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AI-Based Automated Recognition and Detection in Healthcare

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 25 November 2024 | Viewed by 2055

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Dear Colleagues,

AI-based computer-aided diagnosis relies on the recognition and detection of disease symptoms from medical data. Evaluating AI models is crucial for driving progress and fostering competition. While traditional evaluation metrics like accuracy, sensitivity, and specificity are widely understood, they often overlook biases and noise present in the training data. This lack of consideration leads to ambiguity, making it challenging to compare and compete with AI-based solutions. In this Special Issue, we seek papers that surpass standard performance reporting. This can be achieved through preprocessing methods that identify biases and noise in the training data or through post-processing techniques that enhance performance measures with explainability. For this SI, we specify medical data as coming from sensors and taking the form of images and physiological signals.

Dr. Ningrong Lei
Dr. Oliver Faust
Prof. Dr. U Rajendra Acharya
Guest Editors

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Keywords

bias and noise
explainability
computer aided diagnosis
artificial intelligence

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17 pages, 1689 KiB

Open AccessArticle

Advancing Breast Cancer Diagnosis through Breast Mass Images, Machine Learning, and Regression Models

by Amira J. Zaylaa and Sylva Kourtian

Sensors 2024, 24(7), 2312; https://doi.org/10.3390/s24072312 - 05 Apr 2024

Viewed by 466

Abstract

Breast cancer results from a disruption of certain cells in breast tissue that undergo uncontrolled growth and cell division. These cells most often accumulate and form a lump called a tumor, which may be benign (non-cancerous) or malignant (cancerous). Malignant tumors can spread quickly throughout the body, forming tumors in other areas, which is called metastasis. Standard screening techniques are insufficient in the case of metastasis; therefore, new and advanced techniques based on artificial intelligence (AI), machine learning, and regression models have been introduced, the primary aim of which is to automatically diagnose breast cancer through the use of advanced techniques, classifiers, and real images. Real fine-needle aspiration (FNA) images were collected from Wisconsin, and four classifiers were used, including three machine learning models and one regression model: the support vector machine (SVM), naive Bayes (NB), k-nearest neighbors (k-NN), and decision tree (DT)-C4.5. According to the accuracy, sensitivity, and specificity results, the SVM algorithm had the best performance; it was the most powerful computational classifier with a 97.13% accuracy and 97.5% specificity. It also had around a 96% sensitivity for the diagnosis of breast cancer, unlike the models used for comparison, thereby providing an exact diagnosis on the one hand and a clear classification between benign and malignant tumors on the other hand. As a future research prospect, more algorithms and combinations of features can be considered for the precise, rapid, and effective classification and diagnosis of breast cancer images for imperative decisions. Full article

(This article belongs to the Special Issue AI-Based Automated Recognition and Detection in Healthcare)

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12 pages, 1283 KiB

Open AccessCommunication

Explainable Risk Prediction of Post-Stroke Adverse Mental Outcomes Using Machine Learning Techniques in a Population of 1780 Patients

by Chien Wei Oei, Eddie Yin Kwee Ng, Matthew Hok Shan Ng, Ru-San Tan, Yam Meng Chan, Lai Gwen Chan and Udyavara Rajendra Acharya

Sensors 2023, 23(18), 7946; https://doi.org/10.3390/s23187946 - 17 Sep 2023

Cited by 1 | Viewed by 1179

Abstract

Post-stroke depression and anxiety, collectively known as post-stroke adverse mental outcome (PSAMO) are common sequelae of stroke. About 30% of stroke survivors develop depression and about 20% develop anxiety. Stroke survivors with PSAMO have poorer health outcomes with higher mortality and greater functional disability. In this study, we aimed to develop a machine learning (ML) model to predict the risk of PSAMO. We retrospectively studied 1780 patients with stroke who were divided into PSAMO vs. no PSAMO groups based on results of validated depression and anxiety questionnaires. The features collected included demographic and sociological data, quality of life scores, stroke-related information, medical and medication history, and comorbidities. Recursive feature elimination was used to select features to input in parallel to eight ML algorithms to train and test the model. Bayesian optimization was used for hyperparameter tuning. Shapley additive explanations (SHAP), an explainable AI (XAI) method, was applied to interpret the model. The best performing ML algorithm was gradient-boosted tree, which attained 74.7% binary classification accuracy. Feature importance calculated by SHAP produced a list of ranked important features that contributed to the prediction, which were consistent with findings of prior clinical studies. Some of these factors were modifiable, and potentially amenable to intervention at early stages of stroke to reduce the incidence of PSAMO. Full article

(This article belongs to the Special Issue AI-Based Automated Recognition and Detection in Healthcare)

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