Exploring Pain Insensitivity Inducing Gene ZFHX2 by using Deep Convolutional Neural Network

Akshayaa, S; Vidhya, R.; M, Hima Vyshnavi A; Namboori, P. K. Krishnan

doi:10.1109/iccmc.2019.8819666

Cited by 2 publications

(3 citation statements)

References 21 publications

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“…Miettinen et al (2021) [30] identified pain phenotype clusters using DTs, while Xuyi et al (2021) [55] predicted multiple symptoms using NNs. Akshayaa et al (2019) [57] achieved high accuracy in detecting chronic pain with CNN, and Bang et al (2023) [56] predicted breakthrough pain onset with DL models. Masukawa et al (2022) [41] utilized LR, RF, GBM, and SVM to detect social distress and spiritual pain in palliative care.…”

Section: Resultsmentioning

confidence: 99%

“…While most studies used demographic and clinical data as input variables, only 4 studies included genetic data as inputs, Olesen et al, and Reinbolt et al, used single nucleotide polymorphisms (SNPs) data as inputs for the models [47,50]. More novel approaches were observed with studies including image data (pathology, radiological, dosiomic data) as model inputs related to cancer pain (Akshayaa et al [57], and Chao et al, [24]). Our systematic review did not provide specific details on the evaluation process of these features or how they were selected for model input.…”

Section: Performance Of Cancer Related Pain and Pain Management Ai/ml...mentioning

confidence: 99%

“…Deep Learning (DL) [57], [56] Subclass of NN that Includes many layers of the neural network and massive volumes of complex and disparate data.…”

Section: • Association Between Epigenetics and Persistent Cancer Painmentioning

confidence: 99%

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Artificial Intelligence and Machine Learning in Cancer Related Pain: A Systematic Review

Salama,

Godinich,

Geng

et al. 2023

Preprint

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Background/objectivePain is a challenging multifaceted symptom reported by most cancer patients, resulting in a substantial burden on both patients and healthcare systems. This systematic review aims to explore applications of artificial intelligence/machine learning (AI/ML) in predicting pain-related outcomes and supporting decision-making processes in pain management in cancer.MethodsA comprehensive search of Ovid MEDLINE, EMBASE and Web of Science databases was conducted using terms including “Cancer”, “Pain”, “Pain Management”, “Analgesics”, “Opioids”, “Artificial Intelligence”, “Machine Learning”, “Deep Learning”, and “Neural Networks” published up to September 7, 2023. The screening process was performed using the Covidence screening tool. Only original studies conducted in human cohorts were included. AI/ML models, their validation and performance and adherence to TRIPOD guidelines were summarized from the final included studies.ResultsThis systematic review included 44 studies from 2006-2023. Most studies were prospective and uni-institutional. There was an increase in the trend of AI/ML studies in cancer pain in the last 4 years. Nineteen studies used AI/ML for classifying cancer patients’ pain development after cancer therapy, with median AUC 0.80 (range 0.76-0.94). Eighteen studies focused on cancer pain research with median AUC 0.86 (range 0.50-0.99), and 7 focused on applying AI/ML for cancer pain management decisions with median AUC 0.71 (range 0.47-0.89). Multiple ML models were investigated with. median AUC across all models in all studies (0.77). Random forest models demonstrated the highest performance (median AUC 0.81), lasso models had the highest median sensitivity (1), while Support Vector Machine had the highest median specificity (0.74). Overall adherence of included studies to TRIPOD guidelines was 70.7%. Lack of external validation (14%) and clinical application (23%) of most included studies was detected. Reporting of model calibration was also missing in the majority of studies (5%).ConclusionImplementation of various novel AI/ML tools promises significant advances in the classification, risk stratification, and management decisions for cancer pain. These advanced tools will integrate big health-related data for personalized pain management in cancer patients. Further research focusing on model calibration and rigorous external clinical validation in real healthcare settings is imperative for ensuring its practical and reliable application in clinical practice.

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Section: Resultsmentioning

confidence: 99%

Section: Performance Of Cancer Related Pain and Pain Management Ai/ml...mentioning

confidence: 99%

See 1 more Smart Citation

Artificial Intelligence and Machine Learning in Cancer Related Pain: A Systematic Review

Salama,

Godinich,

Geng

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

Artificial Intelligence and Machine Learning in Cancer Pain: A Systematic Review

Salama,

Godinich,

Geng

et al. 2024

Journal of Pain and Symptom Management

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Exploring Pain Insensitivity Inducing Gene ZFHX2 by using Deep Convolutional Neural Network

Cited by 2 publications

References 21 publications

Artificial Intelligence and Machine Learning in Cancer Related Pain: A Systematic Review

Artificial Intelligence and Machine Learning in Cancer Related Pain: A Systematic Review

Artificial Intelligence and Machine Learning in Cancer Pain: A Systematic Review

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