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Algorithms for Computer Aided Diagnosis: 2nd Edition

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A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms and Mathematical Models for Computer-Assisted Diagnostic Systems".

Deadline for manuscript submissions: closed (30 September 2025) | Viewed by 8578

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Dr. Ahmed Shaffie

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Mathematics and Computer Science Department, College of Natural Sciences and Mathematics, Louisiana State University of Alexandria, Alexandria, LA 71302, USA
Interests: medical Imaging; non-invasive computer-assisted diagnosis systems; image and video processing; machine learning; pattern recognition
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Special Issue Information

Dear Colleagues,

Algorithms stand at the forefront of modern medical diagnostics, catalyzing a paradigm shift away from conventional methods toward more efficient and precise healthcare solutions. Within the realm of medical technology, a diverse array of instruments come into play, including temperature probes, heart rate monitors, and respiration rate counters. However, it is algorithms that serve as the linchpin of this transformation. These computational powerhouses breathe life into these devices, interpreting complex physiological data with unprecedented accuracy. For instance, electrocardiogram readings capture the heart's electrical activity, while respiration rate data count chest movements per minute. Through the seamless incorporation of artificial intelligence techniques, the diagnostic process has been revolutionized, streamlining a once time-consuming and cumbersome endeavor.

In this Special Issue, we will delve deep into cutting-edge applications of AI in medical diagnostics, showcasing state-of-the-art approaches that promise to reshape the healthcare landscape. These algorithms, finely tuned for this purpose, are driving diagnoses in a myriad of diseases and disorders, utilizing data sourced from various medical instruments. As we are striving toward a future marked by comprehensive and automated computer-aided diagnosis, it is important to shine a light on these specialized machine learning algorithms. This journey transcends the confines of conventional practices, paving the way for innovative applications within the medical field. With each passing day, algorithms continue to reshape healthcare, propelling us toward a future in which precision and efficiency will define the standard of medical practice, ultimately leading to improved patient outcomes.

The scope of this Special Issue includes, but is not limited to, the following:

Innovative technological advancements in the medical field;
Developing computer-aided diagnosis systems;
Machine learning algorithms for medical images;
Artificial intelligence algorithms in healthcare;
Algorithm-driven wearable devices for comprehensive health assessment;
Enhanced medical image analysis with machine learning algorithms.

Dr. Ahmed Shaffie
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

algorithms
machine learning
artificial intelligence (AI)
computer-aided diagnosis (CAD)
healthcare revolution
medical devices

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Algorithms for Computer Aided Diagnosis in Algorithms (5 articles)
Algorithms for Computer Aided Diagnosis: 3rd Edition in Algorithms

Published Papers (8 papers)

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Research

24 pages, 1857 KB

Open AccessArticle

Ensemble Modeling of Multiple Physical Indicators to Dynamically Phenotype Autism Spectrum Disorder

by Marie Amale Huynh, Aaron Kline, Saimourya Surabhi, Kaitlyn Dunlap, Onur Cezmi Mutlu, Mohammadmahdi Honarmand, Parnian Azizian, Peter Washington and Dennis P. Wall

Algorithms 2025, 18(12), 764; https://doi.org/10.3390/a18120764 - 2 Dec 2025

Viewed by 202

Abstract

Early detection of Autism Spectrum Disorder (ASD), a neurodevelopmental condition characterized by social communication challenges, is essential for timely intervention. Naturalistic home videos collected via mobile applications offer scalable opportunities for digital diagnostics. We leveraged GuessWhat, a mobile game designed to engage parents and children, which has generated over 3000 structured videos from 382 children. From this collection, we curated a final analytic sample of 688 feature-rich videos centered on a single dyad, enabling more consistent modeling. We developed a two-step pipeline: (1) filtering to isolate high-quality videos, and (2) feature engineering to extract interpretable behavioral signals. Unimodal LSTM-based models trained on eye gaze, head position, and facial expression achieved test AUCs of 86% (95% CI: 0.79–0.92), 78% (95% CI: 0.69–0.86), and 67% (95% CI: 0.55–0.78), respectively. Late-stage fusion of unimodal outputs significantly improved predictive performance, yielding a test AUC of 90% (95% CI: 0.84–0.95). Our findings demonstrate the complementary value of distinct behavioral channels and support the feasibility of using mobile-captured videos for detecting clinically relevant signals. While further work is needed to improve generalizability and inclusivity, this study highlights the promise of real-time, scalable autism phenotyping for early interventions. Full article

(This article belongs to the Special Issue Algorithms for Computer Aided Diagnosis: 2nd Edition)

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28 pages, 1976 KB

Open AccessArticle

ECG Signal Analysis and Abnormality Detection Application

by Ales Jandera, Yuliia Petryk, Martin Muzelak and Tomas Skovranek

Algorithms 2025, 18(11), 689; https://doi.org/10.3390/a18110689 - 29 Oct 2025

Viewed by 765

Abstract

The electrocardiogram (ECG) signal carries information crucial for health assessment, but its analysis can be challenging due to noise and signal variability; therefore, automated processing focused on noise removal and detection of key features is necessary. This paper introduces an ECG signal analysis and abnormality detection application developed to process single-lead ECG signals. In this study, the Lobachevsky University database (LUDB) was used as the source of ECG signals, as it includes annotated recordings using a multi-class, multi-label taxonomy that covers several diagnostic categories, each with specific diagnoses that reflect clinical ECG interpretation practices. The main aim of the paper is to provide a tool that efficiently filters noisy ECG data, accurately detects the QRS complex, PQ and QT intervals, calculates heart rate, and compares these values with normal ranges based on age and gender. Additionally, a multi-class, multi-label SVM-based model was developed and integrated into the application for heart abnormality diagnostics, i.e., assigning one or several diagnoses from various diagnostic categories. The MATLAB-based application is capable of processing raw ECG signals, allowing the use of ECG records not only from LUDB but also from other databases. Full article

(This article belongs to the Special Issue Algorithms for Computer Aided Diagnosis: 2nd Edition)

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34 pages, 62676 KB

Open AccessArticle

Multimodal LLM vs. Human-Measured Features for AI Predictions of Autism in Home Videos

by Parnian Azizian, Mohammadmahdi Honarmand, Aditi Jaiswal, Aaron Kline, Kaitlyn Dunlap, Peter Washington and Dennis P. Wall

Algorithms 2025, 18(11), 687; https://doi.org/10.3390/a18110687 - 29 Oct 2025

Viewed by 976

Abstract

Autism diagnosis remains a critical healthcare challenge, with current assessments contributing to average diagnostic ages of 5 and extending to 8 in underserved populations. With the FDA approval of CanvasDx in 2021, the paradigm of human-in-the-loop AI diagnostics entered the pediatric market as the first medical device for clinically precise autism diagnosis at scale, while fully automated deep learning approaches have remained underdeveloped. However, the importance of early autism detection, ideally before 3 years of age, underscores the value of developing even more automated AI approaches, due to their potentials for scale, reach, and privacy. We present the first systematic evaluation of multimodal LLMs as direct replacements for human annotation in AI-based autism detection. Evaluating seven Gemini model variants (1.5–2.5 series) on 50 YouTube videos shows clear generational progression: version 1.5 models achieve 72–80% accuracy, version 2.0 models reach 80%, and version 2.5 models attain 85–90%, with the best model (2.5 Pro) achieving 89.6% classification accuracy using validated autism detection AI models (LR5)—comparable to the 88% clinical baseline and approaching crowdworker performance of 92–98%. The 24% improvement across two generations suggests the gap is closing. LLMs demonstrate high within-model consistency versus moderate human agreement, with distinct assessment strategies: LLMs focus on language/behavioral markers, crowdworkers prioritize social-emotional engagement, clinicians balance both. While LLMs have yet to match the highest-performing subset of human annotators in their ability to extract behavioral features that are useful for human-in-the-loop AI diagnosis, their rapid improvement and advantages in consistency, scalability, cost, and privacy position them as potentially viable alternatives for aiding diagnostic processes in the future. Full article

(This article belongs to the Special Issue Algorithms for Computer Aided Diagnosis: 2nd Edition)

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16 pages, 769 KB

Open AccessArticle

Evaluating Google Gemini’s Capability to Generate NBME-Standard Pharmacology Questions Using a 16-Criterion NBME Rubric

by Wesam Almasri, Marwa Saad and Changiz Mohiyeddini

Algorithms 2025, 18(10), 612; https://doi.org/10.3390/a18100612 - 29 Sep 2025

Viewed by 686

Abstract

Background: Large language models (LLMs) such as Google Gemini have demonstrated strong capabilities in natural language generation, but their ability to create medical assessment items aligned with National Board of Medical Examiners (NBME) standards remains underexplored. Objective: This study evaluated the quality of Gemini-generated NBME-style pharmacology questions using a structured rubric to assess accuracy, clarity, and alignment with examination standards. Methods: Ten pharmacology questions were generated using a standardized prompt and assessed independently by two pharmacology experts. Each item was evaluated using a 16-criterion NBME rubric with binary scoring. Inter-rater reliability was calculated (Cohen’s Kappa = 0.81) following a calibration session. Results: On average, questions met 14.3 of 16 criteria. Strengths included logical structure, appropriate distractors, and clinically relevant framing. Limitations included occasional pseudo-vignettes, cueing issues, and one instance of factual inaccuracy (albuterol mechanism of action). The evaluation highlighted Gemini’s ability to produce high-quality NBME-style questions, while underscoring concerns regarding sample size, reproducibility, and factual reliability. Conclusions: Gemini shows promise as a tool for generating pharmacology assessment items, but its probabilistic outputs, factual inaccuracies, and limited scope necessitate caution. Larger-scale studies, inclusion of multiple medical disciplines, incorporation of student performance data, and use of broader expert panels are recommended to establish reliability and educational applicability. Full article

(This article belongs to the Special Issue Algorithms for Computer Aided Diagnosis: 2nd Edition)

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23 pages, 1096 KB

Open AccessArticle

SAIN: Search-And-INfer, a Mathematical and Computational Framework for Personalised Multimodal Data Modelling with Applications in Healthcare

by Cristian S. Calude, Patrick Gladding, Alec Henderson and Nikola Kasabov

Algorithms 2025, 18(10), 605; https://doi.org/10.3390/a18100605 - 26 Sep 2025

Viewed by 406

Abstract

Personalised modelling has become dominant in personalised medicine and precision health. It creates a computational model for an individual based on large data repositories of existing personalised data, aiming to achieve the best possible personal diagnosis or prognosis and derive an informative explanation for it. Current methods are still working on a single data modality or treating all modalities with the same method. The proposed method, SAIN (Search-And-INfer), offers better results and an informative explanation for classification and prediction tasks on a new multimodal object (sample) using a database of similar multimodal objects. The method is based on different distance measures suitable for each data modality and introduces a new formula to aggregate all modalities into a single vector distance measure to find the closest objects to a new one, and then use them for a probabilistic inference. This paper describes SAIN and applies it to two types of multimodal data, cardiovascular diagnosis and EEG time series, modelled by integrating modalities, such as numbers, categories, images, and time series, and using a software implementation of SAIN. Full article

(This article belongs to the Special Issue Algorithms for Computer Aided Diagnosis: 2nd Edition)

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20 pages, 2590 KB

Open AccessArticle

A Data-Driven Intelligent Methodology for Developing Explainable Diagnostic Model for Febrile Diseases

by Constance Amannah, Kingsley Friday Attai and Faith-Michael Uzoka

Algorithms 2025, 18(4), 190; https://doi.org/10.3390/a18040190 - 26 Mar 2025

Cited by 4 | Viewed by 929

Abstract

Febrile diseases such as malaria, typhoid fever, tuberculosis, and HIV/AIDS pose significant diagnostic challenges in Low- and Middle-Income Countries (LMICs). Misdiagnosis leads to delayed treatment, increased healthcare costs, and higher mortality rates. This study presents a prototype diagnostic framework integrating machine learning (ML) and explainable artificial intelligence (XAI) to enhance diagnostic performance, interpretability, and usability in resource-constrained settings. A dataset of 3914 patient records from secondary and tertiary healthcare facilities was used to train and validate predictive models, employing Random Forest, Extreme Gradient Boost, and Multi-Layer Perceptron with optimized hyperparameters. To ensure transparency, XAI techniques such as Local Interpretable Model-Agnostic Explanations (LIME) and Large Language Models (LLMs) were integrated, enabling clinicians to understand model predictions. A prototype mobile-based diagnostic system was developed to explore its feasibility for real-time decision-making. The system features an intuitive interface, patient record management, and AI-driven diagnostic insights with visual and textual explanations. While usability testing with simulated case studies demonstrated its potential, real-world deployment and large-scale clinical validation are yet to be conducted. The system is designed with scalability in mind, allowing for future adaptation to different LMIC settings. However, limitations such as dataset imbalance and exclusion of pediatric data remain. Future research will focus on refining the model, expanding the dataset, and conducting extensive clinical validation before real-world implementation. This study serves as a foundational step toward AI-driven diagnostic tools in resource-limited healthcare environments. Full article

(This article belongs to the Special Issue Algorithms for Computer Aided Diagnosis: 2nd Edition)

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32 pages, 12235 KB

Open AccessArticle

Explainable MRI-Based Ensemble Learnable Architecture for Alzheimer’s Disease Detection

by Opeyemi Taiwo Adeniran, Blessing Ojeme, Temitope Ezekiel Ajibola, Ojonugwa Oluwafemi Ejiga Peter, Abiola Olayinka Ajala, Md Mahmudur Rahman and Fahmi Khalifa

Algorithms 2025, 18(3), 163; https://doi.org/10.3390/a18030163 - 13 Mar 2025

Cited by 3 | Viewed by 1874

Abstract

With the advancements in deep learning methods, AI systems now perform at the same or higher level than human intelligence in many complex real-world problems. The data and algorithmic opacity of deep learning models, however, make the task of comprehending the input data information, the model, and model’s decisions quite challenging. This lack of transparency constitutes both a practical and an ethical issue. For the present study, it is a major drawback to the deployment of deep learning methods mandated with detecting patterns and prognosticating Alzheimer’s disease. Many approaches presented in the AI and medical literature for overcoming this critical weakness are sometimes at the cost of sacrificing accuracy for interpretability. This study is an attempt at addressing this challenge and fostering transparency and reliability in AI-driven healthcare solutions. The study explores a few commonly used perturbation-based interpretability (LIME) and gradient-based interpretability (Saliency and Grad-CAM) approaches for visualizing and explaining the dataset, models, and decisions of MRI image-based Alzheimer’s disease identification using the diagnostic and predictive strengths of an ensemble framework comprising Convolutional Neural Networks (CNNs) architectures (Custom multi-classifier CNN, VGG-19, ResNet, MobileNet, EfficientNet, DenseNet), and a Vision Transformer (ViT). The experimental results show the stacking ensemble achieving a remarkable accuracy of 98.0% while the hard voting ensemble reached 97.0%. The findings present a valuable contribution to the growing field of explainable artificial intelligence (XAI) in medical imaging, helping end users and researchers to gain deep understanding of the backstory behind medical image dataset and deep learning model’s decisions. Full article

(This article belongs to the Special Issue Algorithms for Computer Aided Diagnosis: 2nd Edition)

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12 pages, 1696 KB

Open AccessArticle

Early Detection of Residual/Recurrent Lung Malignancies on Post-Radiation FDG PET/CT

by Liyuan Chen, Avanka Lowe and Jing Wang

Algorithms 2024, 17(10), 435; https://doi.org/10.3390/a17100435 - 1 Oct 2024

Viewed by 1714

Abstract

Positron Emission Tomography/Computed Tomography (PET/CT) using Fluorodeoxyglucose (FDG) is an important imaging modality for assessing treatment outcomes in patients with pulmonary malignant neoplasms undergoing radiation therapy. However, distinguishing between benign post-radiation changes and residual or recurrent malignancies on PET/CT images is challenging. Leveraging the potential of artificial intelligence (AI), we aimed to develop a hybrid fusion model integrating radiomics and Convolutional Neural Network (CNN) architectures to improve differentiation between benign post-radiation changes and residual or recurrent malignancies on PET/CT images. We retrospectively collected post-radiation PET/CTs with identified labels for benign changes or residual/recurrent malignant lesions from 95 lung cancer patients who received radiation therapy. Firstly, we developed separate radiomics and CNN models using handcrafted and self-learning features, respectively. Then, to build a more reliable model, we fused the probabilities from the two models through an evidential reasoning approach to derive the final prediction probability. Five-folder cross-validation was performed to evaluate the proposed radiomics, CNN, and fusion models. Overall, the hybrid fusion model outperformed the other two models in terms of sensitivity, specificity, accuracy, and the area under the curve (AUC) with values of 0.67, 0.72, 0.69, and 0.72, respectively. Evaluation results on the three AI models we developed suggest that handcrafted features and learned features may provide complementary information for residual or recurrent malignancy identification in PET/CT. Full article

(This article belongs to the Special Issue Algorithms for Computer Aided Diagnosis: 2nd Edition)

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