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Kalidindi S. The Role of Artificial Intelligence in the Diagnosis of Melanoma. Cureus 2024; 16:e69818. [PMID: 39308840 PMCID: PMC11415605 DOI: 10.7759/cureus.69818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/20/2024] [Indexed: 09/25/2024] Open
Abstract
The incidence of melanoma, the most aggressive form of skin cancer, continues to rise globally, particularly among fair-skinned populations (type I and II). Early detection is crucial for improving patient outcomes, and recent advancements in artificial intelligence (AI) have shown promise in enhancing the accuracy and efficiency of melanoma diagnosis and management. This review examines the role of AI in skin lesion diagnostics, highlighting two main approaches: machine learning, particularly convolutional neural networks (CNNs), and expert systems. AI techniques have demonstrated high accuracy in classifying dermoscopic images, often matching or surpassing dermatologists' performance. Integrating AI into dermatology has improved tasks, such as lesion classification, segmentation, and risk prediction, facilitating earlier and more accurate interventions. Despite these advancements, challenges remain, including biases in training data, interpretability issues, and integration of AI into clinical workflows. Ensuring diverse data representation and maintaining high standards of image quality are essential for reliable AI performance. Future directions involve the development of more sophisticated models, such as vision-language and multimodal models, and federated learning to address data privacy and generalizability concerns. Continuous validation and ethical integration of AI into clinical practice are vital for realizing its full potential for improving melanoma diagnosis and patient care.
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Affiliation(s)
- Sadhana Kalidindi
- Clinical Research, Apollo Radiology International Academy, Hyderabad, IND
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Palmieri F, Akhtar NF, Pané A, Jiménez A, Olbeyra RP, Viaplana J, Vidal J, de Hollanda A, Gama-Perez P, Jiménez-Chillarón JC, Garcia-Roves PM. Machine learning allows robust classification of visceral fat in women with obesity using common laboratory metrics. Sci Rep 2024; 14:17263. [PMID: 39068287 PMCID: PMC11283481 DOI: 10.1038/s41598-024-68269-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Accepted: 07/22/2024] [Indexed: 07/30/2024] Open
Abstract
The excessive accumulation and malfunctioning of visceral adipose tissue (VAT) is a major determinant of increased risk of obesity-related comorbidities. Thus, risk stratification of people living with obesity according to their amount of VAT is of clinical interest. Currently, the most common VAT measurement methods include mathematical formulae based on anthropometric dimensions, often biased by human measurement errors, bio-impedance, and image techniques such as X-ray absorptiometry (DXA) analysis, which requires specialized equipment. However, previous studies showed the possibility of classifying people living with obesity according to their VAT through blood chemical concentrations by applying machine learning techniques. In addition, most of the efforts were spent on men living with obesity while little was done for women. Therefore, this study aims to compare the performance of the multilinear regression model (MLR) in estimating VAT and six different supervised machine learning classifiers, including logistic regression (LR), support vector machine and decision tree-based models, to categorize 149 women living with obesity. For clustering, the study population was categorized into classes 0, 1, and 2 according to their VAT and the accuracy of each MLR and classification model was evaluated using DXA-data (DXAdata), blood chemical concentrations (BLDdata), and both DXAdata and BLDdata together (ALLdata). Estimation error and R 2 were computed for MLR, while receiver operating characteristic (ROC) and precision-recall curves (PR) area under the curve (AUC) were used to assess the performance of every classification model. MLR models showed a poor ability to estimate VAT with mean absolute error ≥ 401.40 andR 2 ≤ 0.62 in all the datasets. The highest accuracy was found for LR with values of 0.57, 0.63, and 0.53 for ALLdata, DXAdata, and BLDdata, respectively. The ROC AUC showed a poor ability of both ALLdata and DXAdata to distinguish class 1 from classes 0 and 2 (AUC = 0.31, 0.71, and 0.85, respectively) as also confirmed by PR (AUC = 0.24, 0.57, and 0.73, respectively). However, improved performances were obtained when applying LR model to BLDdata (ROC AUC ≥ 0.61 and PR AUC ≥ 0.42), especially for class 1. These results seem to suggest that, while a direct and reliable estimation of VAT was not possible in our cohort, blood sample-derived information can robustly classify women living with obesity by machine learning-based classifiers, a fact that could benefit the clinical practice, especially in those health centres where medical imaging devices are not available. Nonetheless, these promising findings should be further validated over a larger population.
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Affiliation(s)
- Flavio Palmieri
- Biophysics unit, Department of Physiological Sciences, Faculty of Medicine and Health, Universitat de Barcelona, Bellvitge campus, 08907, Barcelona, Spain.
- Nutrition, Metabolism and Gene Therapy Group; Diabetes and Metabolism Program; Bellvitge Biomedical Research Institute (IDIBELL), 08908, Barcelona, Spain.
| | - Nidà Farooq Akhtar
- Escola d'Enginyeria de Barcelona Est (EEBE) Universitat Politècnica De Catalunya. Barcelona Tech-UPC, 08019, Barcelona, Spain
| | - Adriana Pané
- Obesity Unit, Endocrinology and Nutrition Department, Hospital Clínic de Barcelona, 08036, Barcelona, Spain
- Centro de Investigación Biomédica en Red de la Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029, Madrid, Spain
| | - Amanda Jiménez
- Obesity Unit, Endocrinology and Nutrition Department, Hospital Clínic de Barcelona, 08036, Barcelona, Spain
- Centro de Investigación Biomédica en Red de la Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029, Madrid, Spain
- Fundació Clínic per a la Recerca Biomèdica (FCRB)-Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), 08036, Barcelona, Spain
| | - Romina Paula Olbeyra
- Fundació Clínic per a la Recerca Biomèdica (FCRB)-Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), 08036, Barcelona, Spain
| | - Judith Viaplana
- Fundació Clínic per a la Recerca Biomèdica (FCRB)-Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), 08036, Barcelona, Spain
| | - Josep Vidal
- Obesity Unit, Endocrinology and Nutrition Department, Hospital Clínic de Barcelona, 08036, Barcelona, Spain
- Fundació Clínic per a la Recerca Biomèdica (FCRB)-Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), 08036, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III (ISCIII), 28029, Madrid, Spain
| | - Ana de Hollanda
- Obesity Unit, Endocrinology and Nutrition Department, Hospital Clínic de Barcelona, 08036, Barcelona, Spain
- Centro de Investigación Biomédica en Red de la Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029, Madrid, Spain
- Fundació Clínic per a la Recerca Biomèdica (FCRB)-Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), 08036, Barcelona, Spain
| | - Pau Gama-Perez
- Biophysics unit, Department of Physiological Sciences, Faculty of Medicine and Health, Universitat de Barcelona, Bellvitge campus, 08907, Barcelona, Spain
| | - Josep C Jiménez-Chillarón
- Biophysics unit, Department of Physiological Sciences, Faculty of Medicine and Health, Universitat de Barcelona, Bellvitge campus, 08907, Barcelona, Spain
- Metabolic diseases of pediatric origin unit, Institut de Recerca Sant Joan de Déu - Barcelona Children's Hospital, 08950, Esplugues del Llobregat, Spain
| | - Pablo M Garcia-Roves
- Biophysics unit, Department of Physiological Sciences, Faculty of Medicine and Health, Universitat de Barcelona, Bellvitge campus, 08907, Barcelona, Spain.
- Nutrition, Metabolism and Gene Therapy Group; Diabetes and Metabolism Program; Bellvitge Biomedical Research Institute (IDIBELL), 08908, Barcelona, Spain.
- Centro de Investigación Biomédica en Red de la Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029, Madrid, Spain.
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Zainal NH, Newman MG. Which client with generalized anxiety disorder benefits from a mindfulness ecological momentary intervention versus a self-monitoring app? Developing a multivariable machine learning predictive model. J Anxiety Disord 2024; 102:102825. [PMID: 38245961 PMCID: PMC10922999 DOI: 10.1016/j.janxdis.2024.102825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 12/26/2023] [Accepted: 01/02/2024] [Indexed: 01/23/2024]
Abstract
Precision medicine methods (machine learning; ML) can identify which clients with generalized anxiety disorder (GAD) benefit from mindfulness ecological momentary intervention (MEMI) vs. self-monitoring app (SM). We used randomized controlled trial data of MEMI vs. SM for GAD (N = 110) and tested three ML models to predict one-month follow-up reliable improvement in GAD severity, perseverative cognitions (PC), trait mindfulness (TM), and executive function (EF). Eleven baseline predictors were tested regarding differential reliable change from MEMI vs. SM (age, sex, race, EF errors, inhibitory dyscontrol, set-shifting deficits, verbal fluency, working memory, GAD severity, TM, PC). The final top five prescriptive predictor models of all outcomes performed well (AUC = .752 .886). The following variables predicted better outcome from MEMI vs. SM: Higher GAD severity predicted more GAD improvement but less EF improvement. Elevated PC, inhibitory dyscontrol, and verbal dysfluency predicted better improvement in most outcomes. Greater set-shifting and TM predicted stronger improvements in GAD symptoms and TM. Older age predicted more alleviation of GAD and PC symptoms. Women exhibited more enhancements in trait mindfulness and EF than men. White individuals benefitted more than non-White. PC, TM, EF, and sociodemographic data might help predictive models optimize intervention selection for GAD.
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Affiliation(s)
- Nur Hani Zainal
- Harvard Medical School, Boston, MA, USA; National University of Singapore, Kent Ridge, Singapore.
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Zhang L, Liu Y, Wang K, Ou X, Zhou J, Zhang H, Huang M, Du Z, Qiang S. Integration of machine learning to identify diagnostic genes in leukocytes for acute myocardial infarction patients. J Transl Med 2023; 21:761. [PMID: 37891664 PMCID: PMC10612217 DOI: 10.1186/s12967-023-04573-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 09/25/2023] [Indexed: 10/29/2023] Open
Abstract
BACKGROUND Acute myocardial infarction (AMI) has two clinical characteristics: high missed diagnosis and dysfunction of leukocytes. Transcriptional RNA on leukocytes is closely related to the course evolution of AMI patients. We hypothesized that transcriptional RNA in leukocytes might provide potential diagnostic value for AMI. Integration machine learning (IML) was first used to explore AMI discrimination genes. The following clinical study was performed to validate the results. METHODS A total of four AMI microarrays (derived from the Gene Expression Omnibus) were included in bioanalysis (220 sample size). Then, the clinical validation was finished with 20 AMI and 20 stable coronary artery disease patients (SCAD). At a ratio of 5:2, GSE59867 was included in the training set, while GSE60993, GSE62646, and GSE48060 were included in the testing set. IML was explicitly proposed in this research, which is composed of six machine learning algorithms, including support vector machine (SVM), neural network (NN), random forest (RF), gradient boosting machine (GBM), decision trees (DT), and least absolute shrinkage and selection operator (LASSO). IML had two functions in this research: filtered optimized variables and predicted the categorized value. Finally, The RNA of the recruited patients was analyzed to verify the results of IML. RESULTS Thirty-nine differentially expressed genes (DEGs) were identified between controls and AMI individuals from the training sets. Among the thirty-nine DEGs, IML was used to process the predicted classification model and identify potential candidate genes with overall normalized weights > 1. Finally, two genes (AQP9 and SOCS3) show their diagnosis value with the area under the curve (AUC) > 0.9 in both the training and testing sets. The clinical study verified the significance of AQP9 and SOCS3. Notably, more stenotic coronary arteries or severe Killip classification indicated higher levels of these two genes, especially SOCS3. These two genes correlated with two immune cell types, monocytes and neutrophils. CONCLUSION AQP9 and SOCS3 in leukocytes may be conducive to identifying AMI patients with SCAD patients. AQP9 and SOCS3 are closely associated with monocytes and neutrophils, which might contribute to advancing AMI diagnosis and shed light on novel genetic markers. Multiple clinical characteristics, multicenter, and large-sample relevant trials are still needed to confirm its clinical value.
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Affiliation(s)
- Lin Zhang
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin, 301617, People's Republic of China
| | - Yue Liu
- Department of Nephropathy, Zhangjiagang TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Zhangjiagang, 215600, Jiangsu, People's Republic of China
| | - Kaiyue Wang
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin, 301617, People's Republic of China
| | - Xiangqin Ou
- The First Affiliated Hospital of Guizhou, University of Traditional Chinese Medicine, Guiyang, 550025, Guizhou, People's Republic of China
| | - Jiashun Zhou
- Tianjin Jinghai District Hospital, 14 Shengli Road, Jinghai, Tianjin, 301699, People's Republic of China
| | - Houliang Zhang
- Tianjin Jinghai District Hospital, 14 Shengli Road, Jinghai, Tianjin, 301699, People's Republic of China
| | - Min Huang
- Department of Nephropathy, Zhangjiagang TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Zhangjiagang, 215600, Jiangsu, People's Republic of China
| | - Zhenfang Du
- Department of Nephropathy, Zhangjiagang TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Zhangjiagang, 215600, Jiangsu, People's Republic of China.
| | - Sheng Qiang
- Department of Nephropathy, Zhangjiagang TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Zhangjiagang, 215600, Jiangsu, People's Republic of China.
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Elshahawy M, Elnemr A, Oproescu M, Schiopu AG, Elgarayhi A, Elmogy MM, Sallah M. Early Melanoma Detection Based on a Hybrid YOLOv5 and ResNet Technique. Diagnostics (Basel) 2023; 13:2804. [PMID: 37685342 PMCID: PMC10486497 DOI: 10.3390/diagnostics13172804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 08/11/2023] [Accepted: 08/22/2023] [Indexed: 09/10/2023] Open
Abstract
Skin cancer, specifically melanoma, is a serious health issue that arises from the melanocytes, the cells that produce melanin, the pigment responsible for skin color. With skin cancer on the rise, the timely identification of skin lesions is crucial for effective treatment. However, the similarity between some skin lesions can result in misclassification, which is a significant problem. It is important to note that benign skin lesions are more prevalent than malignant ones, which can lead to overly cautious algorithms and incorrect results. As a solution, researchers are developing computer-assisted diagnostic tools to detect malignant tumors early. First, a new model based on the combination of "you only look once" (YOLOv5) and "ResNet50" is proposed for melanoma detection with its degree using humans against a machine with 10,000 training images (HAM10000). Second, feature maps integrate gradient change, which allows rapid inference, boosts precision, and reduces the number of hyperparameters in the model, making it smaller. Finally, the current YOLOv5 model is changed to obtain the desired outcomes by adding new classes for dermatoscopic images of typical lesions with pigmented skin. The proposed approach improves melanoma detection with a real-time speed of 0.4 MS of non-maximum suppression (NMS) per image. The performance metrics average is 99.0%, 98.6%, 98.8%, 99.5, 98.3%, and 98.7% for the precision, recall, dice similarity coefficient (DSC), accuracy, mean average precision (MAP) from 0.0 to 0.5, and MAP from 0.5 to 0.95, respectively. Compared to current melanoma detection approaches, the provided approach is more efficient in using deep features.
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Affiliation(s)
- Manar Elshahawy
- Information Technology Department, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt;
| | - Ahmed Elnemr
- Applied Mathematical Physics Research Group, Physics Department, Faculty of Science, Mansoura University, Mansoura 35516, Egypt; (A.E.); (A.E.)
| | - Mihai Oproescu
- Faculty of Electronics, Communication, and Computer Science, University of Pitesti, 110040 Pitesti, Romania
| | - Adriana-Gabriela Schiopu
- Department of Manufacturing and Industrial Management, Faculty of Mechanics and Technology, University of Pitesti, 110040 Pitesti, Romania;
| | - Ahmed Elgarayhi
- Applied Mathematical Physics Research Group, Physics Department, Faculty of Science, Mansoura University, Mansoura 35516, Egypt; (A.E.); (A.E.)
| | - Mohammed M. Elmogy
- Information Technology Department, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt;
| | - Mohammed Sallah
- Department of Physics, College of Sciences, University of Bisha, P.O. Box 344, Bisha 61922, Saudi Arabia;
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Zhang L, Lin Y, Wang K, Han L, Zhang X, Gao X, Li Z, Zhang H, Zhou J, Yu H, Fu X. Multiple-model machine learning identifies potential functional genes in dilated cardiomyopathy. Front Cardiovasc Med 2023; 9:1044443. [PMID: 36712235 PMCID: PMC9874116 DOI: 10.3389/fcvm.2022.1044443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 12/22/2022] [Indexed: 01/12/2023] Open
Abstract
Introduction Machine learning (ML) has gained intensive popularity in various fields, such as disease diagnosis in healthcare. However, it has limitation for single algorithm to explore the diagnosing value of dilated cardiomyopathy (DCM). We aim to develop a novel overall normalized sum weight of multiple-model MLs to assess the diagnosing value in DCM. Methods Gene expression data were selected from previously published databases (six sets of eligible microarrays, 386 samples) with eligible criteria. Two sets of microarrays were used as training; the others were studied in the testing sets (ratio 5:1). Totally, we identified 20 differently expressed genes (DEGs) between DCM and control individuals (7 upregulated and 13 down-regulated). Results We developed six classification ML methods to identify potential candidate genes based on their overall weights. Three genes, serine proteinase inhibitor A3 (SERPINA3), frizzled-related proteins (FRPs) 3 (FRZB), and ficolin 3 (FCN3) were finally identified as the receiver operating characteristic (ROC). Interestingly, we found all three genes correlated considerably with plasma cells. Importantly, not only in training sets but also testing sets, the areas under the curve (AUCs) for SERPINA3, FRZB, and FCN3 were greater than 0.88. The ROC of SERPINA3 was significantly high (0.940 in training and 0.918 in testing sets), indicating it is a potentially functional gene in DCM. Especially, the plasma levels in DCM patients of SERPINA3, FCN, and FRZB were significant compared with healthy control. Discussion SERPINA3, FRZB, and FCN3 might be potential diagnosis targets for DCM, Further verification work could be implemented.
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Affiliation(s)
- Lin Zhang
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Yexiang Lin
- Biomedical Engineering, Imperial College London, London, United Kingdom
| | - Kaiyue Wang
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Lifeng Han
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Xue Zhang
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Xiumei Gao
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Zheng Li
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | | | - Jiashun Zhou
- Tianjin Jinghai District Hospital, Tianjin, China
| | - Heshui Yu
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China,*Correspondence: Heshui Yu,
| | - Xuebin Fu
- Department of Cardiovascular-Thoracic Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL, United States,Department of Pediatrics, Ann & Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL, United States,Xuebin Fu,
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Yue G, Wei P, Zhou T, Jiang Q, Yan W, Wang T. Toward Multicenter Skin Lesion Classification Using Deep Neural Network With Adaptively Weighted Balance Loss. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:119-131. [PMID: 36063522 DOI: 10.1109/tmi.2022.3204646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Recently, deep neural network-based methods have shown promising advantages in accurately recognizing skin lesions from dermoscopic images. However, most existing works focus more on improving the network framework for better feature representation but ignore the data imbalance issue, limiting their flexibility and accuracy across multiple scenarios in multi-center clinics. Generally, different clinical centers have different data distributions, which presents challenging requirements for the network's flexibility and accuracy. In this paper, we divert the attention from framework improvement to the data imbalance issue and propose a new solution for multi-center skin lesion classification by introducing a novel adaptively weighted balance (AWB) loss to the conventional classification network. Benefiting from AWB, the proposed solution has the following advantages: 1) it is easy to satisfy different practical requirements by only changing the backbone; 2) it is user-friendly with no tuning on hyperparameters; and 3) it adaptively enables small intraclass compactness and pays more attention to the minority class. Extensive experiments demonstrate that, compared with solutions equipped with state-of-the-art loss functions, the proposed solution is more flexible and more competent for tackling the multi-center imbalanced skin lesion classification task with considerable performance on two benchmark datasets. In addition, the proposed solution is proved to be effective in handling the imbalanced gastrointestinal disease classification task and the imbalanced DR grading task. Code is available at https://github.com/Weipeishan2021.
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Kye S, Lee O. Skin color classification of Koreans using clustering. Skin Res Technol 2022; 28:796-803. [PMID: 36082490 PMCID: PMC9907718 DOI: 10.1111/srt.13201] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 08/09/2022] [Indexed: 11/30/2022]
Abstract
BACKGROUND/PURPOSE Skin color is used as an index for diagnosing and predicting skin irritation, dermatitis, and skin conditions because skin color changes based on various factors. Therefore, a new method for consistently and accurately evaluating skin color while overcoming the limitations of the existing skin color evaluation method was proposed, and its usefulness was demonstrated. METHODS Skin color was quantified using the RGB (Red, Green, Blue), HSV (Hue Saturation Value), CIELab, and YCbCr color spaces in the acquired Korean skin images, which were classified through clustering. In addition, the classification performances of the existing visual scoring method and the proposed skin color classification method were compared and analyzed using multinomial logistic regression, support vector machine, K-nearest neighbor, and random forest. RESULTS After quantifying the skin color through the color space conversion of the skin image, the skin color classification performance according to the number of quantified features and the classifier was verified. In addition, the usefulness of the proposed classification method was confirmed by comparing its classification performance with that of the existing skin color classification method. CONCLUSION In this study, a method was proposed to objectively classify skin color values quantified from skin images of Koreans acquired using a digital camera through clustering. To verify the proposed method, its classification performance was compared with that of the existing classification method, and an optimized classification method was presented for the classification of Korean skin color. Thus, the proposed method can objectively classify skin color and can be used as a cornerstone in research to quantify skin color and establish objective classification criteria.
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Affiliation(s)
- Seula Kye
- Department of Software Convergence, Graduate School, Soonchunhyang University, Asan City, Republic of Korea
| | - Onseok Lee
- Department of Software Convergence, Graduate School, Soonchunhyang University, Asan City, Republic of Korea.,Department of Medical IT Engineering, College of Medical Sciences, Soonchunhyang University, Asan City, Republic of Korea
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9
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Enhanced deep bottleneck transformer model for skin lesion classification. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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10
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Santos K, Dias JP, Amado C. A literature review of machine learning algorithms for crash injury severity prediction. JOURNAL OF SAFETY RESEARCH 2022; 80:254-269. [PMID: 35249605 DOI: 10.1016/j.jsr.2021.12.007] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 07/21/2021] [Accepted: 12/08/2021] [Indexed: 06/14/2023]
Abstract
INTRODUCTION Road traffic crashes represent a major public health concern, so it is of significant importance to understand the factors associated with the increase of injury severity of its interveners when involved in a road crash. Determining such factors is essential to help decision making in road safety management, improving road safety, and reducing the severity of future crashes. METHOD This paper presents a recent literature review of the methods that have been applied to road crash injury severity modeling. It includes 56 studies from 2001 to 2021 that consider more than 20 different statistical or machine learning techniques. RESULTS Random Forest was the algorithm with the best results, achieving the best performance in 70% of the times that it was applied and in 29% of all studies. Support Vector Machine and Decision Tree achieved the best performance in 53% and 31% of the times and in 16% and 14% of all studies, respectively. Bayesian Networks and K-Nearest Neighbors achieved the best performance in 67% and 40% of the times that were used but only achieved the best performance in 4% and 7% of all the studies analyzed, respectively. CONCLUSIONS At this point, Random Forest revealed to be a good approach for road traffic crash injury severity prediction followed by Support Vector Machine, Decision Tree, and K-Nearest Neighbor. However, there is still a lot of room in this area to explore other techniques that can best suit this purpose as not only the model's performance should be considered but also causality issues, unobserved heterogeneity, and temporal instability. Practical Applications: This review enables researchers to understand the recent techniques applied in the analysis of injury severity modeling, and the ones that achieved the best performance results. Based on the reviewed studies, challenges and future research directions are presented.
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Affiliation(s)
- Kenny Santos
- IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal.
| | - João P Dias
- IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal.
| | - Conceição Amado
- Department of Mathematics and CEMAT, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
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Stiff KM, Franklin MJ, Zhou Y, Madabhushi A, Knackstedt TJ. Artificial Intelligence and Melanoma: A Comprehensive Review of Clinical, Dermoscopic, and Histologic Applications. Pigment Cell Melanoma Res 2022; 35:203-211. [PMID: 35038383 DOI: 10.1111/pcmr.13027] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 11/24/2021] [Accepted: 01/09/2022] [Indexed: 11/30/2022]
Abstract
Melanoma detection, prognosis, and treatment represent challenging and complex areas of cutaneous oncology with considerable impact on patient outcomes and healthcare economics. Artificial intelligence (AI) applications in these tasks are rapidly developing. Neural networks with increasing levels of sophistication are being implemented in clinical image, dermoscopic image, and histopathologic specimen classification of pigmented lesions. These efforts hold promise of earlier and highly accurate melanoma detection, as well as reliable prognostication and prediction of therapeutic response. Herein, we provide a brief introduction to AI, discuss contemporary investigational applications of AI in melanoma, and summarize challenges encountered with AI.
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Affiliation(s)
| | | | - Yufei Zhou
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland
| | - Thomas J Knackstedt
- Department of Dermatology, MetroHealth System, Cleveland.,School of Medicine, Case Western Reserve University, Cleveland
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Pathania YS, Apalla Z, Salerni G, Patil A, Grabbe S, Goldust M. Non-invasive diagnostic techniques in pigmentary skin disorders and skin cancer. J Cosmet Dermatol 2021; 21:444-450. [PMID: 34724325 DOI: 10.1111/jocd.14547] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 10/04/2021] [Accepted: 10/11/2021] [Indexed: 11/28/2022]
Abstract
BACKGROUND Diagnosis of pigmentary skin disorders, pre-cancerous and cancerous skin diseases is traditionally relied on visual assessment. The most widely applied invasive diagnostic technique is the skin biopsy. There have been significant technological advances in non-invasive diagnostic methods for skin disorders. OBJECTIVE The objective of this article is to discuss different non-invasive diagnostic modalities, used in the diagnosis of pigmentary skin disorders and cutaneous cancers. METHODS Comprehensive literature search was performed to screen articles related to non-invasive diagnostic techniques in pigmentary skin disorders and cutaneous cancers. Articles published in journals indexed in PubMed were searched along with those in Google Scholar. Clinical trials, review articles, case series, case reports and other relevant articles were considered for review. References of relevant articles were also considered for review. RESULTS Dermoscopy and ultrasonography were the only non-invasive diagnostic and imaging techniques available to dermatologists for many years. The advent of computed tomography (CT) and magnetic resonance imaging (MRI) augmented the visualization of deeper structures. Confocal laser microscopy (CLM) and reflectance spectrophotometers have showed promising results in the non-invasive detection of pigmented lesions. Optical coherence tomography (OCT), electrical impedance spectroscopy (EIS), multispectral imaging, high frequency ultrasonography (HFUS) and adhesive patch biopsy aid in the accurate diagnosis of benign, as well as neoplastic skin diseases. CONCLUSION There have been significant advancements in non-invasive methods for diagnosis of dermatological diseases. These techniques can be repeatedly used in a comfort manner for the patient, and may offer an objective way to follow the course of a disease.
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Affiliation(s)
- Yashdeep Singh Pathania
- Department of Dermatology, Venereology and Leprology, All India Institute of Medical Sciences, Jodhpur, India
| | - Zoe Apalla
- Second Dermatology Department, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Gabriel Salerni
- Department of Dermatology, Hospital Provincial del Centenario de Rosario-Universidad Nacional de Rosario, Rosario, Argentina
| | - Anant Patil
- Department of Pharmacology, Dr. DY Patil Medical College, Navi Mumbai, India
| | - Stephan Grabbe
- Department of Dermatology, University Medical Center of the Johannes Gutenberg University, Mainz, Germany
| | - Mohamad Goldust
- Department of Dermatology, University Medical Center Mainz, Mainz, Germany
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Shemshaki G, Murthy ASN, Malini SS. Assessment and Establishment of Correlation between Reactive Oxidation Species, Citric Acid, and Fructose Level in Infertile Male Individuals: A Machine-Learning Approach. J Hum Reprod Sci 2021; 14:129-136. [PMID: 34316227 PMCID: PMC8279050 DOI: 10.4103/jhrs.jhrs_26_21] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2021] [Revised: 02/14/2021] [Accepted: 03/23/2021] [Indexed: 12/15/2022] Open
Abstract
Background: Biochemical complexity of seminal plasma and obesity has an important role in male infertility (MI); so far, it has not been possible to provide evidence of clinical significance for all of them. Aims: Our goal here is to evaluate the correlation between biochemical markers with semen parameters, which might play a role in MI. Study Setting and Design: We enlisted 100 infertile men as patients and 50 fertile men as controls to evaluate the sperm parameters and biochemical markers in ascertaining MI. Materials and Methods: Semen analyses, seminal fructose, citric acid, and reactive oxidation species (ROS) were measured in 100 patients and 50 controls. Statistical Analysis: Descriptive statistics, an independent t-test, Pearson correlation, and machine-learning approaches were used to integrate the various biochemical and seminal parameters measured to quantify the inter-relatedness between these measurements. Results: Pearson correlation results showed a significant positive correlation between body mass index (BMI) and fructose levels. Citric acid had a positive correlation with sperm count, morphology, motility, and volume but displayed a negative correlation with BMI and basal metabolic rate (BMR). However, BMI and BMR had a positive correlation with ROS. Sperm count, morphology, and motility were negative correlations with ROS. The machine-learning approach detected that pH was the most critical parameter with an inverse effect on citric acid, and BMI and motility were the most critical parameter for ROS. Conclusion: We recommend that evaluation of biochemical markers of seminal fluid may benefit in understanding the etiology of MI based on the functionality of accessory glands and ROS levels.
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Affiliation(s)
- Golnaz Shemshaki
- Department of Studies in Zoology, University of Mysore, Mysore, Karnataka, India
| | | | - Suttur S Malini
- Department of Studies in Zoology, University of Mysore, Mysore, Karnataka, India
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14
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A Hybrid Deep CNN Model for Abnormal Arrhythmia Detection Based on Cardiac ECG Signal. SENSORS 2021; 21:s21030951. [PMID: 33535397 PMCID: PMC7867037 DOI: 10.3390/s21030951] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/13/2020] [Revised: 01/09/2021] [Accepted: 01/15/2021] [Indexed: 11/21/2022]
Abstract
Electrocardiogram (ECG) signals play a vital role in diagnosing and monitoring patients suffering from various cardiovascular diseases (CVDs). This research aims to develop a robust algorithm that can accurately classify the electrocardiogram signal even in the presence of environmental noise. A one-dimensional convolutional neural network (CNN) with two convolutional layers, two down-sampling layers, and a fully connected layer is proposed in this work. The same 1D data was transformed into two-dimensional (2D) images to improve the model’s classification accuracy. Then, we applied the 2D CNN model consisting of input and output layers, three 2D-convolutional layers, three down-sampling layers, and a fully connected layer. The classification accuracy of 97.38% and 99.02% is achieved with the proposed 1D and 2D model when tested on the publicly available Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database. Both proposed 1D and 2D CNN models outperformed the corresponding state-of-the-art classification algorithms for the same data, which validates the proposed models’ effectiveness.
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15
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Lopez-Castroman J, Abad-Tortosa D, Cobo Aguilera A, Courtet P, Barrigón ML, Artés A, Baca-García E. Psychiatric Profiles of eHealth Users Evaluated Using Data Mining Techniques: Cohort Study. JMIR Ment Health 2021; 8:e17116. [PMID: 33470943 PMCID: PMC7857940 DOI: 10.2196/17116] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Revised: 03/31/2020] [Accepted: 04/05/2020] [Indexed: 01/17/2023] Open
Abstract
BACKGROUND New technologies are changing access to medical records and the relationship between physicians and patients. Professionals can now use e-mental health tools to provide prompt and personalized responses to patients with mental illness. However, there is a lack of knowledge about the digital phenotypes of patients who use e-mental health apps. OBJECTIVE This study aimed to reveal the profiles of users of a mental health app through machine learning techniques. METHODS We applied a nonparametric model, the Sparse Poisson Factorization Model, to discover latent features in the response patterns of 2254 psychiatric outpatients to a short self-assessment on general health. The assessment was completed through a mental health app after the first login. RESULTS The results showed the following four different profiles of patients: (1) all patients had feelings of worthlessness, aggressiveness, and suicidal ideas; (2) one in four reported low energy and difficulties to cope with problems; (3) less than a quarter described depressive symptoms with extremely high scores in suicidal thoughts and aggressiveness; and (4) a small number, possibly with the most severe conditions, reported a combination of all these features. CONCLUSIONS User profiles did not overlap with clinician-made diagnoses. Since each profile seems to be associated with a different level of severity, the profiles could be useful for the prediction of behavioral risks among users of e-mental health apps.
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Affiliation(s)
- Jorge Lopez-Castroman
- Institute of Functional Genomics, CNRS-INSERM, Montpellier, France.,Department of Psychiatry, Nimes University Hospital, Nimes, France.,CIBERSAM, Madrid, Spain.,University of Montpellier, Montpellier, France
| | | | - Aurora Cobo Aguilera
- Department of Signal Theory, Universidad Carlos III de Madrid, Madrid, Spain.,Instituto de Investigación Sanitaria Gregorio Marañon, Madrid, Spain
| | - Philippe Courtet
- Institute of Functional Genomics, CNRS-INSERM, Montpellier, France.,University of Montpellier, Montpellier, France.,Department of Psychiatric Emergency and Acute Care, Lapeyronie Hospital, University of Montpellier, Montpellier, France
| | - Maria Luisa Barrigón
- Universidad Autonoma de Madrid, Madrid, Spain.,Department of Psychiatry, Hospital Universitario Fundación Jiménez Díaz, Madrid, Spain
| | - Antonio Artés
- CIBERSAM, Madrid, Spain.,Department of Signal Theory, Universidad Carlos III de Madrid, Madrid, Spain.,Instituto de Investigación Sanitaria Gregorio Marañon, Madrid, Spain
| | - Enrique Baca-García
- Department of Psychiatry, Nimes University Hospital, Nimes, France.,CIBERSAM, Madrid, Spain.,Universidad Autonoma de Madrid, Madrid, Spain.,Department of Psychiatry, Hospital Universitario Fundación Jiménez Díaz, Madrid, Spain.,Department of Psychiatry, University Hospital Villalba, Villalba, Madrid, Spain.,Department of Psychiatry, University Hospital Infanta Elena, Valdemoro, Madrid, Spain.,Department of Psychiatry, University Hospital Rey Juan Carlos, Mostoles, Madrid, Spain.,Universidad Católica del Maule, Talca, Chile
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16
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Maglogiannis I, Kontogianni G, Papadodima O, Karanikas H, Billiris A, Chatziioannou A. An Integrated Platform for Skin Cancer Heterogenous and Multilayered Data Management. J Med Syst 2021; 45:10. [PMID: 33404959 DOI: 10.1007/s10916-020-01679-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2019] [Accepted: 11/23/2020] [Indexed: 01/22/2023]
Abstract
Electronic health record (EHR) systems improve health care services by allowing the combination of health data with clinical decision support features and clinical image analyses. This study presents a modular and distributed platform that is able to integrate and accommodate heterogeneous, multidimensional (omics, histological images and clinical) data for the multi-angled portrayal and management of skin cancer patients. The proposed design offers a layered analytical framework as an expansion of current EHR systems, which can integrate high-volume molecular -omics data, imaging data, as well as relevant clinical observations. We present a case study in the field of dermatology, where we attempt to combine the multilayered information for the early detection and characterization of melanoma. The specific architecture aspires to lower the barrier for the introduction of personalized therapeutic approaches, towards precision medicine. The paper describes the technical issues of implementation, along with an initial evaluation of the system and discussion.
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Affiliation(s)
- Ilias Maglogiannis
- Department of Digital Systems, University of Piraeus, 126 Grigoriou Lambraki, 18534, Piraeus, Greece.
| | - Georgia Kontogianni
- Department of Digital Systems, University of Piraeus, 126 Grigoriou Lambraki, 18534, Piraeus, Greece
- National Hellenic Research Foundation, 48 Vassileos Constantinou Ave, 11635, Athens, Greece
- Center of Systems Biology, Biomedical Research Foundation of the Academy of Athens, 4 Soranou Ephessiou, 11527, Athens, Greece
| | - Olga Papadodima
- National Hellenic Research Foundation, 48 Vassileos Constantinou Ave, 11635, Athens, Greece
| | | | | | - Aristotelis Chatziioannou
- National Hellenic Research Foundation, 48 Vassileos Constantinou Ave, 11635, Athens, Greece
- Center of Systems Biology, Biomedical Research Foundation of the Academy of Athens, 4 Soranou Ephessiou, 11527, Athens, Greece
- e-NIOS Applications Private Company, 17671, Kallithea, Greece
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17
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Optical Technologies for the Improvement of Skin Cancer Diagnosis: A Review. SENSORS 2021; 21:s21010252. [PMID: 33401739 PMCID: PMC7795742 DOI: 10.3390/s21010252] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 12/24/2020] [Accepted: 12/26/2020] [Indexed: 02/04/2023]
Abstract
The worldwide incidence of skin cancer has risen rapidly in the last decades, becoming one in three cancers nowadays. Currently, a person has a 4% chance of developing melanoma, the most aggressive form of skin cancer, which causes the greatest number of deaths. In the context of increasing incidence and mortality, skin cancer bears a heavy health and economic burden. Nevertheless, the 5-year survival rate for people with skin cancer significantly improves if the disease is detected and treated early. Accordingly, large research efforts have been devoted to achieve early detection and better understanding of the disease, with the aim of reversing the progressive trend of rising incidence and mortality, especially regarding melanoma. This paper reviews a variety of the optical modalities that have been used in the last years in order to improve non-invasive diagnosis of skin cancer, including confocal microscopy, multispectral imaging, three-dimensional topography, optical coherence tomography, polarimetry, self-mixing interferometry, and machine learning algorithms. The basics of each of these technologies together with the most relevant achievements obtained are described, as well as some of the obstacles still to be resolved and milestones to be met.
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18
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Saito K, Motani Y, Takae S, Suzuki N, Tsukada K. Convolutional neural network-based automatic detection of follicle cells in ovarian tissue using optical coherence tomography. Biomed Phys Eng Express 2020; 6. [PMID: 34035193 DOI: 10.1088/2057-1976/abc3d4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Accepted: 10/22/2020] [Indexed: 01/17/2023]
Abstract
To preserve the fertility of young female cancer patients, ovarian tissue cryopreservation and transplantation have been investigated as next-generation reproductive medical technologies. Non-invasive visualization of follicles in ovarian tissue and cryopreservation of higher density tissue is essential for effective transplantation. We proposed the use of optical coherence tomography (OCT) that can noninvasively visualize the internal structure of the ovarian tissue. However, a method for quantifying cell density has not yet been established because of the lack of available techniques to visualize follicles noninvasively. We proposed the use of a convolutional neural network (CNN) to extract small features from medical images as an image analysis method to automatically detect follicles from the obtained OCT images. First, we collected a total of 13 ovarian tissues from four-day-old mice and acquired OCT images using a full-field-type OCT. Then, the acquired images were analyzed using three detection methods: filter processing, filter processing combined with the CNN, and only CNN. Finally, to verify the detection accuracy of each method, the detection rate and precision were calculated by taking the doctor's detection as the correct result. The results showed that the detection method only using CNN achieved a detection rate of 0.81 and precision of 0.67; this indicated that follicles could be effectively detected using our proposed method. Furthermore, it is quantitatively evident that the density of follicles from the surface layer to the deep region differs depending on the tissue. In the future, these results could be used to detect follicles in tissues of different maturation stages and quantify follicles three-dimensionally, further accelerating next-generation reproductive medicine.
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Affiliation(s)
- Kasumi Saito
- Graduate School of Fundamental Science and Technology, Keio University, Yokohama, Japan
| | - Yuki Motani
- Graduate School of Fundamental Science and Technology, Keio University, Yokohama, Japan
| | - Seido Takae
- Department of Obstetrics and Gynecology, St. Marianna University School of Medicine, Kawasaki, Japan
| | - Nao Suzuki
- Department of Obstetrics and Gynecology, St. Marianna University School of Medicine, Kawasaki, Japan
| | - Kosuke Tsukada
- Graduate School of Fundamental Science and Technology, Keio University, Yokohama, Japan
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19
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Noyan MA, Durdu M, Eskiocak AH. TzanckNet: a convolutional neural network to identify cells in the cytology of erosive-vesiculobullous diseases. Sci Rep 2020; 10:18314. [PMID: 33110197 PMCID: PMC7591506 DOI: 10.1038/s41598-020-75546-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Accepted: 10/16/2020] [Indexed: 11/09/2022] Open
Abstract
Tzanck smear test is a low-cost, rapid and reliable tool which can be used for the diagnosis of many erosive-vesiculobullous, tumoral and granulomatous diseases. Currently its use is limited mainly due to lack of experience in interpretation of the smears. We developed a deep learning model, TzanckNet, that can identify cells in Tzanck smear test findings. TzanckNet was trained on a retrospective development dataset of 2260 Tzanck smear images collected between December 2006 and December 2019. The finalized model was evaluated using a prospective validation dataset of 359 Tzanck smear images collected from 15 patients during January 2020. It is designed to recognize six cell types (acantholytic cells, eosinophils, hypha, multinucleated giant cells, normal keratinocytes and tadpole cells). For 359 images and 6 cell types, TzanckNet made 2154 predictions. The accuracy was 94.3% (95% CI 93.4-95.3), the sensitivity was 83.7% (95% CI 80.3-87.0) and the specificity was 97.3% (95% CI 96.5-98.1). The area under the receiver operating characteristic curve was 0.974. Our results show that TzanckNet has the potential to lower the experience barrier needed to use this test, broadening its user base, and hence improving patient well-being.
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Affiliation(s)
| | - Murat Durdu
- Department of Dermatology, Başkent University Medical School, Adana Dr. Turgut Noyan Application and Research Center, Adana, Turkey
| | - Ali Haydar Eskiocak
- Department of Dermatology, Başkent University Medical School, Adana Dr. Turgut Noyan Application and Research Center, Adana, Turkey
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20
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Singhal A, Shukla R, Kankar PK, Dubey S, Singh S, Pachori RB. Comparing the capabilities of transfer learning models to detect skin lesion in humans. Proc Inst Mech Eng H 2020; 234:1083-1093. [PMID: 32643539 DOI: 10.1177/0954411920939829] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Effective diagnosis of skin tumours mainly relies on the analysis of the characteristics of the lesion. Automatic detection of malignant skin lesion has become a mandatory task to reduce the risk of human deaths and increase their survival. This article proposes a study of skin lesion classification using transfer learning approach. The transfer learning model uses four different state-of-the-art architectures, namely Inception v3, Residual Networks (ResNet 50), Dense Convolutional Networks (DenseNet 201) and Inception Residual Networks (Inception ResNet v2). These models are trained under the dataset comprising seven different classes of skin lesions. The skin lesion images are pre-processed using image quantization, grayscaling and the Wiener filter before final training step. These models are compared for performance evaluation on different metrics. The present study shows the efficacy of the methodology for automated classification of lesion images.
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Affiliation(s)
- Aditi Singhal
- Department of Electronics Engineering, Bharati Vidyapeeth (Deemed to be) University, College of Engineering Pune, Pune, India
| | - Ramesht Shukla
- Department of Electronics Engineering, Bharati Vidyapeeth (Deemed to be) University, College of Engineering Pune, Pune, India
| | - Pavan Kumar Kankar
- System Dynamics Lab, Discipline of Mechanical Engineering, Indian Institute of Technology Indore, Indore, India
| | - Saurabh Dubey
- Government MGM Medical College & Maharaja Yashwantrao Hospitals-MYH, Indore, India
| | - Sukhjeet Singh
- Machinery Fault Diagnostics and Signal Processing Laboratory, Department of Mechanical Engineering, Guru Nanak Dev University, Amritsar, India
| | - Ram Bilas Pachori
- Discipline of Electrical Engineering, Indian Institute of Technology Indore, Indore, India
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21
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A novel machine learning strategy for model selections - Stepwise Support Vector Machine (StepSVM). PLoS One 2020; 15:e0238384. [PMID: 32853243 PMCID: PMC7451646 DOI: 10.1371/journal.pone.0238384] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2020] [Accepted: 08/14/2020] [Indexed: 12/20/2022] Open
Abstract
An essential aspect of medical research is the prediction for a health outcome and the scientific identification of important factors. As a result, numerous methods were developed for model selections in recent years. In the era of big data, machine learning has been broadly adopted for data analysis. In particular, the Support Vector Machine (SVM) has an excellent performance in classifications and predictions with the high-dimensional data. In this research, a novel model selection strategy is carried out, named as the Stepwise Support Vector Machine (StepSVM). The new strategy is based on the SVM to conduct a modified stepwise selection, where the tuning parameter could be determined by 10-fold cross-validation that minimizes the mean squared error. Two popular methods, the conventional stepwise logistic regression model and the SVM Recursive Feature Elimination (SVM-RFE), were compared to the StepSVM. The Stability and accuracy of the three strategies were evaluated by simulation studies with a complex hierarchical structure. Up to five variables were selected to predict the dichotomous cancer remission of a lung cancer patient. Regarding the stepwise logistic regression, the mean of the C-statistic was 69.19%. The overall accuracy of the SVM-RFE was estimated at 70.62%. In contrast, the StepSVM provided the highest prediction accuracy of 80.57%. Although the StepSVM is more time consuming, it is more consistent and outperforms the other two methods.
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De Logu F, Ugolini F, Maio V, Simi S, Cossu A, Massi D, Nassini R, Laurino M. Recognition of Cutaneous Melanoma on Digitized Histopathological Slides via Artificial Intelligence Algorithm. Front Oncol 2020; 10:1559. [PMID: 33014803 PMCID: PMC7508308 DOI: 10.3389/fonc.2020.01559] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2020] [Accepted: 07/20/2020] [Indexed: 12/12/2022] Open
Abstract
Increasing incidence of skin cancer combined with a shortage of dermatopathologists has increased the workload of pathology departments worldwide. In addition, the high intraobserver and interobserver variability in the assessment of melanocytic skin lesions can result in underestimated or overestimated diagnosis of melanoma. Thus, the development of new techniques for skin tumor diagnosis is essential to assist pathologists to standardize diagnoses and plan accurate patient treatment. Here, we describe the development of an artificial intelligence (AI) system that recognizes cutaneous melanoma from histopathological digitalized slides with clinically acceptable accuracy. Whole-slide digital images from 100 formalin-fixed paraffin-embedded primary cutaneous melanoma were used to train a convolutional neural network (CNN) based on a pretrained Inception-ResNet-v2 to accurately and automatically differentiate tumoral areas from healthy tissue. The CNN was trained by using 60 digital slides in which regions of interest (ROIs) of tumoral and healthy tissue were extracted by experienced dermatopathologists, while the other 40 slides were used as test datasets. A total of 1377 patches of healthy tissue and 2141 patches of melanoma were assessed in the training/validation set, while 791 patches of healthy tissue and 1122 patches of pathological tissue were evaluated in the test dataset. Considering the classification by expert dermatopathologists as reference, the trained deep net showed high accuracy (96.5%), sensitivity (95.7%), specificity (97.7%), F1 score (96.5%), and a Cohen’s kappa of 0.929. Our data show that a deep learning system can be trained to recognize melanoma samples, achieving accuracies comparable to experienced dermatopathologists. Such an approach can offer a valuable aid in improving diagnostic efficiency when expert consultation is not available, as well as reducing interobserver variability. Further studies in larger data sets are necessary to verify whether the deep learning algorithm allows subclassification of different melanoma subtypes.
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Affiliation(s)
- Francesco De Logu
- Section of Clinical Pharmacology and Oncology, Department of Health Sciences, University of Florence, Florence, Italy
| | - Filippo Ugolini
- Section of Pathological Anatomy, Department of Health Sciences, University of Florence, Florence, Italy
| | - Vincenza Maio
- Histopathology and Molecular Diagnostics, Careggi University Hospital, Florence, Italy
| | - Sara Simi
- Section of Pathological Anatomy, Department of Health Sciences, University of Florence, Florence, Italy
| | - Antonio Cossu
- Department of Medical, Surgical, and Experimental Sciences, University of Sassari, Sassari, Italy
| | - Daniela Massi
- Section of Pathological Anatomy, Department of Health Sciences, University of Florence, Florence, Italy
| | | | - Romina Nassini
- Section of Clinical Pharmacology and Oncology, Department of Health Sciences, University of Florence, Florence, Italy
| | - Marco Laurino
- Institute of Clinical Physiology, National Research Council, Pisa, Italy
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Machine Learning Model Comparison in the Screening of Cholangiocarcinoma Using Plasma Bile Acids Profiles. Diagnostics (Basel) 2020; 10:diagnostics10080551. [PMID: 32748848 PMCID: PMC7460348 DOI: 10.3390/diagnostics10080551] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Revised: 07/31/2020] [Accepted: 07/31/2020] [Indexed: 12/13/2022] Open
Abstract
Bile acids (BAs) assessments are garnering increasing interest for their potential involvement in development and progression of cholangiocarcinoma (CCA). Since machine learning (ML) algorithms are increasingly used for exploring metabolomic profiles, we evaluated performance of some ML models for dissecting patients with CCA or benign biliary diseases according to their plasma BAs profiles. We used ultra-performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS) for assessing plasma BAs profile in 112 patients (70 CCA, 42 benign biliary diseases). Twelve normalisation procedures were applied, and performance of six ML algorithms were evaluated (logistic regression, k-nearest neighbors, naïve bayes, RBF SVM, random forest, extreme gradient boosting). Naïve bayes, using direct bilirubin concentration for normalisation of BAs, was the ML model displaying better performance in the holdout set, with an Area Under Curve (AUC) of 0.95, 0.79 sensitivity, 1.00 specificity. This model, also characterised by 1.00 positive predictive value and 0.73 negative predictive value, displayed a globally excellent accuracy (86.4%). The accuracy of the other five models was lower, and AUCs ranged 0.75–0.95. Preliminary results of this study show that application of ML to BAs profile analysis can provide a valuable contribution for characterising bile duct diseases and identifying patients with higher likelihood of having malignant pathologies.
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Bajwa UI, Alam S, Ul Haq N, Ratyal NI, Anwar MW. Skin Disease Classification using Neural Network. Curr Med Imaging 2020; 16:711-719. [PMID: 32723243 DOI: 10.2174/1573405615666190422152926] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2018] [Revised: 03/25/2019] [Accepted: 04/05/2019] [Indexed: 11/22/2022]
Abstract
BACKGROUND In this study, a novel and fully automatic skin disease classification approach is proposed using statistical feature extraction and Artificial Neural Network (ANN) based classification using first and second order statistical moments, the entropy of different color channels and texture-based features. AIMS The basic aim of our study is to develop an automated system for skin disease classification that can help a general physician to automatically detect the lesion and classify it to disease types. METHOD The performance of the proposed approach is corroborated by extensive experiments performed on a dataset of 588 images containing 6907 lesion regions. RESULTS The results show that the proposed methodology can be effectively used to construct a skin disease classification system. CONCLUSION Our proposed method is designed for a specific skin tone. Future investigation is needed to analyze the impact of different skin tones on the performance of lesions detection and classification system.
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Affiliation(s)
- Usama Ijaz Bajwa
- Department of Computer Science, COMSATS University Islamabad, Lahore Campus, Lahore, Pakistan
| | - Sardar Alam
- Department of Computer Science, COMSATS University Islamabad, Abbottabad Campus, Abbottabad, Pakistan
| | - Nuhman Ul Haq
- Department of Computer Science, COMSATS University Islamabad, Abbottabad Campus, Abbottabad, Pakistan
| | - Naeem Iqbal Ratyal
- Department of Electrical Engineering, Mirpur University of Science and Technology (MUST), Mirpur (AJK), Pakistan
| | - Muhammad Waqas Anwar
- Department of Computer Science, COMSATS University Islamabad, Lahore Campus, Lahore, Pakistan
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Lynam AL, Dennis JM, Owen KR, Oram RA, Jones AG, Shields BM, Ferrat LA. Logistic regression has similar performance to optimised machine learning algorithms in a clinical setting: application to the discrimination between type 1 and type 2 diabetes in young adults. Diagn Progn Res 2020; 4:6. [PMID: 32607451 PMCID: PMC7318367 DOI: 10.1186/s41512-020-00075-2] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Accepted: 03/26/2020] [Indexed: 01/20/2023] Open
Abstract
BACKGROUND There is much interest in the use of prognostic and diagnostic prediction models in all areas of clinical medicine. The use of machine learning to improve prognostic and diagnostic accuracy in this area has been increasing at the expense of classic statistical models. Previous studies have compared performance between these two approaches but their findings are inconsistent and many have limitations. We aimed to compare the discrimination and calibration of seven models built using logistic regression and optimised machine learning algorithms in a clinical setting, where the number of potential predictors is often limited, and externally validate the models. METHODS We trained models using logistic regression and six commonly used machine learning algorithms to predict if a patient diagnosed with diabetes has type 1 diabetes (versus type 2 diabetes). We used seven predictor variables (age, BMI, GADA islet-autoantibodies, sex, total cholesterol, HDL cholesterol and triglyceride) using a UK cohort of adult participants (aged 18-50 years) with clinically diagnosed diabetes recruited from primary and secondary care (n = 960, 14% with type 1 diabetes). Discrimination performance (ROC AUC), calibration and decision curve analysis of each approach was compared in a separate external validation dataset (n = 504, 21% with type 1 diabetes). RESULTS Average performance obtained in internal validation was similar in all models (ROC AUC ≥ 0.94). In external validation, there were very modest reductions in discrimination with AUC ROC remaining ≥ 0.93 for all methods. Logistic regression had the numerically highest value in external validation (ROC AUC 0.95). Logistic regression had good performance in terms of calibration and decision curve analysis. Neural network and gradient boosting machine had the best calibration performance. Both logistic regression and support vector machine had good decision curve analysis for clinical useful threshold probabilities. CONCLUSION Logistic regression performed as well as optimised machine algorithms to classify patients with type 1 and type 2 diabetes. This study highlights the utility of comparing traditional regression modelling to machine learning, particularly when using a small number of well understood, strong predictor variables.
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Affiliation(s)
- Anita L. Lynam
- Institute of Biomedical and Clinical Science, College of Medicine and Health, University of Exeter, Exeter, EX2 5DW UK
| | - John M. Dennis
- Institute of Biomedical and Clinical Science, College of Medicine and Health, University of Exeter, Exeter, EX2 5DW UK
| | - Katharine R. Owen
- Oxford Centre for Diabetes Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Oxford, OX3 7LE UK
- Oxford NIHR Biomedical Research Centre, Oxford University Hospitals Foundation Trust, John Radcliffe Hospital, Oxford, UK
| | - Richard A. Oram
- Institute of Biomedical and Clinical Science, College of Medicine and Health, University of Exeter, Exeter, EX2 5DW UK
| | - Angus G. Jones
- Institute of Biomedical and Clinical Science, College of Medicine and Health, University of Exeter, Exeter, EX2 5DW UK
| | - Beverley M. Shields
- Institute of Biomedical and Clinical Science, College of Medicine and Health, University of Exeter, Exeter, EX2 5DW UK
| | - Lauric A. Ferrat
- Institute of Biomedical and Clinical Science, College of Medicine and Health, University of Exeter, Exeter, EX2 5DW UK
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Classification of Arrhythmia by Using Deep Learning with 2-D ECG Spectral Image Representation. REMOTE SENSING 2020. [DOI: 10.3390/rs12101685] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
The electrocardiogram (ECG) is one of the most extensively employed signals used in the diagnosis and prediction of cardiovascular diseases (CVDs). The ECG signals can capture the heart’s rhythmic irregularities, commonly known as arrhythmias. A careful study of ECG signals is crucial for precise diagnoses of patients’ acute and chronic heart conditions. In this study, we propose a two-dimensional (2-D) convolutional neural network (CNN) model for the classification of ECG signals into eight classes; namely, normal beat, premature ventricular contraction beat, paced beat, right bundle branch block beat, left bundle branch block beat, atrial premature contraction beat, ventricular flutter wave beat, and ventricular escape beat. The one-dimensional ECG time series signals are transformed into 2-D spectrograms through short-time Fourier transform. The 2-D CNN model consisting of four convolutional layers and four pooling layers is designed for extracting robust features from the input spectrograms. Our proposed methodology is evaluated on a publicly available MIT-BIH arrhythmia dataset. We achieved a state-of-the-art average classification accuracy of 99.11%, which is better than those of recently reported results in classifying similar types of arrhythmias. The performance is significant in other indices as well, including sensitivity and specificity, which indicates the success of the proposed method.
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Ramroach S, Joshi A, John M. Optimisation of cancer classification by machine learning generates an enriched list of candidate drug targets and biomarkers. Mol Omics 2020; 16:113-125. [PMID: 32095794 DOI: 10.1039/c9mo00198k] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
A novel list of potential biomarkers was generated from RNA-seq expression data and used to optimise cancer classification.
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Affiliation(s)
- Sterling Ramroach
- Department of Electrical and Computer Engineering
- University of the West Indies
- Saint Augustine
- Trinidad and Tobago
| | - Ajay Joshi
- Department of Electrical and Computer Engineering
- University of the West Indies
- Saint Augustine
- Trinidad and Tobago
| | - Melford John
- Department of Pre-Clinical Sciences
- University of the West Indies
- Saint Augustine
- Trinidad and Tobago
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Jeon W, An HJ, Kim JI, Park JM, Kim H, Shin KH, Chie EK. Preliminary Application of Synthetic Computed Tomography Image Generation from Magnetic Resonance Image Using Deep-Learning in Breast Cancer Patients. ACTA ACUST UNITED AC 2019. [DOI: 10.14407/jrpr.2019.44.4.149] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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Assessing the effectiveness of artificial intelligence methods for melanoma: A retrospective review. J Am Acad Dermatol 2019; 81:1176-1180. [DOI: 10.1016/j.jaad.2019.06.042] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2018] [Revised: 06/02/2019] [Accepted: 06/19/2019] [Indexed: 01/08/2023]
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Shahid AH, Singh M. Computational intelligence techniques for medical diagnosis and prognosis: Problems and current developments. Biocybern Biomed Eng 2019. [DOI: 10.1016/j.bbe.2019.05.010] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
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Bottigliengo D, Berchialla P, Lanera C, Azzolina D, Lorenzoni G, Martinato M, Giachino D, Baldi I, Gregori D. The Role of Genetic Factors in Characterizing Extra-Intestinal Manifestations in Crohn's Disease Patients: Are Bayesian Machine Learning Methods Improving Outcome Predictions? J Clin Med 2019; 8:jcm8060865. [PMID: 31212952 PMCID: PMC6617350 DOI: 10.3390/jcm8060865] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Revised: 06/12/2019] [Accepted: 06/13/2019] [Indexed: 01/01/2023] Open
Abstract
(1) Background: The high heterogeneity of inflammatory bowel disease (IBD) makes the study of this condition challenging. In subjects affected by Crohn’s disease (CD), extra-intestinal manifestations (EIMs) have a remarkable potential impact on health status. Increasing numbers of patient characteristics and the small size of analyzed samples make EIMs prediction very difficult. Under such constraints, Bayesian machine learning techniques (BMLTs) have been proposed as a robust alternative to classical models for outcome prediction. This study aims to determine whether BMLT could improve EIM prediction and statistical support for the decision-making process of clinicians. (2) Methods: Three of the most popular BMLTs were employed in this study: Naϊve Bayes (NB), Bayesian Network (BN) and Bayesian Additive Regression Trees (BART). They were applied to a retrospective observational Italian study of IBD genetics. (3) Results: The performance of the model is strongly affected by the features of the dataset, and BMLTs poorly classify EIM appearance. (4) Conclusions: This study shows that BMLTs perform worse than expected in classifying the presence of EIMs compared to classical statistical tools in a context where mixed genetic and clinical data are available but relevant data are also missing, as often occurs in clinical practice.
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Affiliation(s)
- Daniele Bottigliengo
- Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, and Vascular Sciences and Public Health, University of Padova, 35131 Padova, Italy.
| | - Paola Berchialla
- Department of Clinical and Biological Sciences, University of Torino, 10126 Torino, Italy.
| | - Corrado Lanera
- Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, and Vascular Sciences and Public Health, University of Padova, 35131 Padova, Italy.
| | - Danila Azzolina
- Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, and Vascular Sciences and Public Health, University of Padova, 35131 Padova, Italy.
| | - Giulia Lorenzoni
- Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, and Vascular Sciences and Public Health, University of Padova, 35131 Padova, Italy.
| | - Matteo Martinato
- Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, and Vascular Sciences and Public Health, University of Padova, 35131 Padova, Italy.
| | - Daniela Giachino
- Department of Clinical and Biological Sciences, University of Torino, 10126 Torino, Italy.
| | - Ileana Baldi
- Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, and Vascular Sciences and Public Health, University of Padova, 35131 Padova, Italy.
| | - Dario Gregori
- Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, and Vascular Sciences and Public Health, University of Padova, 35131 Padova, Italy.
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Marka A, Carter JB, Toto E, Hassanpour S. Automated detection of nonmelanoma skin cancer using digital images: a systematic review. BMC Med Imaging 2019; 19:21. [PMID: 30819133 PMCID: PMC6394090 DOI: 10.1186/s12880-019-0307-7] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2018] [Accepted: 01/07/2019] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND Computer-aided diagnosis of skin lesions is a growing area of research, but its application to nonmelanoma skin cancer (NMSC) is relatively under-studied. The purpose of this review is to synthesize the research that has been conducted on automated detection of NMSC using digital images and to assess the quality of evidence for the diagnostic accuracy of these technologies. METHODS Eight databases (PubMed, Google Scholar, Embase, IEEE Xplore, Web of Science, SpringerLink, ScienceDirect, and the ACM Digital Library) were searched to identify diagnostic studies of NMSC using image-based machine learning models. Two reviewers independently screened eligible articles. The level of evidence of each study was evaluated using a five tier rating system, and the applicability and risk of bias of each study was assessed using the Quality Assessment of Diagnostic Accuracy Studies tool. RESULTS Thirty-nine studies were reviewed. Twenty-four models were designed to detect basal cell carcinoma, two were designed to detect squamous cell carcinoma, and thirteen were designed to detect both. All studies were conducted in silico. The overall diagnostic accuracy of the classifiers, defined as concordance with histopathologic diagnosis, was high, with reported accuracies ranging from 72 to 100% and areas under the receiver operating characteristic curve ranging from 0.832 to 1. Most studies had substantial methodological limitations, but several were robustly designed and presented a high level of evidence. CONCLUSION Most studies of image-based NMSC classifiers report performance greater than or equal to the reported diagnostic accuracy of the average dermatologist, but relatively few studies have presented a high level of evidence. Clinical studies are needed to assess whether these technologies can feasibly be implemented as a real-time aid for clinical diagnosis of NMSC.
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Affiliation(s)
- Arthur Marka
- Dartmouth Geisel School of Medicine, Box 163, Kellogg Building, 45 Dewey Field Road, Hanover, NH USA
| | - Joi B. Carter
- Section of Dermatology, Dartmouth-Hitchcock Medical Center, Lebanon, NH USA
- Department of Surgery, Dartmouth Geisel School of Medicine, Hanover, NH USA
| | - Ermal Toto
- Department of Computer Science, Worcester Polytechnic Institute, Worcester, MA USA
| | - Saeed Hassanpour
- Department of Biomedical Data Science, Dartmouth College, Lebanon, NH USA
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Resolution invariant wavelet features of melanoma studied by SVM classifiers. PLoS One 2019; 14:e0211318. [PMID: 30726260 PMCID: PMC6364923 DOI: 10.1371/journal.pone.0211318] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2018] [Accepted: 01/12/2019] [Indexed: 11/19/2022] Open
Abstract
This article refers to the Computer Aided Diagnosis of the melanoma skin cancer. We derive wavelet-based features of melanoma from the dermoscopic images of pigmental skin lesions and apply binary C-SVM classifiers to discriminate malignant melanoma from dysplastic nevus. The aim of this research is to select the most efficient model of the SVM classifier for various image resolutions and to search for the best resolution-invariant wavelet bases. We show AUC as a function of the wavelet number and SVM kernels optimized by the Bayesian search for two independent data sets. Our results are compatible with the previous experiments to discriminate melanoma in dermoscopy images with ensembling and feed-forward neural networks.
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Ben-Assuli O, Padman R. Analysing repeated hospital readmissions using data mining techniques. Health Syst (Basingstoke) 2018; 7:166-180. [PMID: 31215903 DOI: 10.1080/20476965.2018.1510040] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2017] [Revised: 09/12/2017] [Accepted: 10/05/2017] [Indexed: 10/27/2022] Open
Abstract
Few studies have examined how to identify future readmission of patients with a large number of repeat emergency department (ED) visits. We explore 30-day readmission risk prediction using Microsoft's AZURE machine learning software and compare five classification methods: Logistic Regression, Boosted Decision Trees (BDTs), Support Vector Machine (SVM), Bayes Point Machine (BPM), and Two-Class Neural Network (TCNN). We predict the last readmission visit of frequent ED patients extracted from the electronic health records of their 8455 penultimate visits. The methods show differential improvement, with the BDT indicating marginally better AUC (area under the ROC curve) than logistic regression and BPM, followed by the TCNN and SVM. A comparison of BDT and Logistic Regression results for correct and incorrect classification highlights the similarities and differences in the significant predictors identified by each method. Future research may incorporate time-varying covariates to identify other longitudinal factors that can lead to readmission risk reduction.
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Affiliation(s)
- Ofir Ben-Assuli
- Information Systems Department, Faculty of Business Administration, Ono Academic College, Kiryat Ono, Israel
| | - Rema Padman
- The H. John Heinz III College, Carnegie Mellon University, Pittsburgh, PA, USA
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35
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Improving the performance of convolutional neural network for skin image classification using the response of image analysis filters. Neural Comput Appl 2018. [DOI: 10.1007/s00521-018-3711-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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Carrara M, Massari E, Cicchetti A, Giandini T, Avuzzi B, Palorini F, Stucchi C, Fellin G, Gabriele P, Vavassori V, Degli Esposti C, Cozzarini C, Pignoli E, Fiorino C, Rancati T, Valdagni R. Development of a Ready-to-Use Graphical Tool Based on Artificial Neural Network Classification: Application for the Prediction of Late Fecal Incontinence After Prostate Cancer Radiation Therapy. Int J Radiat Oncol Biol Phys 2018; 102:1533-1542. [PMID: 30092335 DOI: 10.1016/j.ijrobp.2018.07.2014] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2017] [Revised: 06/19/2018] [Accepted: 07/26/2018] [Indexed: 12/13/2022]
Abstract
PURPOSE This study was designed to apply artificial neural network (ANN) classification methods for the prediction of late fecal incontinence (LFI) after high-dose prostate cancer radiation therapy and to develop a ready-to-use graphical tool. MATERIALS AND METHODS In this study, 598 men recruited in 2 national multicenter trials were analyzed. Information was recorded on comorbidity, previous abdominal surgery, use of drugs, and dose distribution. Fecal incontinence was prospectively evaluated through self-reported questionnaires. To develop the ANN, the study population was randomly split into training (n = 300), validation (n = 149), and test (n = 149) sets. Mean grade of longitudinal LFI (ie, expressed as the average incontinence grade over the first 3 years after radiation therapy) ≥1 was considered the endpoint. A suitable subset of variables able to better predict LFI was selected by simulating 100,000 ANN configurations. The search for the definitive ANN was then performed by varying the number of inputs and hidden neurons from 4 to 5 and from 1 to 9, respectively. A final classification model was established as the average of the best 5 among 500 ANNs with the same architecture. An ANN-based graphical method to compute LFI prediction was developed to include one continuous and n dichotomous variables. RESULTS An ANN architecture was selected, with 5 input variables (mean dose, previous abdominal surgery, use of anticoagulants, use of antihypertensive drugs, and use of neoadjuvant and adjuvant hormone therapy) and 4 hidden neurons. The developed classification model correctly identified patients with LFI with 80.8% sensitivity and 63.7% ± 1.0% specificity and an area under the curve of 0.78. The developed graphical tool may efficiently classify patients in low, intermediate, and high LFI risk classes. CONCLUSIONS An ANN-based model was developed to predict LFI. The model was translated in a ready-to-use graphical tool for LFI risk classification, with direct interpretation of the role of the predictors.
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Affiliation(s)
- Mauro Carrara
- Department of Medical Physics, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
| | - Eleonora Massari
- Department of Medical Physics, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Alessandro Cicchetti
- Prostate Cancer Program, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Tommaso Giandini
- Department of Medical Physics, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Barbara Avuzzi
- Department of Radiation Oncology 1, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Federica Palorini
- Prostate Cancer Program, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Claudio Stucchi
- Department of Medical Physics, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Giovanni Fellin
- Department of Radiation Oncology, Ospedale Santa Chiara, Trento, Italy
| | - Pietro Gabriele
- Department of Radiation Oncology, Istituto di Candiolo-Fondazione del Piemonte per l'Oncologia IRCCS, Candiolo, Italy
| | - Vittorio Vavassori
- Department of Radiation Oncology, Cliniche Gavazzeni-Humanitas, Bergamo, Italy
| | | | - Cesare Cozzarini
- Department of Radiation Oncology, San Raffaele Scientific Institute, Milano, Italy
| | - Emanuele Pignoli
- Department of Medical Physics, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Claudio Fiorino
- Department of Medical Physics, San Raffaele Scientific Institute, Milano, Italy
| | - Tiziana Rancati
- Prostate Cancer Program, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Riccardo Valdagni
- Prostate Cancer Program, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy; Department of Radiation Oncology 1, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy; Department of Oncology and Hemato-oncology, Università degli Studi di Milano, Milan, Italy
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Singh N, Gupta SK. Recent advancement in the early detection of melanoma using computerized tools: An image analysis perspective. Skin Res Technol 2018; 25:129-141. [PMID: 30030916 DOI: 10.1111/srt.12622] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/23/2018] [Indexed: 11/29/2022]
Abstract
BACKGROUND The paper reviews the advancement of tools and current technologies for the detection of melanoma. We discussed several computational strategies from pre- to postprocessing image operations, descriptors, and popular classifiers to diagnose a suspected skin lesion based on its virtual similarity to the malignant lesion with known histopathology. We reviewed the current state of smart phone-based apps as diagnostic tools for screening. METHODS A literature survey was conducted using a combination of keywords in the bibliographic databases: PubMed, AJCC, PH2, EDRA, and ISIC melanoma project. A number of melanoma detection apps were downloaded for two major mobile operating systems, iOS and Android; their important uses, key challenges, and various expert opinions were evaluated and also discussed. RESULTS We have provided an overview of research on the computer-aided diagnosis methods to estimate melanoma risk and early screening. Dermoscopic images are the most viable option for the advent of new image processing technologies based on which many of the skin cancer detection apps are being developed recently. We have categorized and explored their potential uses, evaluation criteria, limitations, and other details. CONCLUSION Such advancements are helpful in the sense they are raising awareness. Diagnostic accuracy is the major issue of smart phone-based apps and it cannot replace an adequate clinical experience and biopsy procedures.
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Affiliation(s)
- Nivedita Singh
- Department of Bioinformatics, Systems Toxicology Group, CSIR-Indian Institute of Toxicology Research, Lucknow, Uttar Pradesh, India.,Department of Biochemistry, Babu Banarasi Das University, Lucknow, Uttar Pradesh, India
| | - Shailendra K Gupta
- Department of Bioinformatics, Systems Toxicology Group, CSIR-Indian Institute of Toxicology Research, Lucknow, Uttar Pradesh, India.,Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany.,Chhattisgarh Swami Vivekanand Technical University, Bhilai, Chhattisgarh, India
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Zhang Z, Li H, Chang H, Pan Z, Luo X. Machine learning predictive framework for CO2 thermodynamic properties in solution. J CO2 UTIL 2018. [DOI: 10.1016/j.jcou.2018.04.025] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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Classification of forensic autopsy reports through conceptual graph-based document representation model. J Biomed Inform 2018; 82:88-105. [PMID: 29738820 DOI: 10.1016/j.jbi.2018.04.013] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2017] [Revised: 02/27/2018] [Accepted: 04/24/2018] [Indexed: 12/19/2022]
Abstract
Text categorization has been used extensively in recent years to classify plain-text clinical reports. This study employs text categorization techniques for the classification of open narrative forensic autopsy reports. One of the key steps in text classification is document representation. In document representation, a clinical report is transformed into a format that is suitable for classification. The traditional document representation technique for text categorization is the bag-of-words (BoW) technique. In this study, the traditional BoW technique is ineffective in classifying forensic autopsy reports because it merely extracts frequent but discriminative features from clinical reports. Moreover, this technique fails to capture word inversion, as well as word-level synonymy and polysemy, when classifying autopsy reports. Hence, the BoW technique suffers from low accuracy and low robustness unless it is improved with contextual and application-specific information. To overcome the aforementioned limitations of the BoW technique, this research aims to develop an effective conceptual graph-based document representation (CGDR) technique to classify 1500 forensic autopsy reports from four (4) manners of death (MoD) and sixteen (16) causes of death (CoD). Term-based and Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) based conceptual features were extracted and represented through graphs. These features were then used to train a two-level text classifier. The first level classifier was responsible for predicting MoD. In addition, the second level classifier was responsible for predicting CoD using the proposed conceptual graph-based document representation technique. To demonstrate the significance of the proposed technique, its results were compared with those of six (6) state-of-the-art document representation techniques. Lastly, this study compared the effects of one-level classification and two-level classification on the experimental results. The experimental results indicated that the CGDR technique achieved 12% to 15% improvement in accuracy compared with fully automated document representation baseline techniques. Moreover, two-level classification obtained better results compared with one-level classification. The promising results of the proposed conceptual graph-based document representation technique suggest that pathologists can adopt the proposed system as their basis for second opinion, thereby supporting them in effectively determining CoD.
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Li L, Gao Q, Xu G, Shi B, Ma X, Liu H, Li Q, Tian T, Tang J, Niu H. Postoperative recurrence analysis of breast cancer patients based on clinical serum markers using discriminant methods. Cancer Biomark 2018; 19:403-409. [PMID: 28582844 DOI: 10.3233/cbm-160322] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
BACKGROUND Breast cancer is a common gynecological malignant tumor and currently its clinical diagnosis mainly depends on methods of iconography and measurement of serum level. OBJECTIVE To analyze correlation between serum index levels and prognosis of patients with breast cancer in one week and six months after operation, and to establish support vector machine (SVM) model to evaluate its effectiveness. METHODS One hundred sixty eight patients diagnosed with breast cancer at Affiliated Cancer Hospital of Zhengzhou University were collected, 46 of which did palindromia while other 122 didn't six months after operation. Serum CA153, CA125 and CEA levels of different periods in two groups were analyzed from their differences. Through receiver operating characteristic (ROC) curve analysis, their diagnostic threshold values were calculated, at the same time, SVM model was built. RESULTS There was a significant difference between serum index levels of recurrence group and non-recurrence group in one week and six months after operation (P< 0.05); SVM model was established with an accuracy of 96.67% (29/30), a sensitivity of 90% (9/10) and a specificity of 100% (20/20). CONCLUSIONS Serum CAl53, CEA and CA125 levels after operation have certain instructional significance for prognosis of breast cancer patients, and the established SVM model has high clinical application value.
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Affiliation(s)
- Lu Li
- Integrated TCM and Western Medicine Department, Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Qilong Gao
- Integrated TCM and Western Medicine Department, Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Guolin Xu
- Inpatient Department, Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Bian Shi
- Integrated TCM and Western Medicine Department, Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Xuhui Ma
- Integrated TCM and Western Medicine Department, Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Huaimin Liu
- Integrated TCM and Western Medicine Department, Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Qiujian Li
- Integrated TCM and Western Medicine Department, Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Tongde Tian
- Integrated TCM and Western Medicine Department, Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Jingwen Tang
- Integrated TCM and Western Medicine Department, Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Hong Niu
- Integrated TCM and Western Medicine Department, Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, Henan, China
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Gautam D, Ahmed M, Meena YK, Ul Haq A. Machine learning-based diagnosis of melanoma using macro images. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2018; 34:e2953. [PMID: 29266819 DOI: 10.1002/cnm.2953] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Cancer bears a poisoning threat to human society. Melanoma, the skin cancer, originates from skin layers and penetrates deep into subcutaneous layers. There exists an extensive research in melanoma diagnosis using dermatoscopic images captured through a dermatoscope. While designing a diagnostic model for general handheld imaging systems is an emerging trend, this article proposes a computer-aided decision support system for macro images captured by a general-purpose camera. General imaging conditions are adversely affected by nonuniform illumination, which further affects the extraction of relevant information. To mitigate it, we process an image to define a smooth illumination surface using the multistage illumination compensation approach, and the infected region is extracted using the proposed multimode segmentation method. The lesion information is numerated as a feature set comprising geometry, photometry, border series, and texture measures. The redundancy in feature set is reduced using information theory methods, and a classification boundary is modeled to distinguish benign and malignant samples using support vector machine, random forest, neural network, and fast discriminative mixed-membership-based naive Bayesian classifiers. Moreover, the experimental outcome is supported by hypothesis testing and boxplot representation for classification losses. The simulation results prove the significance of the proposed model that shows an improved performance as compared with competing arts.
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Affiliation(s)
- Diwakar Gautam
- Malaviya National Institute of Technology, Jaipur, India
| | - Mushtaq Ahmed
- Malaviya National Institute of Technology, Jaipur, India
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Manavalan B, Shin TH, Kim MO, Lee G. AIPpred: Sequence-Based Prediction of Anti-inflammatory Peptides Using Random Forest. Front Pharmacol 2018; 9:276. [PMID: 29636690 PMCID: PMC5881105 DOI: 10.3389/fphar.2018.00276] [Citation(s) in RCA: 117] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2018] [Accepted: 03/12/2018] [Indexed: 12/31/2022] Open
Abstract
The use of therapeutic peptides in various inflammatory diseases and autoimmune disorders has received considerable attention; however, the identification of anti-inflammatory peptides (AIPs) through wet-lab experimentation is expensive and often time consuming. Therefore, the development of novel computational methods is needed to identify potential AIP candidates prior to in vitro experimentation. In this study, we proposed a random forest (RF)-based method for predicting AIPs, called AIPpred (AIP predictor in primary amino acid sequences), which was trained with 354 optimal features. First, we systematically studied the contribution of individual composition [amino acid-, dipeptide composition (DPC), amino acid index, chain-transition-distribution, and physicochemical properties] in AIP prediction. Since the performance of the DPC-based model is significantly better than that of other composition-based models, we applied a feature selection protocol on this model and identified the optimal features. AIPpred achieved an area under the curve (AUC) value of 0.801 in a 5-fold cross-validation test, which was ∼2% higher than that of the control RF predictor trained with all DPC composition features, indicating the efficiency of the feature selection protocol. Furthermore, we evaluated the performance of AIPpred on an independent dataset, with results showing that our method outperformed an existing method, as well as 3 different machine learning methods developed in this study, with an AUC value of 0.814. These results indicated that AIPpred will be a useful tool for predicting AIPs and might efficiently assist the development of AIP therapeutics and biomedical research. AIPpred is freely accessible at www.thegleelab.org/AIPpred.
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Affiliation(s)
| | - Tae H Shin
- Department of Physiology, Ajou University School of Medicine, Suwon, South Korea.,Institute of Molecular Science and Technology, Ajou University, Suwon, South Korea
| | - Myeong O Kim
- Division of Life Science and Applied Life Science (BK21 Plus), College of Natural Sciences, Gyeongsang National University, Jinju, South Korea
| | - Gwang Lee
- Department of Physiology, Ajou University School of Medicine, Suwon, South Korea.,Institute of Molecular Science and Technology, Ajou University, Suwon, South Korea
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Ko J, Baldassano SN, Loh PL, Kording K, Litt B, Issadore D. Machine learning to detect signatures of disease in liquid biopsies - a user's guide. LAB ON A CHIP 2018; 18:395-405. [PMID: 29192299 PMCID: PMC5955608 DOI: 10.1039/c7lc00955k] [Citation(s) in RCA: 85] [Impact Index Per Article: 14.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
New technologies that measure sparse molecular biomarkers from easily accessible bodily fluids (e.g. blood, urine, and saliva) are revolutionizing disease diagnostics and precision medicine. Microchip devices can measure more disease biomarkers with better sensitivity and specificity each year, but clinical interpretation of these biomarkers remains a challenge. Single biomarkers in 'liquid biopsy' often cannot accurately predict the state of a disease due to heterogeneity in phenotype and disease expression across individuals. To address this challenge, investigators are combining multiplexed measurements of different biomarkers that together define robust signatures for specific disease states. Machine learning is a useful tool to automatically discover and detect these signatures, especially as new technologies output increasing quantities of molecular data. In this paper, we review the state of the field of machine learning applied to molecular diagnostics and provide practical guidance to use this tool effectively and to avoid common pitfalls.
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Affiliation(s)
- Jina Ko
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
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Pathan S, Prabhu KG, Siddalingaswamy P. Techniques and algorithms for computer aided diagnosis of pigmented skin lesions—A review. Biomed Signal Process Control 2018. [DOI: 10.1016/j.bspc.2017.07.010] [Citation(s) in RCA: 65] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Erol R, Bayraktar M, Kockara S, Kaya S, Halic T. Texture based skin lesion abruptness quantification to detect malignancy. BMC Bioinformatics 2017; 18:484. [PMID: 29297290 PMCID: PMC5751661 DOI: 10.1186/s12859-017-1892-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Abruptness of pigment patterns at the periphery of a skin lesion is one of the most important dermoscopic features for detection of malignancy. In current clinical setting, abrupt cutoff of a skin lesion determined by an examination of a dermatologist. This process is subjective, nonquantitative, and error-prone. We present an improved computational model to quantitatively measure abruptness of a skin lesion over our previous method. To achieve this, we quantitatively analyze the texture features of a region within the lesion boundary. This region is bounded by an interior border line of the lesion boundary which is determined using level set propagation (LSP) method. This method provides a fast border contraction without a need for extensive boolean operations. Then, we build feature vectors of homogeneity, standard deviation of pixel values, and mean of the pixel values of the region between the contracted border and the original border. These vectors are then classified using neural networks (NN) and SVM classifiers. RESULTS As lower homogeneity indicates sharp cutoffs, suggesting melanoma, we carried out our experiments on two dermoscopy image datasets, which consist of 800 benign and 200 malignant melanoma cases. LSP method helped produce better results than Kaya et al., 2016 study. By using texture homogeneity at the periphery of a lesion border determined by LSP, as a classification results, we obtained 87% f1-score and 78% specificity; that we obtained better results than in the previous study. We also compared the performances of two different NN classifiers and support vector machine classifier. The best results obtained using combination of RGB color spaces with the fully-connected multi-hidden layer NN. CONCLUSIONS Computational results also show that skin lesion abrupt cutoff is a reliable indicator of malignancy. Results show that computational model of texture homogeneity along the periphery of skin lesion borders based on LSP is an effective way of quantitatively measuring abrupt cutoff of a lesion.
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Affiliation(s)
- Recep Erol
- Department of Computer Science, UCA, Conway, AR 72034 USA
| | | | - Sinan Kockara
- Department of Computer Science, UCA, Conway, AR 72034 USA
| | | | - Tansel Halic
- Department of Computer Science, UCA, Conway, AR 72034 USA
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Ben-Assuli O, Padman R. Analysing repeated hospital readmissions using data mining techniques. Health Syst (Basingstoke) 2017; 7:120-134. [PMID: 31214343 DOI: 10.1080/20476965.2017.1390635] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2017] [Revised: 09/12/2017] [Accepted: 10/05/2017] [Indexed: 10/28/2022] Open
Abstract
Few studies have examined how to identify future readmission of patients with a large number of repeat emergency department (ED) visits. We explore 30-day readmission risk prediction using Microsoft's AZURE machine learning software and compare five classification methods: Logistic Regression, Boosted Decision Trees (BDTs), Support Vector Machine (SVM), Bayes Point Machine (BPM), and Two-Class Neural Network (TCNN). We predict the last readmission visit of frequent ED patients extracted from the electronic health records of their 8455 penultimate visits. The methods show differential improvement, with the BDT indicating marginally better AUC (area under the ROC curve) than logistic regression and BPM, followed by the TCNN and SVM. A comparison of BDT and Logistic Regression results for correct and incorrect classification highlights the similarities and differences in the significant predictors identified by each method. Future research may incorporate time-varying covariates to identify other longitudinal factors that can lead to readmission risk reduction.
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Affiliation(s)
- Ofir Ben-Assuli
- Information Systems Department, Faculty of Business Administration, Ono Academic College, Kiryat Ono, Israel
| | - Rema Padman
- The H. John Heinz III College, Carnegie Mellon University, Pittsburgh, PA, USA
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Kim I, Ryu JM, Kim JM, Choi HJ, Lee SK, Yu JH, Lee JE, Kim SW, Nam SJ. Development of a Nomogram to Predict N2 or N3 Stage in T1-2 Invasive Breast Cancer Patients with No Palpable Lymphadenopathy. J Breast Cancer 2017; 20:270-278. [PMID: 28970853 PMCID: PMC5620442 DOI: 10.4048/jbc.2017.20.3.270] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2017] [Accepted: 09/12/2017] [Indexed: 11/30/2022] Open
Abstract
Purpose Subsequent to the American College of Surgeons Oncology Group (ACOSOG) Z0011 and After Mapping of the Axilla: Radiotherapy or Surgery (AMAROS) trials, complete axillary lymph node dissection is not routinely performed, even in cases where metastatic sentinel lymph nodes are detected. We investigated the percentage of N2 or N3 stages in T1–2 invasive breast cancer patients with no lymphadenopathy and developed a nomogram to predict the possibility of N2 or N3 stages in these patients. Methods We retrospectively reviewed the charts of invasive breast cancer patients who were clinically N0 stage, but had a positive sentinel or non-sentinel lymph node detected on sentinel lymph node biopsy. The association of potential risk factors with known outcomes (N2 or N3 stages) was tested using logistic regression analysis. Variables with p<0.05 in the multivariate analysis were included in the nomogram. Internal performance validation was carried out using a 5-fold cross validation method. Results Among a total of 1,437 patients, 1,355 patients had stage N1 disease (94.3%), while 82 had stage N2 or N3 disease (5.7%). Multivariate stepwise logistic regression analysis revealed lymphovascular invasion (p=0.008), T2 stage (p=0.026), metastatic lymph node ratio (p<0.001), and perinodal extension (p<0.001) as independent predictors of N2 or N3 stages. A nomogram was developed based on these factors. The area under the curve estimated from the receiver operating characteristic graph was 0.8050 in the model set and 0.8246 in the test set. Conclusion Our nomogram can be employed for the prediction of N2 or N3 stage among cases fulfilling the ACOSOG Z0011 or AMAROS criteria.
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Affiliation(s)
- Isaac Kim
- Division of Breast and Endocrine Surgery, Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Jai Min Ryu
- Division of Breast and Endocrine Surgery, Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Jae-Myung Kim
- Division of Breast and Endocrine Surgery, Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Hee Jun Choi
- Division of Breast and Endocrine Surgery, Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Se Kyung Lee
- Division of Breast and Endocrine Surgery, Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Jong Hwan Yu
- Division of Breast and Endocrine Surgery, Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Jeong Eon Lee
- Division of Breast and Endocrine Surgery, Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Seok Won Kim
- Division of Breast and Endocrine Surgery, Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Seok Jin Nam
- Division of Breast and Endocrine Surgery, Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
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Mujtaba G, Shuib L, Raj RG, Rajandram R, Shaikh K. Prediction of cause of death from forensic autopsy reports using text classification techniques: A comparative study. J Forensic Leg Med 2017; 57:41-50. [PMID: 29801951 DOI: 10.1016/j.jflm.2017.07.001] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2016] [Revised: 06/23/2017] [Accepted: 07/02/2017] [Indexed: 10/19/2022]
Abstract
OBJECTIVES Automatic text classification techniques are useful for classifying plaintext medical documents. This study aims to automatically predict the cause of death from free text forensic autopsy reports by comparing various schemes for feature extraction, term weighing or feature value representation, text classification, and feature reduction. METHODS For experiments, the autopsy reports belonging to eight different causes of death were collected, preprocessed and converted into 43 master feature vectors using various schemes for feature extraction, representation, and reduction. The six different text classification techniques were applied on these 43 master feature vectors to construct a classification model that can predict the cause of death. Finally, classification model performance was evaluated using four performance measures i.e. overall accuracy, macro precision, macro-F-measure, and macro recall. RESULTS From experiments, it was found that that unigram features obtained the highest performance compared to bigram, trigram, and hybrid-gram features. Furthermore, in feature representation schemes, term frequency, and term frequency with inverse document frequency obtained similar and better results when compared with binary frequency, and normalized term frequency with inverse document frequency. Furthermore, the chi-square feature reduction approach outperformed Pearson correlation, and information gain approaches. Finally, in text classification algorithms, support vector machine classifier outperforms random forest, Naive Bayes, k-nearest neighbor, decision tree, and ensemble-voted classifier. CONCLUSION Our results and comparisons hold practical importance and serve as references for future works. Moreover, the comparison outputs will act as state-of-art techniques to compare future proposals with existing automated text classification techniques.
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Affiliation(s)
- Ghulam Mujtaba
- Department of Information Systems, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia; Department of Computer Science, Sukkur Institute of Business Administration, Sukkur, Pakistan
| | - Liyana Shuib
- Department of Information Systems, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia.
| | - Ram Gopal Raj
- Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia.
| | - Retnagowri Rajandram
- Department of Surgery, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Khairunisa Shaikh
- Department of Social and Preventive Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia; Department of Community Medicine, Shaheed Mohtarma Benazir Bhutto Medical University, Larkana, Pakistan
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Odeh SM, Baareh AKM. A comparison of classification methods as diagnostic system: A case study on skin lesions. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2016; 137:311-319. [PMID: 28110734 DOI: 10.1016/j.cmpb.2016.09.012] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/04/2016] [Revised: 09/11/2016] [Accepted: 09/18/2016] [Indexed: 06/06/2023]
Abstract
BACKGROUND AND OBJECTIVE Numerous classification methods are currently available, but most of them were performed on different datasets. In this paper, different classification techniques were used for a diagnostic system on different skin lesions for the same data, which gives consistency for the data to have more accurate and better results. METHODS Four classification methods were proposed, a classical method based on K-Nearest Neighbor with Sequential Scanning selection technique for feature selection, a classical method with complex technique KNN with Genetic Algorithm, a complex method based on Artificial Neural Networks with Genetic Algorithm and an Adaptive Neuro-Fuzzy Inference System. RESULTS From the results obtained we can say that the performance of KNN with optimization of genetic algorithm for the feature selection was the best with an accuracy rate of 94%. The Adaptive Neuro-Fuzzy Inference System result was also good with an accuracy rate of 92%, and the other techniques' results were also satisfactory. CONCLUSION The improvement on the performance of the classifier depends on the feature selection methods. In addition, the diagnosis system as a decision support tool could be used to increase the performance of human experts to make a correct decision.
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Affiliation(s)
- Suhail M Odeh
- Computer and Information System Department, Bethlehem University, Bethlehem, Palestine.
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