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Brydges G, Uppal A, Gottumukkala V. Application of Machine Learning in Predicting Perioperative Outcomes in Patients with Cancer: A Narrative Review for Clinicians. Curr Oncol 2024; 31:2727-2747. [PMID: 38785488 PMCID: PMC11120613 DOI: 10.3390/curroncol31050207] [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: 04/09/2024] [Revised: 05/07/2024] [Accepted: 05/10/2024] [Indexed: 05/25/2024] Open
Abstract
This narrative review explores the utilization of machine learning (ML) and artificial intelligence (AI) models to enhance perioperative cancer care. ML and AI models offer significant potential to improve perioperative cancer care by predicting outcomes and supporting clinical decision-making. Tailored for perioperative professionals including anesthesiologists, surgeons, critical care physicians, nurse anesthetists, and perioperative nurses, this review provides a comprehensive framework for the integration of ML and AI models to enhance patient care delivery throughout the perioperative continuum.
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Affiliation(s)
- Garry Brydges
- Division of Anesthesiology, Critical Care & Pain Medicine, The University of Texas at MD Anderson Cancer Center, Houston, TX 77030, USA;
| | - Abhineet Uppal
- Department of Colon & Rectal Surgery, The University of Texas at MD Anderson Cancer Center, Houston, TX 77030, USA;
| | - Vijaya Gottumukkala
- Department of Anesthesiology & Perioperative Medicine, The University of Texas at MD Anderson Cancer Center, 1400-Unit 409, Holcombe Blvd, Houston, TX 77030, USA
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Tandon R, Agrawal S, Rathore NPS, Mishra AK, Jain SK. A systematic review on deep learning-based automated cancer diagnosis models. J Cell Mol Med 2024; 28:e18144. [PMID: 38426930 PMCID: PMC10906380 DOI: 10.1111/jcmm.18144] [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: 06/28/2023] [Revised: 12/08/2023] [Accepted: 01/16/2024] [Indexed: 03/02/2024] Open
Abstract
Deep learning is gaining importance due to its wide range of applications. Many researchers have utilized deep learning (DL) models for the automated diagnosis of cancer patients. This paper provides a systematic review of DL models for automated diagnosis of cancer patients. Initially, various DL models for cancer diagnosis are presented. Five major categories of cancers such as breast, lung, liver, brain and cervical cancer are considered. As these categories of cancers have a very high percentage of occurrences with high mortality rate. The comparative analysis of different types of DL models is drawn for the diagnosis of cancer at early stages by considering the latest research articles from 2016 to 2022. After comprehensive comparative analysis, it is found that most of the researchers achieved appreciable accuracy with implementation of the convolutional neural network model. These utilized the pretrained models for automated diagnosis of cancer patients. Various shortcomings with the existing DL-based automated cancer diagnosis models are also been presented. Finally, future directions are discussed to facilitate further research for automated diagnosis of cancer patients.
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Affiliation(s)
| | | | | | - Abhinava K. Mishra
- Molecular, Cellular and Developmental Biology DepartmentUniversity of California Santa BarbaraSanta BarbaraCaliforniaUSA
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Yuan G, Lv B, Hao C. Application of artificial neural networks in reproductive medicine. HUM FERTIL 2023; 26:1195-1201. [PMID: 36628627 DOI: 10.1080/14647273.2022.2156301] [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: 12/14/2021] [Accepted: 09/01/2022] [Indexed: 01/12/2023]
Abstract
With the emergence of the age of information, the data on reproductive medicine has improved immensely. Nonetheless, healthcare workers who wish to utilise the relevance and implied value of the various data available to aid clinical decision-making encounter the difficulty of statistically analysing such large data. The application of artificial intelligence becoming widespread in recent years has emerged as a turning point in this regard. Artificial neural networks (ANNs) exhibit beneficial characteristics of comprehensive analysis and autonomous learning, owing to which these are being applied to disease diagnosis, embryo quality assessment, and prediction of pregnancy outcomes. The present report aims to summarise the application of ANNs in the field of reproduction and analyse its further application potential.
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Affiliation(s)
- Guanghui Yuan
- Department of Qingdao Medical College, Qingdao University, Qingdao, Shandong, China
| | - Bohan Lv
- Department of Intensive Care Unit, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Cuifang Hao
- Department of Reproductive Medicine, The Affiliated Women and Children's Hospital of Qingdao University, Qingdao, Shandong, China
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Lorenc A, Romaszko-Wojtowicz A, Jaśkiewicz Ł, Doboszyńska A, Buciński A. Exploring the efficacy of artificial neural networks in predicting lung cancer recurrence: a retrospective study based on patient records. Transl Lung Cancer Res 2023; 12:2083-2097. [PMID: 38025814 PMCID: PMC10654430 DOI: 10.21037/tlcr-23-350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 09/26/2023] [Indexed: 12/01/2023]
Abstract
Background Lung cancer remains a significant public health concern, accounting for a considerable number of cancer-related deaths worldwide. Neural networks have emerged as a promising tool that can aid in the diagnosis and treatment of various cancers. Consequently, there has been a growing interest in exploring the potential of artificial intelligence (AI) methods in medicine. The present study aimed to evaluate the effectiveness of a neural network in predicting lung cancer recurrence. Methods The study employed retrospective data from 2,296 medical records of patients diagnosed with lung cancer and admitted to the Warmińsko-Mazurskie Center for Lung Diseases in Olsztyn, Poland. The statistical software STATISTICA 7.1, equipped with the Neural Networks module (StatSoft Inc., Tulsa, USA), was utilized to analyze the data. The neural network model was trained using patient information regarding gender, treatment, smoking status, family history, and symptoms of cancer. Results The study employed a multilayer perceptron neural network with a two-phase learning process. The network demonstrated high predictive ability, as indicated by the percentage of correct classifications, which amounted to 87.5%, 89.1%, and 89.9% for the training, validation, and test sets, respectively. Conclusions The findings of this study support the potential usefulness of a neural network-based predictive model in assessing the risk of lung cancer recurrence. Further research is warranted to validate these findings and to explore AI's broader implications in cancer diagnosis and treatment.
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Affiliation(s)
- Andżelika Lorenc
- Department of Biopharmacy, Ludwik Rydygier Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, Bydgoszcz, Poland
| | - Anna Romaszko-Wojtowicz
- Department of Pulmonology, School of Public Health, Collegium Medicum, University of Warmia and Mazury in Olsztyn, Olsztyn, Poland
- The Center for Pulmonary Diseases, Olsztyn, Poland
| | - Łukasz Jaśkiewicz
- Department of Human Physiology and Pathophysiology, School of Medicine, Collegium Medicum, University of Warmia and Mazury in Olsztyn, Olsztyn, Poland
| | - Anna Doboszyńska
- Department of Pulmonology, School of Public Health, Collegium Medicum, University of Warmia and Mazury in Olsztyn, Olsztyn, Poland
- The Center for Pulmonary Diseases, Olsztyn, Poland
| | - Adam Buciński
- Department of Biopharmacy, Ludwik Rydygier Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, Bydgoszcz, Poland
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Pandiyan S, Wang L. A comprehensive review on recent approaches for cancer drug discovery associated with artificial intelligence. Comput Biol Med 2022; 150:106140. [PMID: 36179510 DOI: 10.1016/j.compbiomed.2022.106140] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2022] [Revised: 07/20/2022] [Accepted: 09/18/2022] [Indexed: 11/03/2022]
Abstract
Through the revolutionization of artificial intelligence (AI) technologies in clinical research, significant improvement is observed in diagnosis of cancer. Utilization of these AI technologies, such as machine and deep learning, is imperative for the discovery of novel anticancer drugs and improves existing/ongoing cancer therapeutics. However, building a model for complicated cancers and their types remains a challenge due to lack of effective therapeutics that hinder the establishment of effective computational tools. In this review, we exploit recent approaches and state-of-the-art in implementing AI methods for anticancer drug discovery, and discussed how advances in these applications need to be considered in the current cancer therapeutics. Considering the immense potential of AI, we explore molecular docking and their interactions to recognize metabolic activities that support drug design. Finally, we highlight corresponding strategies in applying machine and deep learning methods to various types of cancer with their pros and cons.
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Affiliation(s)
- Sanjeevi Pandiyan
- Research Center for Intelligent Information Technology, Nantong University, Nantong, China; School of Information Science and Technology, Nantong University, Nantong, China; Nantong Research Institute for Advanced Communication Technologies, Nantong, China
| | - Li Wang
- Research Center for Intelligent Information Technology, Nantong University, Nantong, China; School of Information Science and Technology, Nantong University, Nantong, China; Nantong Research Institute for Advanced Communication Technologies, Nantong, China.
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Jasti VDP, Prasad E, Sawale M, Mewada S, Bangare ML, Bangare PM, Bangare SL, Sammy F. Image Processing and Machine Learning-Based Classification and Detection of Liver Tumor. BIOMED RESEARCH INTERNATIONAL 2022; 2022:3398156. [PMID: 35928918 PMCID: PMC9345695 DOI: 10.1155/2022/3398156] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 07/01/2022] [Accepted: 07/12/2022] [Indexed: 11/21/2022]
Abstract
The liver is in charge of a plethora of tasks that are critical to healthy health. One of these roles is the conversion of food into protein and bile, which are both needed for digestion. Inhaled and possibly harmful chemicals are flushed from the body. It destroys numerous nutrients acquired through the gastrointestinal system and limits the release of cholesterol by utilizing vitamins, carbohydrates, and minerals stored in the liver. The body's tissues are made up of tiny structures known as cells. Cells proliferate and divide in order to create new ones in the normal sequence of events. When an old or damaged cell has to be replaced, a new cell must be synthesized. In other circumstances, the procedure is a total and utter failure. If the tissues of dead or damaged cells that have been cleared from the body are not removed, they may give birth to nodules and tumors. The liver can produce two types of tumors: benign and malignant. Malignant tumors are more dangerous to one's health than benign tumors. This article presents a technique for the classification and identification of liver cancers that is based on image processing and machine learning. The approach may be found here. During the preprocessing stage of picture creation, the fuzzy histogram equalization method is applied in order to bring about a reduction in image noise. After that, the photographs are divided into many parts in order to zero down on the area of interest. For this particular classification task, the RBF-SVM approach, the ANN method, and the random forest method are all applied.
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Affiliation(s)
| | - Enagandula Prasad
- Department of Mathematics, Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering & Technology, Vignana Jyothi Nagar, Pragathi Nagar, Nizampet, Hyderabad, Telangana, India
| | - Manish Sawale
- Department of Electronics and Communication, Oriental Institute of Science and Technology, Bhopal, India
| | - Shivlal Mewada
- Department of Computer Science, Government College, Makdone (Vikram University), Ujjain, India
| | - Manoj L. Bangare
- Department of Information Technology, Smt. Kashibai Navale College of Engineering, Savitribai Phule Pune University, Pune, India
| | - Pushpa M. Bangare
- Department of E&TC, Sinhgad College of Engineering, Savitribai Phule Pune University, Pune, Maharashtra, India
| | - Sunil L. Bangare
- Department of Information Technology, Sinhgad Academy of Engineering, Savitribai Phule Pune University, Pune, India
| | - F. Sammy
- Department of Information Technology, Dambi Dollo University, Dembi Dolo, Welega, Ethiopia
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Luo F, Hong G, Wan Q. Artificial Intelligence in Biomedical Applications of Zirconia. FRONTIERS IN DENTAL MEDICINE 2021. [DOI: 10.3389/fdmed.2021.689288] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Artificial intelligence (AI) is rapidly developed based on computer technology, which can perform tasks that customarily require human intelligence by building intelligent software or machines. As a subfield of AI, machine learning (ML) can learn from the intrinsic statistical patterns and structures in data through algorithms to predict invisible data. With the increasing interest in aesthetics in dentistry, zirconia has drawn lots of attention due to its superior biocompatibility, aesthetically pleasing, high corrosion resistance, good mechanical properties, and absence of reported allergic reactions. The evolution of AI and ML led to the development of novel approaches for the biomedical applications of zirconia in dental devices. AI techniques in zirconia-related research and clinical applications have attracted much attention due to their ability to analyze data and reveal correlations between complex phenomena. The AI applications in the field of zirconia science change according to the application direction of zirconia. Therefore, in this article, we focused on AI in biomedical applications of zirconia in dental devices and AI in zirconia-related applications in dentistry.
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Devi KR, Pradhan J, Bhutia R, Dadul P, Sarkar A, Gohain N, Narain K. Molecular diversity of Mycobacterium tuberculosis complex in Sikkim, India and prediction of dominant spoligotypes using artificial intelligence. Sci Rep 2021; 11:7365. [PMID: 33795751 PMCID: PMC8016865 DOI: 10.1038/s41598-021-86626-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2020] [Accepted: 03/12/2021] [Indexed: 02/06/2023] Open
Abstract
In India, tuberculosis is an enormous public health problem. This study provides the first description of molecular diversity of the Mycobacterium tuberculosis complex (MTBC) from Sikkim, India. A total of 399 Acid Fast Bacilli sputum positive samples were cultured on Lőwenstein-Jensen media and genetic characterisation was done by spoligotyping and 24-loci MIRU-VNTR typing. Spoligotyping revealed the occurrence of 58 different spoligotypes. Beijing spoligotype was the most dominant type constituting 62.41% of the total isolates and was associated with Multiple Drug Resistance. Minimum Spanning tree analysis of 249 Beijing strains based on 24-loci MIRU-VNTR analysis identified 12 clonal complexes (Single Locus Variants). The principal component analysis was used to visualise possible grouping of MTBC isolates from Sikkim belonging to major spoligotypes using 24-MIRU VNTR profiles. Artificial intelligence-based machine learning (ML) methods such as Random Forests (RF), Support Vector Machines (SVM) and Artificial Neural Networks (ANN) were used to predict dominant spoligotypes of MTBC using MIRU-VNTR data. K-fold cross-validation and validation using unseen testing data set revealed high accuracy of ANN, RF, and SVM for predicting Beijing, CAS1_Delhi, and T1 Spoligotypes (93-99%). However, prediction using the external new validation data set revealed that the RF model was more accurate than SVM and ANN.
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Affiliation(s)
- Kangjam Rekha Devi
- grid.420069.90000 0004 1803 0080N.E. Region, Indian Council of Medical Research (ICMR)-Regional Medical Research Centre, Post Box #105, Dibrugarh, Assam 786 001 India
| | - Jagat Pradhan
- National Tuberculosis Elimination Programme (NTEP), Gangtok, Sikkim India
| | - Rinchenla Bhutia
- National Tuberculosis Elimination Programme (NTEP), Gangtok, Sikkim India
| | - Peggy Dadul
- Department of Health Care, Human Services and Family Welfare, State Tuberculosis Control Society, Gangtok, Sikkim India
| | - Atanu Sarkar
- grid.420069.90000 0004 1803 0080N.E. Region, Indian Council of Medical Research (ICMR)-Regional Medical Research Centre, Post Box #105, Dibrugarh, Assam 786 001 India
| | - Nitumoni Gohain
- grid.420069.90000 0004 1803 0080N.E. Region, Indian Council of Medical Research (ICMR)-Regional Medical Research Centre, Post Box #105, Dibrugarh, Assam 786 001 India
| | - Kanwar Narain
- grid.420069.90000 0004 1803 0080N.E. Region, Indian Council of Medical Research (ICMR)-Regional Medical Research Centre, Post Box #105, Dibrugarh, Assam 786 001 India
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