy of the TTASAAN Report—Premature Conclusions and Forgotten Promises: A Review of Policy and Practice Part I

2022

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Brain perfusion single photon emission computed tomography (SPECT) scans were initially developed in 1970's. A key radiopharmaceutical, hexamethylpropyleneamine oxime (HMPAO), was originally approved in 1988, but was unstable. As a result, the quality of SPECT images varied greatly based on technique until 1993, when a method of stabilizing HMPAO was developed. In addition, most SPECT perfusion studies pre-1996 were performed on single-head gamma cameras. In 1996, the Therapeutics and Technology Assessment Subcommittee of the American Academy of Neurology (TTASAAN) issued a report regarding the use of SPECT in the evaluation of neurological disorders. Although the TTASAAN report was published in January 1996, it was approved for publication in October 1994. Consequently, the reported brain SPECT studies relied upon to derive the conclusions of the TTASAAN report largely pre-date the introduction of stabilized HMPAO. While only 12% of the studies on traumatic brain injury (TBI) in the TTASAAN report utilized stable tracers and multi-head cameras, 69 subsequent studies with more than 23,000 subjects describe the utility of perfusion SPECT scans in the evaluation of TBI. Similarly, dementia SPECT imaging has improved. Modern SPECT utilizing multi-headed gamma cameras and quantitative analysis has a sensitivity of 86% and a specificity of 89% for the diagnosis of mild to moderate Alzheimer's disease—comparable to fluorodeoxyglucose positron emission tomography. Advances also have occurred in seizure neuroimaging. Lastly, developments in SPECT imaging of neurotoxicity and neuropsychiatric disorders have been striking. At the 25-year anniversary of the publication of the TTASAAN report, it is time to re-examine the utility of perfusion SPECT brain imaging. Herein, we review studies cited by the TTASAAN report vs. current brain SPECT imaging research literature for the major indications addressed in the report, as well as for emerging indications. In Part II, we elaborate technical aspects of SPECT neuroimaging and discuss scan interpretation for the clinician.

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Section: Assessment Of the State Of The Art—spect In Brain Disordersmentioning

confidence: 99%

The Legacy of the TTASAAN Report—Premature Conclusions and Forgotten Promises: A Review of Policy and Practice Part I

2022

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“…Artificial intelligence has made tremendous and remarkable progress in the health field [51], as it facilitates many of the problems related to filling out forms for admission in hospitals or clinics and preventing the surge of discounts at the reception desk. In recent times, the science of artificial intelligence has been able to contribute to the development of the healthcare system to be more effective in detecting diseases such as cancer [52], infections [53][54][55][56], and other diseases, in addition to the development of robots that perform surgeries and accurate diagnosis [57,58]. Recent statistics indicate that artificial intelligence developers have arrived at the stage of providing effective solutions to complex obstacles in healthcare management systems, thereby revolutionizing the medical field to eliminate inefficient systems prevailing in societies.…”

Section: The Importance Of Artificial Intelligence In Healthcare and ...mentioning

confidence: 99%

Has the Future Started? The Current Growth of Artificial Intelligence, Machine Learning, and Deep Learning

2022

ijcsm

102

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In the modern era, many terms related to artificial intelligence, machine learning, and deep learning are widely used in domains such as business, healthcare, industries, and military. In these fields, the accurate prediction and analysis of data are crucial, regardless of how large the data are. However, using big data is confusing due to the rapid growth and massive development in public life, which requires a tremendous human effort in order to deal with such type of data and extract worthy information from it. Thus, the role of artificial intelligence begins in analyzing big data based on scientific techniques, especially in machine learning, whereby it can identify patterns of decision-making and reduce human intervention. In this regard, the significance role of artificial intelligence, machine learning and deep learning is growing rapidly. In this article, the authors decide to highlight these sciences by discussing how to develop and apply them in many decision-making domains. In addition, the influence of artificial intelligence in healthcare and the gains this science provides in the face of the COVID-19 pandemic are highlighted. This article concludes that these sciences have a significant impact, especially in healthcare, as well as the ability to grow and improve their methodology in decision-making. Additionally, artificial intelligence is a vital science, especially in the face of COVID-19.

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“…There are a large number of investigations in which artificial intelligence techniques have been used in analysing medical data, whether images or texts, due to the significance of these technologies in healthcare. From 2019 until August 2022, many investigations appeared in the application of machine learning and deep learning techniques in diagnosing COVID-19 diseases through chest X-rays and comparing the performance and practices of these techniques in analysing the most significant number of images [77][78][79][80][81]. Analyses based on machine learning in the field of diagnosis focus on many diseases, including heart disease, cancer, kidney disease, and Alzheimer's.…”

Section: Digitalisation and Ai In Healthcarementioning

confidence: 99%

The Significance of Digitalisation and Artificial Intelligence in The Healthcare Sector: A Review

Mijwil

Al-Mistarehi

2022

AJPNMS

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Nowadays, artificial intelligence, machine learning, and deep learning are among the most popular and applied topics in many scientific and life fields that serve humanity. This science seeks to impose itself strongly on the various activities and academic circles and information science. Artificial intelligence techniques have proven their worth to be important in many fields, especially in the medical fields, business administration, military applications, communications, and many others. In short, artificial intelligence is from another world that will be of great importance in the future. In this article, the importance of digitisation and artificial intelligence in the healthcare sector will be addressed, what services they provide to this sector, and how they contribute to the service of healthcare workers and patient satisfaction. This article concluded that artificial intelligence and digital technologies are of great importance in the healthcare sector and can never be dispensed with.

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Use of Innovative SPECT Techniques in the Presurgical Evaluation of Patients with Nonlesional Extratemporal Drug-Resistant Epilepsy

Cited by 14 publications

References 72 publications

The Legacy of the TTASAAN Report—Premature Conclusions and Forgotten Promises: A Review of Policy and Practice Part I

The Legacy of the TTASAAN Report—Premature Conclusions and Forgotten Promises: A Review of Policy and Practice Part I

Has the Future Started? The Current Growth of Artificial Intelligence, Machine Learning, and Deep Learning

The Significance of Digitalisation and Artificial Intelligence in The Healthcare Sector: A Review

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