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Chae S, Street WN, Ramaraju N, Gilbertson-White S. Prediction of Cancer Symptom Trajectory Using Longitudinal Electronic Health Record Data and Long Short-Term Memory Neural Network. JCO Clin Cancer Inform 2024; 8:e2300039. [PMID: 38471054 PMCID: PMC10948138 DOI: 10.1200/cci.23.00039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 12/08/2023] [Accepted: 01/26/2024] [Indexed: 03/14/2024] Open
Abstract
PURPOSE Ability to predict symptom severity and progression across treatment trajectories would allow clinicians to provide timely intervention and treatment planning. However, such predictions are difficult because of sparse and inconsistent assessment, and simplistic measures such as the last observed symptom severity are often used. The purpose of this study is to develop a model for predicting future cancer symptom experiences on the basis of past symptom experiences. PATIENTS AND METHODS We performed a retrospective, longitudinal analysis using records of patients with cancer (n = 208) hospitalized between 2008 and 2014. A long short-term memory (LSTM)-based recurrent neural network, a linear regression, and random forest models were trained on previous symptoms experienced and used to predict future symptom trajectories. RESULTS We found that at least one of three tested models (LSTM, linear regression, and random forest) outperform predictions based solely on the previous clinical observation. LSTM models significantly outperformed linear regression and random forest models in predicting nausea (P < .1) and psychosocial status (P < .01). Linear regression outperformed all models when predicting oral health (P < .01), while random forest outperformed all models when predicting mobility (P < .01) and nutrition (P < .01). CONCLUSION We can successfully predict patients' symptom trajectories with a prediction model, built with sparse assessment data, using routinely collected nursing documentation. The results of this project can be applied to better individualize symptom management to support cancer patients' quality of life.
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Affiliation(s)
- Sena Chae
- The University of Iowa College of Nursing, Iowa City, IA
| | - W. Nick Street
- The University of Iowa Tippie College of Business, Iowa City, IA
| | - Naveenkumar Ramaraju
- University of Illinois Urbana-Champaign, Gies College of Business, Champaign, IL
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2
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Ferro M, Crocetto F, Barone B, del Giudice F, Maggi M, Lucarelli G, Busetto GM, Autorino R, Marchioni M, Cantiello F, Crocerossa F, Luzzago S, Piccinelli M, Mistretta FA, Tozzi M, Schips L, Falagario UG, Veccia A, Vartolomei MD, Musi G, de Cobelli O, Montanari E, Tătaru OS. Artificial intelligence and radiomics in evaluation of kidney lesions: a comprehensive literature review. Ther Adv Urol 2023; 15:17562872231164803. [PMID: 37113657 PMCID: PMC10126666 DOI: 10.1177/17562872231164803] [Citation(s) in RCA: 35] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 03/04/2023] [Indexed: 04/29/2023] Open
Abstract
Radiomics and artificial intelligence (AI) may increase the differentiation of benign from malignant kidney lesions, differentiation of angiomyolipoma (AML) from renal cell carcinoma (RCC), differentiation of oncocytoma from RCC, differentiation of different subtypes of RCC, to predict Fuhrman grade, to predict gene mutation through molecular biomarkers and to predict treatment response in metastatic RCC undergoing immunotherapy. Neural networks analyze imaging data. Statistical, geometrical, textural features derived are giving quantitative data of contour, internal heterogeneity and gray zone features of lesions. A comprehensive literature review was performed, until July 2022. Studies investigating the diagnostic value of radiomics in differentiation of renal lesions, grade prediction, gene alterations, molecular biomarkers and ongoing clinical trials have been analyzed. The application of AI and radiomics could lead to improved sensitivity, specificity, accuracy in detecting and differentiating between renal lesions. Standardization of scanner protocols will improve preoperative differentiation between benign, low-risk cancers and clinically significant renal cancers and holds the premises to enhance the diagnostic ability of imaging tools to characterize renal lesions.
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Affiliation(s)
| | - Felice Crocetto
- Department of Neurosciences and Reproductive
Sciences and Odontostomatology, University of Naples Federico II, Naples,
Italy
| | - Biagio Barone
- Department of Neurosciences and Reproductive
Sciences and Odontostomatology, University of Naples Federico II, Naples,
Italy
| | - Francesco del Giudice
- Department of Maternal Infant and Urologic
Sciences, Policlinico Umberto I Hospital, Sapienza University of Rome, Rome,
Italy
| | - Martina Maggi
- Department of Maternal Infant and Urologic
Sciences, Policlinico Umberto I Hospital, Sapienza University of Rome, Rome,
Italy
| | - Giuseppe Lucarelli
- Urology, Andrology and Kidney Transplantation
Unit, Department of Emergency and Organ Transplantation, University of Bari,
Bari, Italy
| | - Gian Maria Busetto
- Department of Urology and Organ
Transplantation, University of Foggia, Foggia, Italy
| | | | - Michele Marchioni
- Department of Medical, Oral and
Biotechnological Sciences, Urology Unit, SS Annunziata Hospital, G.
d’Annunzio University of Chieti, Chieti, Italy
- Department of Urology, ASL Abruzzo 2, Chieti,
Italy
| | - Francesco Cantiello
- Department of Urology, Magna Graecia
University of Catanzaro, Catanzaro, Italy
| | - Fabio Crocerossa
- Department of Urology, Magna Graecia
University of Catanzaro, Catanzaro, Italy
| | - Stefano Luzzago
- Department of Urology, IEO – European
Institute of Oncology, IRCCS – Istituto di Ricovero e Cura a Carattere
Scientifico, Milan, Italy
- Università degli Studi di Milano, Milan,
Italy
| | - Mattia Piccinelli
- Cancer Prognostics and Health Outcomes Unit,
Division of Urology, University of Montréal Health Center, Montréal, QC,
Canada
- Department of Urology, IEO – European
Institute of Oncology, IRCCS – Istituto di Ricovero e Cura a Carattere
Scientifico, Milan, Italy
| | - Francesco Alessandro Mistretta
- Department of Urology, IEO – European
Institute of Oncology, IRCCS – Istituto di Ricovero e Cura a Carattere
Scientifico, Milan, Italy
- Department of Oncology and
Hematology-Oncology, Università degli Studi di Milano, Milan, Italy
| | - Marco Tozzi
- Department of Urology, IEO – European
Institute of Oncology, IRCCS – Istituto di Ricovero e Cura a Carattere
Scientifico, Milan, Italy
- Università degli Studi di Milano, Milan,
Italy
| | - Luigi Schips
- Department of Medical, Oral and
Biotechnological Sciences, Urology Unit, SS Annunziata Hospital, G.
d’Annunzio University of Chieti, Chieti, Italy
| | | | - Alessandro Veccia
- Urology Unit, Azienda Ospedaliera
Universitaria Integrata Verona, University of Verona, Verona, Italy
| | - Mihai Dorin Vartolomei
- Department of Cell and Molecular Biology,
George Emil Palade University of Medicine, Pharmacy, Science and Technology
of Târgu Mures, Târgu Mures, Romania
- Department of Urology, Medical University of
Vienna, Vienna, Austria
| | - Gennaro Musi
- Department of Urology, IEO – European
Institute of Oncology, IRCCS – Istituto di Ricovero e Cura a Carattere
Scientifico, Milan, Italy
- Department of Oncology and
Hematology-Oncology, Università degli Studi di Milano, Milan, Italy
| | - Ottavio de Cobelli
- Department of Urology, IEO – European
Institute of Oncology, IRCCS – Istituto di Ricovero e Cura a Carattere
Scientifico, Milan, Italy
- Department of Oncology and
Hematology-Oncology, Università degli Studi di Milano, Milan, Italy
| | - Emanuele Montanari
- Department of Urology, Foundation IRCCS Ca’
Granda – Ospedale Maggiore Policlinico, Department of Clinical Sciences and
Community Health, University of Milan, Milan, Italy
| | - Octavian Sabin Tătaru
- Institution Organizing University Doctoral
Studies (IOSUD), George Emil Palade University of Medicine, Pharmacy,
Science and Technology of Târgu Mures, Târgu Mures, Romania
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3
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Hong Z, Xu Q, Yan X, Zhang R, Ren Y, Tong Q. Analysis of Signs and Effects of Surgical Breast Cancer Patients Based on Big Data Technology. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:3373553. [PMID: 36188697 PMCID: PMC9525194 DOI: 10.1155/2022/3373553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 09/04/2022] [Accepted: 09/10/2022] [Indexed: 12/24/2022]
Abstract
Big data in health care has gained popularity in recent years for disease prediction. Breast cancer infections are the most common cancer in urban Indian women, as well as women internationally, and are impacted by many events across countries and regions. Breast malignant growth is a notable disease among Indian women. According to the WHO, it represents 14% of all malignant growth tumors in women. A couple of studies have been directed utilizing big data to foresee breast malignant growth. Big data is causing a transformation in healthcare, with better and more ideal results. Monstrous volumes of patient-level data are created by using EHR (Electronic Health Record) systems data because of fast mechanical upgrades. Big data applications in the healthcare business will assist with improving results. Conventional forecast models, then again, are less productive in terms of accuracy and error rate because the exact pace of a specific calculation relies upon different factors such as execution structure, datasets (little or enormous), and kinds of datasets utilized (trait-based or picture based). This audit article looks at complex information mining, AI, and profound learning models utilized for recognizing breast malignant growth. Since "early identification is the way to avoidance in any malignant growth," the motivation behind this audit article is to support the choice of fitting breast disease expectation calculations, explicitly in the big information climate, to convey powerful and productive results. This survey article analyzes the precision paces of perplexing information mining, AI, and profound learning models utilized for distinguishing breast disease on the grounds that the exactness pace of a specific calculation relies upon different factors such as execution structure, datasets (little or enormous), and dataset types (quality based or picture based). The reason for this audit article is to aid the determination of suitable breast disease expectation calculations, explicitly in the big information climate, to convey successful and productive outcomes. Thus, "Early discovery is the way to counteraction in the event of any malignant growth."
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Affiliation(s)
- Zhen Hong
- School of Nursing, Jiangsu Jiankang Vocational College, Nanjing 211800, China
| | - Qin Xu
- School of Nursing, Nanjing Medical University, Nanjing 211166, China
| | - Xin Yan
- School of Nursing, Jiangsu Jiankang Vocational College, Nanjing 211800, China
| | - Ran Zhang
- School of Nursing, Jiangsu Jiankang Vocational College, Nanjing 211800, China
| | - Yuanfang Ren
- Department of Thyroid and Breast Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Qian Tong
- Department of Thyroid and Breast Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
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4
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Dewangan KK, Dewangan DK, Sahu SP, Janghel R. Breast cancer diagnosis in an early stage using novel deep learning with hybrid optimization technique. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 81:13935-13960. [PMID: 35233181 PMCID: PMC8874754 DOI: 10.1007/s11042-022-12385-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 01/17/2022] [Accepted: 01/21/2022] [Indexed: 05/17/2023]
Abstract
Breast cancer is one of the primary causes of death that is occurred in females around the world. So, the recognition and categorization of initial phase breast cancer are necessary to help the patients to have suitable action. However, mammography images provide very low sensitivity and efficiency while detecting breast cancer. Moreover, Magnetic Resonance Imaging (MRI) provides high sensitivity than mammography for predicting breast cancer. In this research, a novel Back Propagation Boosting Recurrent Wienmed model (BPBRW) with Hybrid Krill Herd African Buffalo Optimization (HKH-ABO) mechanism is developed for detecting breast cancer in an earlier stage using breast MRI images. Initially, the MRI breast images are trained to the system, and an innovative Wienmed filter is established for preprocessing the MRI noisy image content. Moreover, the projected BPBRW with HKH-ABO mechanism categorizes the breast cancer tumor as benign and malignant. Additionally, this model is simulated using Python, and the performance of the current research work is evaluated with prevailing works. Hence, the comparative graph shows that the current research model produces improved accuracy of 99.6% with a 0.12% lower error rate.
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Affiliation(s)
- Kranti Kumar Dewangan
- Department of Information Technology, National Institute of Technology, Raipur, Chhatisgarh 492010 India
| | - Deepak Kumar Dewangan
- Department of Information Technology, National Institute of Technology, Raipur, Chhatisgarh 492010 India
| | - Satya Prakash Sahu
- Department of Information Technology, National Institute of Technology, Raipur, Chhatisgarh 492010 India
| | - Rekhram Janghel
- Department of Information Technology, National Institute of Technology, Raipur, Chhatisgarh 492010 India
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5
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Alshayeji MH, Ellethy H, Abed S, Gupta R. Computer-aided detection of breast cancer on the Wisconsin dataset: An artificial neural networks approach. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103141] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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6
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Shang Y, Xu J, Zhang T, Dong Z, Li J, Bi W, Xie Z. Prediction of the Collapse of Necrotic Femoral Head by CT and X-Ray Examinations before Hip Replacement Based on Intelligent Medical Big Data. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:9971236. [PMID: 34976333 PMCID: PMC8716235 DOI: 10.1155/2021/9971236] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 10/19/2021] [Accepted: 10/25/2021] [Indexed: 11/21/2022]
Abstract
It was to explore the effect of the CT and X-ray examinations before the hip replacement to predict the collapse of the necrotic femoral head under the classification of medical big data based on the decision tree algorithm of the difference grey wolf optimization (GWO) and provide a more effective examination basis for the treatment of patients with the osteonecrosis of the femoral head (ONFH). From January 2019 to January 2021, a total of 152,000 patients with ONFH and hip replacement in the tertiary hospitals were enrolled in this study. They were randomly divided into two groups, the study sample-X group (X-ray examination results) and based-CT group (CT examination results)-76,000 cases in each group. The actual measurement results of the femoral head form the gold standard to evaluate the effect of the two groups of detection methods. The measurement results of X-ray and CT before hip replacement are highly consistent with the detection results of the physical femoral head specimens, which can effectively predict the collapse of ONFH and carry out accurate staging. It is worthy of clinical promotion.
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Affiliation(s)
- Yongwei Shang
- The Second Department of Orthopedics, The People Hospital of Shijiazhuang, Shijiazhuang 050000, China
| | - Jianjie Xu
- The Second Department of Orthopedics, The People Hospital of Shijiazhuang, Shijiazhuang 050000, China
| | - Ting Zhang
- Department of Orthopedic Surgery, Third Hospital of Hebei Medical University, Shijiazhuang 050000, China
| | - Zhihui Dong
- The Second Department of Orthopedics, The People Hospital of Shijiazhuang, Shijiazhuang 050000, China
| | - Jiebing Li
- The Second Department of Orthopedics, The People Hospital of Shijiazhuang, Shijiazhuang 050000, China
| | - Weidong Bi
- The Second Department of Orthopedics, The People Hospital of Shijiazhuang, Shijiazhuang 050000, China
| | - Zhe Xie
- The Second Department of Orthopedics, The People Hospital of Shijiazhuang, Shijiazhuang 050000, China
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7
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A survey on artificial intelligence techniques for chronic diseases: open issues and challenges. Artif Intell Rev 2021. [DOI: 10.1007/s10462-021-10084-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
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8
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Zhang L, Zhong Q, Yu Z. Optimization of Tumor Disease Monitoring in Medical Big Data Environment Based on High-Order Simulated Annealing Neural Network Algorithm. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2021; 2021:8996673. [PMID: 34712319 PMCID: PMC8548164 DOI: 10.1155/2021/8996673] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 08/25/2021] [Accepted: 09/01/2021] [Indexed: 12/17/2022]
Abstract
With the development of medical informatization, the data related to medical field are growing at an amazing speed, and medical big data appears. The mining and analysis of these data plays an important role in the prediction, monitoring, diagnosis, and treatment of tumor diseases. Therefore, this paper proposes a clustering algorithm of the high-order simulated annealing neural network algorithm and uses this algorithm to extract tumor disease-related big data, constructs training set according to the relevant information mined, designs a kind of dimension reduction model, aiming at the problem of excessive and wrong diagnosis and treatment in the diagnosis and treatment module of tumor disease monitoring mode, and establishes the corresponding control mechanism, so as to optimize the tumor disease monitoring mode. The results show that the clustering accuracy of the high-order simulated annealing neural network algorithm on different data sets (iris, wine, and Pima India diabetes) is 97.33%, 82.11%, and 70.56% and the execution time is 0.75 s, 0.562 s, and 1.092 s, which are better than those of the fast k-medoids algorithm and improved k-medoids clustering algorithm. To sum up, the high-order simulated annealing neural network algorithm can achieve good clustering effect in medical big data mining. The establishment of model M1 can reduce the probability of excessive and wrong medical treatment and improve the effectiveness of diagnosis and treatment module monitoring in tumor disease monitoring mode.
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Affiliation(s)
- Lei Zhang
- Department of Breast Surgery, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning 110001, China
| | - Qixiang Zhong
- Department of Thoracic Surgery, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning 110001, China
| | - Zhenglun Yu
- Department of Thoracic Surgery, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning 110001, China
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9
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A Review of Artificial Intelligence, Big Data, and Blockchain Technology Applications in Medicine and Global Health. BIG DATA AND COGNITIVE COMPUTING 2021. [DOI: 10.3390/bdcc5030041] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Artificial intelligence (AI) programs are applied to methods such as diagnostic procedures, treatment protocol development, patient monitoring, drug development, personalized medicine in healthcare, and outbreak predictions in global health, as in the case of the current COVID-19 pandemic. Machine learning (ML) is a field of AI that allows computers to learn and improve without being explicitly programmed. ML algorithms can also analyze large amounts of data called Big data through electronic health records for disease prevention and diagnosis. Wearable medical devices are used to continuously monitor an individual’s health status and store it in cloud computing. In the context of a newly published study, the potential benefits of sophisticated data analytics and machine learning are discussed in this review. We have conducted a literature search in all the popular databases such as Web of Science, Scopus, MEDLINE/PubMed and Google Scholar search engines. This paper describes the utilization of concepts underlying ML, big data, blockchain technology and their importance in medicine, healthcare, public health surveillance, case estimations in COVID-19 pandemic and other epidemics. The review also goes through the possible consequences and difficulties for medical practitioners and health technologists in designing futuristic models to improve the quality and well-being of human lives.
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10
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Soellner M, Koenigstorfer J. Compliance with medical recommendations depending on the use of artificial intelligence as a diagnostic method. BMC Med Inform Decis Mak 2021; 21:236. [PMID: 34362359 PMCID: PMC8344186 DOI: 10.1186/s12911-021-01596-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Accepted: 07/28/2021] [Indexed: 12/16/2022] Open
Abstract
Background Advanced analytics, such as artificial intelligence (AI), increasingly gain relevance in medicine. However, patients’ responses to the involvement of AI in the care process remains largely unclear. The study aims to explore whether individuals were more likely to follow a recommendation when a physician used AI in the diagnostic process considering a highly (vs. less) severe disease compared to when the physician did not use AI or when AI fully replaced the physician. Methods Participants from the USA (n = 452) were randomly assigned to a hypothetical scenario where they imagined that they received a treatment recommendation after a skin cancer diagnosis (high vs. low severity) from a physician, a physician using AI, or an automated AI tool. They then indicated their intention to follow the recommendation. Regression analyses were used to test hypotheses. Beta coefficients (ß) describe the nature and strength of relationships between predictors and outcome variables; confidence intervals [CI] excluding zero indicate significant mediation effects. Results The total effects reveal the inferiority of automated AI (ß = .47, p = .001 vs. physician; ß = .49, p = .001 vs. physician using AI). Two pathways increase intention to follow the recommendation. When a physician performs the assessment (vs. automated AI), the perception that the physician is real and present (a concept called social presence) is high, which increases intention to follow the recommendation (ß = .22, 95% CI [.09; 0.39]). When AI performs the assessment (vs. physician only), perceived innovativeness of the method is high, which increases intention to follow the recommendation (ß = .15, 95% CI [− .28; − .04]). When physicians use AI, social presence does not decrease and perceived innovativeness increases. Conclusion Pairing AI with a physician in medical diagnosis and treatment in a hypothetical scenario using topical therapy and oral medication as treatment recommendations leads to a higher intention to follow the recommendation than AI on its own. The findings might help develop practice guidelines for cases where AI involvement benefits outweigh risks, such as using AI in pathology and radiology, to enable augmented human intelligence and inform physicians about diagnoses and treatments. Supplementary Information The online version contains supplementary material available at 10.1186/s12911-021-01596-6.
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Affiliation(s)
- Michaela Soellner
- Chair of Sport and Health Management, Technical University of Munich, Campus D - Uptown Munich, Georg-Brauchle-Ring 60/62, 80992, Munich, Germany
| | - Joerg Koenigstorfer
- Chair of Sport and Health Management, Technical University of Munich, Campus D - Uptown Munich, Georg-Brauchle-Ring 60/62, 80992, Munich, Germany.
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11
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Stephan P, Stephan T, Kannan R, Abraham A. A hybrid artificial bee colony with whale optimization algorithm for improved breast cancer diagnosis. Neural Comput Appl 2021. [DOI: 10.1007/s00521-021-05997-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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12
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Giulietti M, Cecati M, Sabanovic B, Scirè A, Cimadamore A, Santoni M, Montironi R, Piva F. The Role of Artificial Intelligence in the Diagnosis and Prognosis of Renal Cell Tumors. Diagnostics (Basel) 2021; 11:206. [PMID: 33573278 PMCID: PMC7912267 DOI: 10.3390/diagnostics11020206] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 01/22/2021] [Accepted: 01/26/2021] [Indexed: 02/07/2023] Open
Abstract
The increasing availability of molecular data provided by next-generation sequencing (NGS) techniques is allowing improvement in the possibilities of diagnosis and prognosis in renal cancer. Reliable and accurate predictors based on selected gene panels are urgently needed for better stratification of renal cell carcinoma (RCC) patients in order to define a personalized treatment plan. Artificial intelligence (AI) algorithms are currently in development for this purpose. Here, we reviewed studies that developed predictors based on AI algorithms for diagnosis and prognosis in renal cancer and we compared them with non-AI-based predictors. Comparing study results, it emerges that the AI prediction performance is good and slightly better than non-AI-based ones. However, there have been only minor improvements in AI predictors in terms of accuracy and the area under the receiver operating curve (AUC) over the last decade and the number of genes used had little influence on these indices. Furthermore, we highlight that different studies having the same goal obtain similar performance despite the fact they use different discriminating genes. This is surprising because genes related to the diagnosis or prognosis are expected to be tumor-specific and independent of selection methods and algorithms. The performance of these predictors will be better with the improvement in the learning methods, as the number of cases increases and by using different types of input data (e.g., non-coding RNAs, proteomic and metabolic). This will allow for more precise identification, classification and staging of cancerous lesions which will be less affected by interpathologist variability.
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Affiliation(s)
- Matteo Giulietti
- Department of Specialistic Clinical & Odontostomatological Sciences, Polytechnic University of Marche, 60126 Ancona, Italy; (M.G.); (M.C.); (B.S.)
| | - Monia Cecati
- Department of Specialistic Clinical & Odontostomatological Sciences, Polytechnic University of Marche, 60126 Ancona, Italy; (M.G.); (M.C.); (B.S.)
| | - Berina Sabanovic
- Department of Specialistic Clinical & Odontostomatological Sciences, Polytechnic University of Marche, 60126 Ancona, Italy; (M.G.); (M.C.); (B.S.)
| | - Andrea Scirè
- Department of Life and Environmental Sciences, Polytechnic University of Marche, 60126 Ancona, Italy;
| | - Alessia Cimadamore
- Section of Pathological Anatomy, Polytechnic University of Marche, United Hospitals, 60126 Ancona, Italy; (A.C.); (R.M.)
| | - Matteo Santoni
- Oncology Unit, Macerata Hospital, 62012 Macerata, Italy;
| | - Rodolfo Montironi
- Section of Pathological Anatomy, Polytechnic University of Marche, United Hospitals, 60126 Ancona, Italy; (A.C.); (R.M.)
| | - Francesco Piva
- Department of Specialistic Clinical & Odontostomatological Sciences, Polytechnic University of Marche, 60126 Ancona, Italy; (M.G.); (M.C.); (B.S.)
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