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Diagnostics
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Application of Machine Learning in Disease Screening, Diagnosis and Prognosis
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Application of Machine Learning in Disease Screening, Diagnosis and Prognosis

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: 31 August 2025 | Viewed by 1030

Special Issue Editor

Prof. Dr. Dee Pei

E-Mail Website
Guest Editor
Division of Endocrinology and Metabolism, Department of Internal Medicine, Fu Jen Catholic University Hospital, School of Medicine, College of Medicine, Fu Jen Catholic University, New Taipei 243, Taiwan
Interests: insulin resistance; pancreatic islet cell function; clinical application of machine learning, etc.
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue will delve into the Application of Machine Learning in Disease Screening, Diagnosis and Prognosis, showcasing the revolutionary impact of AI on healthcare. It will bring together groundbreaking research that illuminates how machine learning techniques can improve the precision and speed of disease detection, enabling earlier and more effective interventions. These contributions will explore the integration of these algorithms into diagnostic processes, enhancing their accuracy, and discuss the development of prognostic models that predict disease progression. Together, these articles demonstrate the transformative potential of machine learning for advancing medical care and patient outcomes.

Prof. Dr. Dee Pei
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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Diagnostics is an international peer-reviewed open access semimonthly 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 2600 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

  • diagnosis
  • screening
  • marker
  • prognosis
  • machine learning

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Published Papers (3 papers)

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Research

12 pages, 1158 KiB  
Article
Equation Built by Multiple Adaptive Regression Spline to Estimate Biological Age in Healthy Postmenopausal Women in Taiwan
by Chun-Feng Chang, Ta-Wei Chu, Chi-Hao Liu, Sheng-Tang Wu and Chung-Chi Yang
Diagnostics 2025, 15(9), 1147; https://doi.org/10.3390/diagnostics15091147 (registering DOI) - 30 Apr 2025
Abstract
Background: Biological age (BA) is a better representative of health status than chronological age (CA), as it uses different biological markers to quantify cellular and systemic change status. However, BA can be difficult to accurately estimate using current methods. This study uses multiple [...] Read more.
Background: Biological age (BA) is a better representative of health status than chronological age (CA), as it uses different biological markers to quantify cellular and systemic change status. However, BA can be difficult to accurately estimate using current methods. This study uses multiple adaptive regression spline (MARS) to build an equation to estimate BA among healthy postmenopausal women, thereby potentially improving the efficiency and accuracy of BA assessment. Methods: A total of 11,837 healthy women were enrolled (≥51 years old), excluding participants with metabolic syndrome variable values outside two standard deviations. MARS was applied, with the results compared to traditional multiple linear regression (MLR). The method with the smaller degree of estimation error was considered to be more accurate. The lower prediction errors yielded by MARS compared to the MLR method suggest that MARS performs better than MLR. Results: The equation derived from MARS is depicted. It could be noted that BA could be determined by marriage, systolic blood pressure (SBP), diastolic blood pressure (DBP), waist–hip ratio (WHR), alkaline phosphatase (ALP), lactate dehydrogenase (LDH), creatinine (Cr), carcinoembryonic antigen (CEA), bone mineral density (BMD), education level, and income. The MARS equation is generated. Conclusions: Using MARS, an equation was built to estimate biological age among healthy postmenopausal women in Taiwan. This equation could be used as a reference for calculating BA in general. Our equation showed that the most important factor was BMD, followed by WHR, Cr, marital status, education level, income, CEA, blood pressure, ALP, and LDH. Full article
(This article belongs to the Special Issue Application of Machine Learning in Disease Screening, Diagnosis and Prognosis)
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20 pages, 704 KiB  
Article
A Comparison of Skin Lesions’ Diagnoses Between AI-Based Image Classification, an Expert Dermatologist, and a Non-Expert
by Lior Mevorach, Alessio Farcomeni, Giovanni Pellacani and Carmen Cantisani
Diagnostics 2025, 15(9), 1115; https://doi.org/10.3390/diagnostics15091115 - 28 Apr 2025
Viewed by 176
Abstract
Background/Objectives: This study aims to evaluate and compare the diagnostic accuracy of skin lesion classification among three different classifiers: AI-based image classification, an expert dermatologist, and a non-expert. Given the rising interest in artificial intelligence (AI) within dermatology, it is crucial to [...] Read more.
Background/Objectives: This study aims to evaluate and compare the diagnostic accuracy of skin lesion classification among three different classifiers: AI-based image classification, an expert dermatologist, and a non-expert. Given the rising interest in artificial intelligence (AI) within dermatology, it is crucial to assess its performance against human expertise to determine its viability as a reliable diagnostic tool. Methods: This reader study utilized a set of pre-labeled skin lesion images, which were assessed by an AI-based image classification system, an expert dermatologist, and a non-expert. The accuracy of each classifier was measured and compared against the ground truth labels. Statistical analysis was conducted to compare the diagnostic accuracy of the three classifiers. Results: The AI-based image classification system exhibited high sensitivity (93.59%) and specificity (70.42%) in identifying malignant lesions. The AI model demonstrated similar sensitivity and notably higher specificity compared to the expert dermatologist and non-expert. However, both the expert and non-expert provided valuable diagnostic insights, especially in classifying specific cases like melanoma. The results indicate that AI has the potential to assist dermatologists by providing a second opinion and enhancing diagnostic accuracy. Conclusions: This study concludes that AI-based image classification systems may serve as a valuable tool in dermatological diagnostics, potentially augmenting the capabilities of dermatologists. However, it is not yet a replacement for expert clinical judgment. Continued improvements and validation in diverse clinical settings are necessary before widespread implementation. Full article
(This article belongs to the Special Issue Application of Machine Learning in Disease Screening, Diagnosis and Prognosis)
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13 pages, 35894 KiB  
Article
An Artificial Intelligence Approach to the Craniofacial Recapitulation of Crisponi/Cold-Induced Sweating Syndrome 1 (CISS1/CISS) from Newborns to Adolescent Patients
by Giulia Pascolini, Dario Didona and Luigi Tarani
Diagnostics 2025, 15(5), 521; https://doi.org/10.3390/diagnostics15050521 - 21 Feb 2025
Viewed by 573
Abstract
Background/Objectives: Crisponi/cold-induced sweating syndrome 1 (CISS1/CISS, MIM#272430) is a genetic disorder due to biallelic variants in CRFL1 (MIM*604237). The related phenotype is mainly characterized by abnormal thermoregulation and sweating, facial muscle contractions in response to tactile and crying-inducing stimuli at an early [...] Read more.
Background/Objectives: Crisponi/cold-induced sweating syndrome 1 (CISS1/CISS, MIM#272430) is a genetic disorder due to biallelic variants in CRFL1 (MIM*604237). The related phenotype is mainly characterized by abnormal thermoregulation and sweating, facial muscle contractions in response to tactile and crying-inducing stimuli at an early age, skeletal anomalies (camptodactyly of the hands, scoliosis), and craniofacial dysmorphisms, comprising full cheeks, micrognathia, high and narrow palate, low-set ears, and a depressed nasal bridge. The condition is associated with high lethality during the neonatal period and can benefit from timely symptomatic therapy. Methods: We collected frontal images of all patients with CISS1/CISS published to date, which were analyzed with Face2Gene (F2G), a machine-learning technology for the facial diagnosis of syndromic phenotypes. In total, 75 portraits were subdivided into three cohorts, based on age (Cohort 1 and 2) and the presence of the typical facial trismus (Cohort 3). These portraits were uploaded to F2G to test their suitability for facial analysis and to verify the capacity of the AI tool to correctly recognize the syndrome based on the facial features only. The photos which passed this phase (62 images) were fed to three different AI algorithms—DeepGestalt, Facial D-Score, and GestaltMatcher. Results: The DeepGestalt algorithm results, including the correct diagnosis using a frontal portrait, suggested a similar facial phenotype in the first two cohorts. Cohort 3 seemed to be highly differentiable. The results were expressed in terms of the area under the curve (AUC) of the receiver operating characteristic (ROC) curve and p Value. The Facial D-Score values indicated the presence of a consistent degree of dysmorphic signs in the three cohorts, which was also confirmed by the GestaltMatcher algorithm. Interestingly, the latter allowed us to identify overlapping genetic disorders. Conclusions: This is the first AI-powered image analysis in defining the craniofacial contour of CISS1/CISS and in determining the feasibility of training the tool used in its clinical recognition. The obtained results showed that the use of F2G can reveal valid support in the diagnostic process of CISS1/CISS, especially in more severe phenotypes, manifesting with facial contractions and potentially lethal consequences. Full article
(This article belongs to the Special Issue Application of Machine Learning in Disease Screening, Diagnosis and Prognosis)
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