Some Methods for Substantiating Diagnostic Decisions Made Using Machine Learning Algorithms

Losev, Alexander; Popov, Illarion; Petrenko, Alexander; Гудков, А. Г.; Vesnin, Sergey; Chizhikov, S. V.

doi:10.1007/s10527-022-10153-y

Cited by 2 publications

(5 citation statements)

References 14 publications

(9 reference statements)

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“…In several clinical examples [13] using two coefficients [9], data obtained using MWR showed to be ahead of MMG results by 1.5-2 years. More recently, artificial intelligencebased approaches have been applied to MWR data for early stage breast cancer prediction to approve diagnostic accuracy.…”

Section: Discussionmentioning

confidence: 93%

Passive Microwave Radiometry and microRNA Detection for Breast Cancer Diagnostics

Фишер¹,

Fisher²,

Chebanov

et al. 2022

Diagnostics

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Breast cancer prevention is an important health issue for women worldwide. In this study, we compared the conventional breast cancer screening exams of mammography and ultrasound with the novel approaches of passive microwave radiometry (MWR) and microRNA (miRNA) analysis. While mammography screening dynamics could be completed in 3–6 months, MWR provided a prediction in a matter of weeks or even days. Moreover, MWR has the potential of being complemented with miRNA diagnostics to further improve its predictive quality. These novel techniques can be used alone or in conjunction with more established techniques to improve early breast cancer diagnosis.

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Section: Discussionmentioning

confidence: 93%

Passive Microwave Radiometry and microRNA Detection for Breast Cancer Diagnostics

Фишер¹,

Fisher²,

Chebanov

et al. 2022

Diagnostics

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“…These are built from of various combinations of temperature differences measured at different points in the breast. Algorithms such as random forest, XGBoost, k-nearest neighbors, support vector machine, cascade-correlation neural network, deep neural network, convolutional neural network, decision trees, and naive Bayesian classifier are used to classify thermometric data [22,63,65]. These cause an increase in value sensitivity and specificity for cancer detection and a decrease in errors in diagnoses in early stages of tumor growth [16,63,66].…”

Section: Mwr Methods For Detecting Breast Cancermentioning

confidence: 99%

“…Heat release rates of Q (can) = 30 000 W m −3 , which is typical of fast growing tumors doubling in 100 days or less, allow detection of tumors up to 1 cm in diameter. The classification of even smaller tumors tumors requires a transition to the analysis of feature spaces, an increase in the sample size, and the use of heuristics [22,65,64]. Feature spaces are built in the form of matrices of various dimensions, the elements of which are temperature differences at different measurement points (See Figure 10).…”

Section: Influence Of Tumor Spatial Location On the Brightness Temper...mentioning

confidence: 99%

“…The data of breast temperature measurements in patients were obtained in accordance with the traditionally used scheme based on 22 points (Figure 10a) [12,22,63]. Figure 10b also shows the new measurement scheme at 38 points on the patient's body.…”

Section: Breast Thermometric Databasementioning

confidence: 99%

“…Machine learning and the application of the full range of artificial intelligence algorithms are a powerful tool for processing medical measurement data [4,14,21,22,23]. Convolutional neural networks can automatically detect signs of breast cancer in the thermal images with up to 98.95 percent accuracy [14].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Improving the Efficiency of Oncological Diagnosis of the Breast Based on the Combined Use of Simulation Modeling and Artificial Intelligence Algorithms

Хоперсков

Polyakov

2022

Algorithms

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This work includes a brief overview of the applications of the powerful and easy-to-perform method of microwave radiometry (MWR) for the diagnosis of various diseases. The main goal of this paper is to develop a method for diagnosing breast oncology based on machine learning algorithms using thermometric data, both real medical measurements and simulation results of MWR examinations. The dataset includes distributions of deep and skin temperatures calculated in numerical models of the dynamics of thermal and radiation fields inside biological tissue. The constructed combined dataset allows us to explore the limits of applicability of the MWR method for detecting weak tumors. We use convolutional neural networks and classic machine learning algorithms (k-nearest neighbors, naive Bayes classifier, support vector machine) to classify data. The construction of Kohonen self-organizing maps to explore the structure of our combined dataset demonstrated differences between the temperatures of patients with positive and negative diagnoses. Our analysis shows that the MWR can detect tumors with a radius of up to 0.5 cm if they are at the stage of rapid growth, when the tumor volume doubling occurs in approximately 100 days or less. The use of convolutional neural networks for MWR provides both high sensitivity (sens=0.86) and specificity (spec=0.82), which is an advantage over other methods for diagnosing breast cancer. A new modified scheme for medical measurements of IR temperature and brightness temperature is proposed for a larger number of points in the breast compared to the classical scheme. This approach can increase the effectiveness and sensitivity of diagnostics by several percent.

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Some Methods for Substantiating Diagnostic Decisions Made Using Machine Learning Algorithms

Cited by 2 publications

References 14 publications

Passive Microwave Radiometry and microRNA Detection for Breast Cancer Diagnostics

Passive Microwave Radiometry and microRNA Detection for Breast Cancer Diagnostics

Improving the Efficiency of Oncological Diagnosis of the Breast Based on the Combined Use of Simulation Modeling and Artificial Intelligence Algorithms

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