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Fetal Cerebral Blood Flow (Dys)autoregulation
From Lab to Clinic: Artificial Intelligence with Spectroscopic Liquid Biopsies
Multi-Task Deep Learning on MRI for Tumor Segmentation and Treatment Response Prediction in an Experimental Model of Hepatocellular Carcinoma
Current Concepts of the Applications and Treatment Implications of Drug-Induced Sleep Endoscopy for the Management of Obstructive Sleep Apnoea

Journal Description

Diagnostics

Diagnostics is an international, peer-reviewed, open access journal on medical diagnosis published semimonthly online by MDPI. The British Neuro-Oncology Society (BNOS), the International Society for Infectious Diseases in Obstetrics and Gynaecology (ISIDOG) and the Swiss Union of Laboratory Medicine (SULM) are affiliated with Diagnostics and their members receive a discount on the article processing charges.

Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within Scopus, SCIE (Web of Science), PubMed, PMC, Embase, Inspec, CAPlus / SciFinder, and other databases.
Journal Rank: JCR - Q1 (Medicine, General and Internal) / CiteScore - Q2 (Internal Medicine)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 21.6 days after submission; acceptance to publication is undertaken in 2.7 days (median values for papers published in this journal in the second half of 2025).
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Companion journals for Diagnostics include: LabMed and AI in Medicine.

Impact Factor: 3.3 (2024); 5-Year Impact Factor: 3.3 (2024)

Imprint Information Journal Flyer Open Access ISSN: 2075-4418

Latest Articles

15 pages, 4088 KB

Open AccessCase Report

Candida albicans Meningoencephalitis After Vestibular Schwannoma Surgery: An Autopsy-Confirmed Case Report

by Jessika Camatti, Matteo Tudini, Maria Paola Bonasoni, Anna Laura Santunione, Rossana Cecchi, Erjon Radheshi and Edoardo Carretto

Diagnostics 2026, 16(2), 228; https://doi.org/10.3390/diagnostics16020228 (registering DOI) - 11 Jan 2026

Background and Clinical Significance: Cerebral candidiasis (Candida albicans meningoencephalitis) is a rare but severe central nervous system (CNS) infection, usually associated with neurosurgical procedures or indwelling devices. Diagnosis is challenging due to frequent negativity of cerebrospinal fluid (CSF) cultures, and mortality remains [...] Read more.

Background and Clinical Significance: Cerebral candidiasis (Candida albicans meningoencephalitis) is a rare but severe central nervous system (CNS) infection, usually associated with neurosurgical procedures or indwelling devices. Diagnosis is challenging due to frequent negativity of cerebrospinal fluid (CSF) cultures, and mortality remains high despite antifungal therapy. Case Presentation: We describe a 64-year-old woman who underwent retrosigmoid resection of a left vestibular schwannoma. The early postoperative course was complicated by fever, neurological deterioration, and hydrocephalus requiring external CSF drainage. Multiple lumbar punctures revealed inflammatory CSF profiles but persistently negative cultures. One month post-surgery, intraoperative samples from mastoid repair material grew Candida albicans, prompting antifungal therapy. Despite treatment, the patient experienced fluctuating neurological status and required multiple external ventricular drains. Three months after surgery, she clinically deteriorated and died. Autopsy showed diffuse meningeal thickening and purulent exudates at the brain base and posterior fossa. Histopathology confirmed chronic lympho-histiocytic meningitis with necrotizing foci containing Candida albicans. Conclusions: This case underscores the diagnostic and therapeutic challenges of post-neurosurgical Candida CNS infections. Repeatedly negative CSF cultures delayed diagnosis, emphasizing the value of ancillary tests such as β-d-glucan and molecular assays. Even with antifungal therapy, prognosis is poor. Autopsy remains essential for uncovering fatal healthcare-associated fungal infections and informing clinical vigilance and medico-legal assessment. Full article

(This article belongs to the Special Issue Diagnostic Methods in Forensic Pathology, Third Edition)

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10 pages, 1114 KB

Open AccessArticle

Development of AI-Based Laryngeal Cancer Diagnostic Platform Using Laryngoscope Images

by Hye-Bin Jang, Seung Bae Park, Sang Jun Lee, Gyung Sueng Yang, A Ram Hong and Dong Hoon Lee

Diagnostics 2026, 16(2), 227; https://doi.org/10.3390/diagnostics16020227 (registering DOI) - 11 Jan 2026

Objective: To develop and evaluate artificial intelligence (AI)-based models for detecting laryngeal cancer using laryngoscope images. Methods: Two deep learning models were designed. The first identified and selected vocal cord images from laryngoscope datasets; the second localized laryngeal cancer within the [...] Read more.

Objective: To develop and evaluate artificial intelligence (AI)-based models for detecting laryngeal cancer using laryngoscope images. Methods: Two deep learning models were designed. The first identified and selected vocal cord images from laryngoscope datasets; the second localized laryngeal cancer within the selected images. Both employed FCN–ResNet101. Datasets were annotated by otolaryngologists, preprocessed (cropping, normalization), and augmented (horizontal/vertical flip, grid distortion, color jitter). Performance was assessed using Intersection over Union (IoU), Dice score, accuracy, precision, recall, F1 score, and per-image inference time. Results: The vocal cord selection model achieved a mean IoU of 0.6534 and mean Dice score of 0.7692, with image-level accuracy of 0.9972. The laryngeal cancer model achieved a mean IoU of 0.6469 and mean Dice score of 0.7515, with accuracy of 0.9860. Real-time inference was observed (0.0244–0.0284 s/image). Conclusions: By integrating a vocal cord selection model with a lesion detection model, the proposed platform enables accurate and fast detection of laryngeal cancer from laryngoscope images under the current experimental setting. Full article

(This article belongs to the Section Machine Learning and Artificial Intelligence in Diagnostics)

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13 pages, 989 KB

Open AccessArticle

Cone-Beam Computed Tomography Laser-Guided Transthoracic Needle Biopsy for Pulmonary Lesions in a Hybrid Operating Room: Feasibility Study by an Interventional Pulmonologist

by Lun-Che Chen, Po-Keng Su, Geng-Ning Hu, Shwetambara Malwade, Wen-Yuan Chung, Ling-Kai Chang and Shun-Mao Yang

Diagnostics 2026, 16(2), 226; https://doi.org/10.3390/diagnostics16020226 (registering DOI) - 10 Jan 2026

Background/Objectives: Percutaneous transthoracic needle biopsy (PTNB) using advanced navigation techniques is increasingly performed; however, pulmonologists’ experience remains limited. This study reports an interventional pulmonologist’s initial experience with cone-beam computed tomography (CBCT) laser-guided PTNB and the diagnostic performance for lesions with diameters greater than [...] Read more.

Background/Objectives: Percutaneous transthoracic needle biopsy (PTNB) using advanced navigation techniques is increasingly performed; however, pulmonologists’ experience remains limited. This study reports an interventional pulmonologist’s initial experience with cone-beam computed tomography (CBCT) laser-guided PTNB and the diagnostic performance for lesions with diameters greater than or less than 20 mm. Methods: We retrospectively analysed the data of patients who underwent PTNB in a C-arm CBCT-equipped hybrid operating room between July 2020 and March 2024. All patients underwent the biopsy procedure under local anaesthesia. This was preceded by an initial 3D scan for planning of the needle route, followed by coaxial needle insertion. A post-procedural scan was also performed to identify complications. Results: Seventy-seven patients were enrolled in the study. The median distances of the needle path from the skin to the pleura and from the pleura to the lesion were 33.4 mm and 31.7 mm, respectively. The median number of tissue samplings was 4.9 ± 1.8. The median operating room duration was 51.5 ± 25.7 min, respectively. The median total dose area product was 8485.4 ± 5819.9 µGym². The sensitivity and specificity of our study findings were 93.3% (56/60) and 100%, while the accuracy was 94.8% (73/77). The overall complication rate was 13%. Conclusions: PTNB procedure by pulmonologists is a feasible and safe, single-operator workflow in a hybrid operating room. It can be performed under CBCT laser guidance with a similar diagnostic yield, acceptable radiation exposure and procedure duration, and minimal or manageable complications. Full article

(This article belongs to the Special Issue Advances in Interventional Pulmonology)

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21 pages, 7689 KB

Open AccessArticle

Diffusion-Weighted Whole-Body Magnetic Resonance Imaging with Background Body Signal Suppression for Differentiating Infectious from Non-Infectious Aortitis

by Jien Saito, Masahiro Muto, Masafumi Tada, Isao Yokota, Shinji Kamiya, Yukihide Numata, Hideki Sasaki, Takuya Hashizume, Kenji Iwata, Miki Asano and Satoru Wakasa

Diagnostics 2026, 16(2), 225; https://doi.org/10.3390/diagnostics16020225 (registering DOI) - 10 Jan 2026

Background/Objectives: This study examined the clinical utility of diffusion-weighted whole-body magnetic resonance imaging with background body signal suppression (DWIBS) for differentiating infectious from non-infectious aortitis. Methods: The study included 32 patients with suspected inflammatory aortitis who underwent non-contrast computed tomography (NCCT) and magnetic [...] Read more.

Background/Objectives: This study examined the clinical utility of diffusion-weighted whole-body magnetic resonance imaging with background body signal suppression (DWIBS) for differentiating infectious from non-infectious aortitis. Methods: The study included 32 patients with suspected inflammatory aortitis who underwent non-contrast computed tomography (NCCT) and magnetic resonance imaging. We evaluated the diagnostic performance of DWIBS using the spinal cord as a reference, NCCT, and their combination. The diagnosis of infectious aortitis was adjudicated based on imaging, clinical, and laboratory findings. We conducted a sensitivity analysis using a stricter definition of infectious aortitis that required both surgical and microbiological confirmation. Results: Fifteen patients were diagnosed with infectious aortitis. The sensitivity, specificity, and areas under the receiver operating characteristic curves were 93.3%, 70.6%, and 0.82, respectively, for NCCT; 93.3%, 76.5%, and 0.85, respectively, for DWIBS; and 86.7%, 94.1%, and 0.90, respectively, for the combination of both modalities. In the sensitivity analysis, the combined DWIBS and NCCT approach demonstrated a specificity of 87.5% and a sensitivity of 70.8%. Conclusions: DWIBS using the spinal cord as a reference appears to be a promising diagnostic tool for differentiating infectious from non-infectious aortitis, especially when combined with NCCT. Full article

(This article belongs to the Section Medical Imaging and Theranostics)

17 pages, 1110 KB

Open AccessCase Report

Giant Right Sphenoid Wing Meningioma as a Reversible Frontal Network Lesion: A Pseudo-bvFTD Case with Venous-Sparing Skull-Base Resection

by Valentin Titus Grigorean, Octavian Munteanu, Felix-Mircea Brehar, Catalina-Ioana Tataru, Matei Serban, Razvan-Adrian Covache-Busuioc, Corneliu Toader, Cosmin Pantu, Alexandru Breazu and Lucian Eva

Diagnostics 2026, 16(2), 224; https://doi.org/10.3390/diagnostics16020224 (registering DOI) - 10 Jan 2026

Background and Clinical Significance: Giant sphenoid wing meningiomas are generally viewed as skull base masses that compress frontal centers and their respective pathways gradually enough to cause a dysexecutive–apathetic syndrome, which can mimic primary neurodegenerative disease. The aim of this report is [...] Read more.

Background and Clinical Significance: Giant sphenoid wing meningiomas are generally viewed as skull base masses that compress frontal centers and their respective pathways gradually enough to cause a dysexecutive–apathetic syndrome, which can mimic primary neurodegenerative disease. The aim of this report is to illustrate how bedside phenotyping and multimodal imaging can disclose similar clinical presentations as surgically treatable network lesions. Case Presentation: An independent, right-handed older female developed an incremental, two-year decline of her ability to perform executive functions, extreme apathy, lack of instrumental functioning, and a frontal-based gait disturbance, culminating in a first generalized seizure and a newly acquired left-sided upper extremity pyramidal sign. Standardized neuropsychological evaluation revealed a predominant frontal-based dysexecutive profile with intact core language skills, similar to behavioral-variant frontotemporal dementia (bvFTD). MRI demonstrated a large, right fronto-temporo-basal extra-axial tumor attached to the sphenoid wing with homogeneous postcontrast enhancement, significant vasogenic edema within the frontal projection pathways, and a marked midline displacement of structures with an open venous pathway. With the use of a skull-base flattening pterional craniotomy with early devascularization followed by staged internal debulking, arachnoid preserving dissection, and conservative venous preservation, the surgeon accomplished a Simpson Grade I resection. Sequential improvements in the patient’s frontal “re-awakening” were demonstrated through postoperative improvements on standardized stroke, cognitive and functional assessment scales that correlated well with persistent decompression and symmetric ventricles on follow-up images. Conclusions: This case illustrates the possibility of a non-dominant sphenoid wing meningioma resulting in a pseudo-degenerative frontal syndrome and its potential for reversal if recognized as a network lesion and treated with tailored, venous-sparing skull-base surgery. Contrast-enhanced imaging and routine frontal testing in atypical “dementia” presentations may aid in identifying additional patients with potentially surgically remediable cases. Full article

(This article belongs to the Special Issue Brain/Neuroimaging 2025–2026)

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5 pages, 509 KB

Open AccessInteresting Images

Unveiling Hidden Abscesses: The Clinical Utility of Diffusion-Weighted Whole-Body Imaging with Background Suppression (DWIBS) in Metastatic Abscess Screening

by Koji Hayashi, Maho Hayashi, Rina Izumi, Mamiko Sato, Seigaku Hayashi, Toshiko Iwasaki, Ippei Sakamaki and Yasutaka Kobayashi

Diagnostics 2026, 16(2), 223; https://doi.org/10.3390/diagnostics16020223 (registering DOI) - 10 Jan 2026

A 74-year-old man with type 2 diabetes presented with fever, urinary retention, and urinary difficulties. Initial abdominal Computed Tomography (CT) suggested acute pyelonephritis, but a low-density area in the prostate was overlooked. Following the confirmation of methicillin-resistant Staphylococcus aureus (MRSA) in blood and [...] Read more.

A 74-year-old man with type 2 diabetes presented with fever, urinary retention, and urinary difficulties. Initial abdominal Computed Tomography (CT) suggested acute pyelonephritis, but a low-density area in the prostate was overlooked. Following the confirmation of methicillin-resistant Staphylococcus aureus (MRSA) in blood and urine cultures, comprehensive screening for metastatic abscesses was necessitated. Diffusion-weighted whole-body imaging with background suppression (DWIBS) was utilized and clearly identified a prostatic abscess (PA), nephritis, urethritis, and subcutaneous cysts. These findings also raised suspicion of pyogenic vertebral osteomyelitis. Crucially, the PA, urethritis, subcutaneous cysts, and potentially the vertebral osteomyelitis were either overlooked or not detected by initial CT imaging. DWIBS allows for simultaneous whole-body screening and serves as a useful adjunctive tool for identifying minute abscesses, which may assist in detecting inflammatory foci that are sometimes overlooked by conventional imaging. Unlike CT, DWIBS avoids radiation and contrast agents, and is significantly more cost-effective than positron emission tomography-CT (PET-CT). DWIBS can thus serve as a useful, non-invasive tool for the early detection and exclusion of abscesses in other organs when metastatic abscess formation is suspected or cultures are positive for microorganisms causing metastatic abscesses. Full article

(This article belongs to the Special Issue Advances in Inflammation and Infection Imaging: 2nd Edition)

13 pages, 647 KB

Open AccessSystematic Review

Thermography and Infrared Spectroscopy in the Detection of Periodontal Inflammation In Vivo: A Systematic Review

by Heythem Nassim Guetatlia, Mickael Gette, Laurent Estrade, Victor Rimbaud, Frédéric Denis, Gaël Y. Rochefort and Matthieu Renaud

Diagnostics 2026, 16(2), 222; https://doi.org/10.3390/diagnostics16020222 (registering DOI) - 10 Jan 2026

Background/Objectives: Periodontal inflammation is a key feature of periodontal diseases, but traditional diagnostic methods are limited by invasiveness and radiation exposure. This systematic review aims to evaluate the potential of thermography and infrared spectroscopy for the in vivo detection of periodontal inflammation and [...] Read more.

Background/Objectives: Periodontal inflammation is a key feature of periodontal diseases, but traditional diagnostic methods are limited by invasiveness and radiation exposure. This systematic review aims to evaluate the potential of thermography and infrared spectroscopy for the in vivo detection of periodontal inflammation and to assess their reliability for clinical use. Methods: In accordance with PRISMA guidelines, an electronic search of the MEDLINE (PubMed) database was conducted to identify relevant studies published between 2000 and October 2025 that investigated these imaging modalities in periodontal inflammation diagnosis. Results: The search identified 310 records; after exclusions, 13 studies were included, comprising 7 thermography studies and 6 infrared spectroscopy studies, for a total of 712 patients. The included studies demonstrated the feasibility of thermography and infrared spectroscopy for detecting inflammatory changes in periodontal tissues in vivo. These non-invasive imaging techniques may help overcome the limitations of conventional clinical and radiographic diagnostic methods, particularly invasiveness and exposure to ionizing radiation. Conclusions: This field remains underexplored, and further studies are required to validate diagnostic performance, standardize methodologies, and determine their clinical applicability in routine periodontal practice. Full article

(This article belongs to the Special Issue Diagnostic Approach and Innovations in the Different Dentistry Fields, 2nd Edition)

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18 pages, 309 KB

Open AccessReview

Significance of Follicle-Stimulating Hormone Receptor Gene Single-Nucleotide Polymorphism rs6165/rs6166 Analysis for Infertility-Associated Ovarian Disease Susceptibility Prediction and Optimized Individualized Ovulation Induction/Ovarian Stimulation

by Kotaro Kitaya, Atsumi Hamazaki, Naoko Kobayashi, Takako Mihara and Masaya Mihara

Diagnostics 2026, 16(2), 221; https://doi.org/10.3390/diagnostics16020221 (registering DOI) - 10 Jan 2026

Follicle-stimulating hormone receptor (FSHR) is expressed on the plasma membrane of granulosa cells in the ovarian follicles. FSHR is involved in the development and maturation of Graafian follicles, along with granulosa proliferation and estrogen synthesis. There are two well-characterized non-synonymous single-nucleotide gene polymorphisms [...] Read more.

Follicle-stimulating hormone receptor (FSHR) is expressed on the plasma membrane of granulosa cells in the ovarian follicles. FSHR is involved in the development and maturation of Graafian follicles, along with granulosa proliferation and estrogen synthesis. There are two well-characterized non-synonymous single-nucleotide gene polymorphisms in the exon 10 of the human FSHR gene, namely rs6165 (c.919G>A, Ala307Thr) and rs6166 (c.2039A>G, Ser680Asn). Recent research clarifies the association of rs6165/rs6166 with susceptibility to infertility-associated ovarian diseases, ranging from polycystic ovarian syndrome, premature ovarian insufficiency, endometriosis, to ovarian cancer, along with response/resistance to ovulation induction/ovarian stimulation with clomiphene citrate, letrozole, metformin, FSH preparations, and adjunctive growth hormone in infertility treatment. This narrative review aims to update the knowledge on the relationship among rs6165/rs6166, infertility etiology, and differential responses to oral ovulation induction agents, FSH preparations, and adjunctive growth hormone. The re6165/rs6166 genotype-guided choice of individualized ovulation stimulation preparations has great potential to reduce unexpected poor or high ovarian responses in ovulation induction and ovarian stimulation and improve clinical outcomes in reproductive medicine. Current evidence is insufficient, and further studies are warranted to ascertain its potential for clinical implementation. Full article

(This article belongs to the Section Pathology and Molecular Diagnostics)

15 pages, 888 KB

Open AccessArticle

Optimizing Layer Thickness in Multi-Planar Volume Reconstruction for Distinguishing Invasive Adenocarcinoma from Non-Invasive and Minimally Invasive Lesions in Pulmonary Nodules (≤15 mm): A Comparative Study with Conventional Lung Window Settings

by Ke Zhang, Wen-Tao Zhang, Ji-Wen Huo, Wei-Wei Jing, Si-Fan Chen, Mao-Lu Tan and Fa-Jin Lv

Diagnostics 2026, 16(2), 220; https://doi.org/10.3390/diagnostics16020220 - 9 Jan 2026

Objective: To determine the optimal layer thickness for multi-planar volume reconstruction (MPVR) in differentiating invasive adenocarcinoma from non-invasive and minimally invasive lesions in pulmonary nodules (≤ 15 mm). Materials and Methods: This retrospective study enrolled a total of 601 solitary pulmonary nodules (≤15 [...] Read more.

Objective: To determine the optimal layer thickness for multi-planar volume reconstruction (MPVR) in differentiating invasive adenocarcinoma from non-invasive and minimally invasive lesions in pulmonary nodules (≤ 15 mm). Materials and Methods: This retrospective study enrolled a total of 601 solitary pulmonary nodules (≤15 mm) between June 2020 and February 2024, including 404 invasive adenocarcinomas (IAC), 80 micro-invasive adenocarcinomas (MIAs), 96 adenocarcinomas in situ (AISs), and 21 atypical adenomatous hyperplasias (AAHs). Thin-section computed tomography (TSCT) images with lung window settings and MPVR images with varying layer thicknesses (ranging from 2 to 14 mm with intervals of 2 mm) were analyzed for their morphological characteristics. Multivariate logistic regression analysis was employed to develop models for differentiating invasive adenocarcinoma from non-invasive and minimally invasive lesions. The model’s performances were further evaluated and compared to identify the optimal thickness for diagnosis. Results: The 10 mm MPVR model exhibited the best performance (AUC: 0.910, 95% CI [confidence interval]: 0.905–0.914; sensitivity: 0.906; specificity: 0.753; accuracy: 0.856; PPV: 0.883; and NPV: 0.796). As the MPVR layer thickness increased from 2 mm to 10 mm, model performance improved, with sensitivity rising from 0.870 to 0.906, specificity rising from 0.519 to 0.753, and accuracy increasing from 0.755 to 0.856. However, for layer thicknesses of 12 mm to 14 mm, all of them decreased. Furthermore, the overall performance of the 10 mm MPVR model surpassed that of the lung window model (AUC: 0.841, 95% CI: 0.831–0.844; sensitivity: 0.787; specificity: 0.760; accuracy: 0.778; PPV: 0.871; and NPV: 0.634). Conclusions: MPVR images with varying layer thicknesses can effectively distinguish invasive adenocarcinoma from non-invasive and minimally invasive lesions in pulmonary nodules ≤ 15 mm. Notably, the diagnostic performance of the 10 mm model was superior to model built with TSCT images, showing great potential as a precise and non-invasive tool for assessing the invasiveness of adenocarcinomas ≤ 15 mm. Full article

(This article belongs to the Section Medical Imaging and Theranostics)

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15 pages, 660 KB

Open AccessArticle

Comparative Evaluation of Deep Learning Models for the Classification of Impacted Maxillary Canines on Panoramic Radiographs

by Nazlı Tokatlı, Buket Erdem, Mustafa Özcan, Begüm Turan Maviş, Çağla Şar and Fulya Özdemir

Diagnostics 2026, 16(2), 219; https://doi.org/10.3390/diagnostics16020219 - 9 Jan 2026

Background/Objectives: The early and accurate identification of impacted teeth in the maxilla is critical for effective dental treatment planning. Traditional diagnostic methods relying on manual interpretation of radiographic images are often time-consuming and subject to variability. Methods: This study presents a deep learning-based [...] Read more.

Background/Objectives: The early and accurate identification of impacted teeth in the maxilla is critical for effective dental treatment planning. Traditional diagnostic methods relying on manual interpretation of radiographic images are often time-consuming and subject to variability. Methods: This study presents a deep learning-based approach for automated classification of impacted maxillary canines using panoramic radiographs. A comparative evaluation of four pre-trained convolutional neural network (CNN) architectures—ResNet50, Xception, InceptionV3, and VGG16—was conducted through transfer learning techniques. In this retrospective single-center study, the dataset comprised 694 annotated panoramic radiographs sourced from the archives of a university dental hospital, with a mildly imbalanced representation of impacted and non-impacted cases. Models were assessed using accuracy, precision, recall, specificity, and F1-score. Results: Among the tested architectures, VGG16 demonstrated superior performance, achieving an accuracy of 99.28% and an F1-score of 99.43%. Additionally, a prototype diagnostic interface was developed to demonstrate the potential for clinical application. Conclusions: The findings underscore the potential of deep learning models, particularly VGG16, in enhancing diagnostic workflows; however, further validation on diverse, multi-center datasets is required to confirm clinical generalizability. Full article

(This article belongs to the Section Machine Learning and Artificial Intelligence in Diagnostics)

17 pages, 3765 KB

Open AccessCase Report

Paratubal Leiomyoma Mimicking Ovarian Malignancy: A Case Report and Literature Review

by Wen-Lin Hsieh and Dah-Ching Ding

Diagnostics 2026, 16(2), 218; https://doi.org/10.3390/diagnostics16020218 - 9 Jan 2026

Background and Clinical Significance: A paratubal leiomyoma is an exceptionally rare benign smooth muscle tumor arising from paratubal tissue, with only sporadic cases reported in the literature. Case Presentation: We present the case of a 72-year-old postmenopausal woman with intermittent spotting [...] Read more.

Background and Clinical Significance: A paratubal leiomyoma is an exceptionally rare benign smooth muscle tumor arising from paratubal tissue, with only sporadic cases reported in the literature. Case Presentation: We present the case of a 72-year-old postmenopausal woman with intermittent spotting for three months. A pelvic examination revealed a retained intrauterine device, which was removed along with an old sanguineous discharge. A transvaginal ultrasound demonstrated a complex left adnexal mass with calcifications, and computed tomography (CT) confirmed a 7.8 × 5.5 × 4.7 cm lesion suggestive of an ovarian malignancy. Tumor markers showed mildly elevated CA-125 and carcinoembryonic antigen (CEA) levels. Endometrial sampling using a hysteroscopy and curettage revealed hyperplasia without atypia. The patient underwent a total laparoscopic hysterectomy with a bilateral salpingo-oophorectomy. A diagnostic laparoscopy revealed a well-circumscribed solid mass arising from the mesosalpinx, separate from the ovary and fallopian tube and consistent with a paratubal mass, which was successfully excised laparoscopically. Frozen sections suggested a fibroma, and the final pathology confirmed a paratubal leiomyoma with hyalinization, accompanied by adenomyosis and simple endometrial hyperplasia. The patient recovered uneventfully, and the 6-month follow-up showed no recurrence. This case highlights the diagnostic challenge of differentiating paratubal leiomyomas from ovarian tumors based on imaging alone. Histopathological examination is essential for confirmation. Conclusions: Awareness of paratubal leiomyomas as a differential diagnosis may prevent overtreatment and guide the appropriate surgical management of postmenopausal women presenting with adnexal masses. Full article

(This article belongs to the Special Issue Precision Diagnostics in Gynecologic Health and Disease)

18 pages, 595 KB

Open AccessSystematic Review

Vibration Perception Threshold as a Method for Detecting Diabetic Peripheral Neuropathy: A Systematic Review of Measurement Characteristics

by Danijela Ribič and Nejc Šarabon

Diagnostics 2026, 16(2), 217; https://doi.org/10.3390/diagnostics16020217 - 9 Jan 2026

Background: Diabetic peripheral neuropathy (DPN) is one of the most common complications of diabetes mellitus (DM), leading to sensory loss, balance disturbances, and an increased risk of ulcers and amputations. Early screening is crucial, and devices for measuring vibration perception threshold (VPT) play [...] Read more.

Background: Diabetic peripheral neuropathy (DPN) is one of the most common complications of diabetes mellitus (DM), leading to sensory loss, balance disturbances, and an increased risk of ulcers and amputations. Early screening is crucial, and devices for measuring vibration perception threshold (VPT) play an important role in the timely detection and management of this condition. Objective: The aim of this systematic review was to evaluate the diagnostic accuracy and reliability of VPT measurement devices in individuals with DM. Methods: A systematic search was conducted in four databases, including studies that assessed the diagnostic accuracy and reliability of VPT measurement devices in patients with type 1 or type 2 DM, with VPT compared against reference standards for DPN, including nerve conduction studies (NCS) and clinical diagnosis. Cross-sectional and case–control studies were included. Risk of bias was assessed using the Quality Appraisal of Reliability (QAREL) tool and the JBI Critical Appraisal Checklist for Diagnostic Test Accuracy Studies. Results: Eighteen studies were analyzed. Most studies demonstrated moderate sensitivity and specificity and an acceptable level of reliability, with results varying according to technical and methodological factors. Conclusions: VPT measurement devices appear to be useful screening tools for detecting DPN; however, their diagnostic accuracy and reliability are not uniform and largely depend on technical and methodological factors. Standardized threshold values and measurement procedures, along with further research comparing the effectiveness of different protocols, are needed to improve clinical utility. Full article

(This article belongs to the Special Issue Advances in Modern Diabetes Diagnosis and Treatment Technology)

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9 pages, 357 KB

Open AccessArticle

Clinicopathologic Features and Postoperative Outcomes of Parotidectomy: A 16-Year Retrospective Cohort Study from a Tertiary Referral Center

by Seval Akay, Ozlem Yagiz Agayarov, Volkan Semiz, Ulku Kucuk, Ilker Burak Arslan, Olcun Umit Unal and Ibrahim Cukurova

Diagnostics 2026, 16(2), 216; https://doi.org/10.3390/diagnostics16020216 - 9 Jan 2026

Background: Parotid gland tumors pose diagnostic and surgical challenges due to their histological heterogeneity and proximity to the facial nerve. This study aimed to evaluate clinicopathological features and postoperative outcomes with a specific focus on facial nerve function in patients undergoing parotidectomy. [...] Read more.

Background: Parotid gland tumors pose diagnostic and surgical challenges due to their histological heterogeneity and proximity to the facial nerve. This study aimed to evaluate clinicopathological features and postoperative outcomes with a specific focus on facial nerve function in patients undergoing parotidectomy. Methods: This retrospective study included 314 patients who underwent parotidectomy between 2008 and 2024 at a tertiary center. Demographic data, tumor histology, and postoperative complications—particularly facial nerve paralysis within the first three months—were analyzed. Histopathological features including capsular, perineural, and lymphovascular invasion were also assessed. Results: Of all cases, 79% were benign, 14.6% malignant, and 6.4% non-neoplastic. Pleomorphic adenoma and Warthin tumor were the most common benign entities, while mucoepidermoid carcinoma was the most frequent malignancy. Malignant tumors were associated with higher rates of positive surgical margins (44.2% vs. 12.5%, p < 0.001), capsular invasion (25% vs. 7%, p < 0.001), and tumor necrosis (22% vs. <1%, p < 0.001). Facial paralysis occurred in 4.4% of patients, largely transient and significantly associated with malignant tumors (p < 0.001) and extensive lymph node dissection (p < 0.001). Capsular invasion and necrosis were rare in benign lesions but still observed, especially in pleomorphic adenoma. Conclusions: Histopathologic aggressiveness markers were associated with malignant disease and postoperative facial nerve dysfunction. These findings support a risk-stratified approach to follow-up: all patients undergo universal early assessment at two weeks and three months, after which surveillance intensity may be individualized according to histopathologic features—such as necrosis, perineural invasion, capsular invasion, or positive margins. Full article

(This article belongs to the Section Pathology and Molecular Diagnostics)

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18 pages, 2654 KB

Open AccessArticle

Automated Tumor and Node Staging from Esophageal Cancer Endoscopic Ultrasound Reports: A Benchmark of Advanced Reasoning Models with Prompt Engineering and Cross-Lingual Evaluation

by Xudong Hu, Lingde Feng, Bingzhong Jing, Linna Luo, Wencheng Tan, Yin Li, Xinyi Zheng, Xinxin Huang, Shiyong Lin, Huiling Wu and Longjun He

Diagnostics 2026, 16(2), 215; https://doi.org/10.3390/diagnostics16020215 - 9 Jan 2026

Objectives: To benchmark the performance of DeepSeek-R1 against three other advanced AI reasoning models (GPT-4o, Qwen3, Grok-3) in automatically extracting T/N staging from esophageal cancer endoscopic ultrasound (EUS) complex medical reports, and to evaluate the impact of language (Chinese/English) and prompting strategy (with/without [...] Read more.

Objectives: To benchmark the performance of DeepSeek-R1 against three other advanced AI reasoning models (GPT-4o, Qwen3, Grok-3) in automatically extracting T/N staging from esophageal cancer endoscopic ultrasound (EUS) complex medical reports, and to evaluate the impact of language (Chinese/English) and prompting strategy (with/without designed prompt) on model accuracy and robustness. Methods: We retrospectively analyzed 625 EUS reports for T-staging and 579 for N-staging, which were collected from 663 patients at the Sun Yat-sen University Cancer Center between 2018 and 2020. A 2 × 2 factorial design (Language × Prompt) was employed under a zero-shot setting. The performance of the models was evaluated using accuracy, and the odds ratio (OR) was calculated to quantify the comparative performance advantage between models across different scenarios. Results: Performance was evaluated across four scenarios: (1) Chinese with-prompt, (2) Chinese without-prompt, (3) English with-prompt, and (4) English without-prompt. In both T and N-staging tasks, DeepSeek-R1 demonstrated superior overall performance compared to the competitors. For T-staging, the average accuracy was (DeepSeek-R1 vs. GPT-4o vs. Qwen3 vs. Grok-3: 91.4% vs. 84.2% vs. 89.5% vs. 81.3%). For N-staging, the respective average accuracy was 84.2% vs. 65.0% vs. 68.4% vs. 51.9%. Notably, N-staging proved more challenging than T-staging for all models, as indicated by lower accuracy. This superiority was most pronounced in the Chinese without-prompt T-staging scenario, where DeepSeek-R1 achieved significantly higher accuracy than GPT-4o (OR = 7.84, 95% CI [4.62–13.30], p < 0.001), Qwen3 (OR = 5.00, 95% CI [2.85–8.79], p < 0.001), and Grok-3 (OR = 6.47, 95% CI [4.30–9.74], p < 0.001). Conclusions: This study validates the feasibility and effectiveness of large language models (LLMs) for automated T/N staging from EUS reports. Our findings confirm that DeepSeek-R1 possesses strong intrinsic reasoning capabilities, achieving the most robust performance across diverse conditions, with the most pronounced advantage observed in the challenging English without-prompt N-staging task. By establishing a standardized, objective benchmark, DeepSeek-R1 mitigates inter-observer variability, and its deployment provides a reliable foundation for guiding precise, individualized treatment planning for esophageal cancer patients. Full article

(This article belongs to the Special Issue AI-Enhanced Medical Imaging: A New Era in Oncology)

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36 pages, 5330 KB

Open AccessReview

Doppler Assessment of the Fetal Brain Circulation

by Maria Isabel Sá, Miriam Illa and Luís Guedes-Martins

Diagnostics 2026, 16(2), 214; https://doi.org/10.3390/diagnostics16020214 - 9 Jan 2026

Doppler assessment of fetal cerebral circulation has become a cornerstone of modern fetal medicine. It is used to evaluate cerebral vascular malformations, brain anomalies, fetal growth restriction due to placental insufficiency, fetal anemia, and hemodynamic complications arising from placental vascular anastomoses in monochorionic [...] Read more.

Doppler assessment of fetal cerebral circulation has become a cornerstone of modern fetal medicine. It is used to evaluate cerebral vascular malformations, brain anomalies, fetal growth restriction due to placental insufficiency, fetal anemia, and hemodynamic complications arising from placental vascular anastomoses in monochorionic pregnancies. Emerging research also explores the predictive value of Doppler parameters for perinatal outcomes and long-term neurodevelopment. To review the anatomy and physiology of fetal cerebral vessels accessible to Doppler evaluation, outline key technical aspects, and summarize current obstetric applications. A PubMed search identified 113 relevant publications, published between 1984 and 2025. Three book chapters by authors recognized internationally within the scientific community were included. A total of 116 publications were critically analyzed in this narrative review. Strong evidence supports the use of Doppler ultrasound in obstetrics, particularly for evaluating fetal cerebral hemodynamics, where it contributes to reducing fetal morbidity and mortality. Doppler assessment of fetal brain circulation is a valuable tool for evaluating brain vascular malformations, other structural abnormalities, and for assessing fetuses with growth restriction, anemia, and twin-to-twin transfusion syndrome. It allows targeted fetal monitoring and timely interventions, providing critical prognostic information and aiding parental counseling. Ongoing advances in Doppler technology and understanding of fetal brain physiology are likely to broaden its clinical uses, improving both perinatal outcomes and long-term neurological health. Full article

(This article belongs to the Special Issue Advances in Fetal Diagnosis and Therapy)

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17 pages, 1725 KB

Open AccessArticle

Development of an Intelligent Clinical Decision Support System for Spirometry Quality Control

by Julia López-Canay, Ana Priegue-Carrera, Alejandro Casado-Trigo, Cristina Represas-Represas, Alberto Fernández-García, Alberto Comesaña-Campos, Manuel Casal-Guisande and Alberto Fernández-Villar

Diagnostics 2026, 16(2), 213; https://doi.org/10.3390/diagnostics16020213 - 9 Jan 2026

Background: Spirometry is the most widely used pulmonary function test for diagnosing respiratory diseases. However, the quality of the results mainly depends on the correct execution of the maneuver, making quality control essential. Traditionally, this process relies on subjective and laborious visual [...] Read more.

Background: Spirometry is the most widely used pulmonary function test for diagnosing respiratory diseases. However, the quality of the results mainly depends on the correct execution of the maneuver, making quality control essential. Traditionally, this process relies on subjective and laborious visual inspection. Methods: To overcome these limitations, this work proposes an intelligent clinical decision support system to assist in spirometry quality control. The proposed system generates a graphical construct that integrates the spirometry curves (flow-volume and volume-time curves) along with patient demographic information (sex, age, and BMI) extracted from the spirometry report. The resulting image is processed by a convolutional neural network based on the ResNet-18 architecture, whose output quantifies the risk of the performed test being unacceptable. This approach allows for simple integration of the system into clinical practice while accounting for individual patient characteristics during classification. Results: The results obtained in the test set are promising, with an AUC of 0.94 (95% CI: 0.80–1.00) and a sensitivity and specificity at the selected cut-off point of 75.00% (95% CI: 40–100%) and 100.00% (95% CI: 100–100%), respectively. Conclusions: Despite this, it should be noted that the system is still in a conceptual phase of development and therefore requires broader validation in real clinical environments as well as the incorporation of more diverse datasets to evaluate its robustness and generalization before its final implementation. Full article

(This article belongs to the Section Machine Learning and Artificial Intelligence in Diagnostics)

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33 pages, 24811 KB

Open AccessArticle

Demystifying Deep Learning Decisions in Leukemia Diagnostics Using Explainable AI

by Shahd H. Altalhi and Salha M. Alzahrani

Diagnostics 2026, 16(2), 212; https://doi.org/10.3390/diagnostics16020212 - 9 Jan 2026

Background/Objectives: Conventional workflows, peripheral blood smears, and bone marrow assessment supplemented by LDI-PCR, molecular cytogenetics, and array-CGH, are expert-driven in the face of biological and imaging variability. Methods: We propose an AI pipeline that integrates convolutional neural networks (CNNs) and transfer [...] Read more.

Background/Objectives: Conventional workflows, peripheral blood smears, and bone marrow assessment supplemented by LDI-PCR, molecular cytogenetics, and array-CGH, are expert-driven in the face of biological and imaging variability. Methods: We propose an AI pipeline that integrates convolutional neural networks (CNNs) and transfer learning-based models with two explainable AI (XAI) approaches, LIME and Grad-Cam, to deliver both high diagnostic accuracy and transparent rationale. Seven public sources were curated into a unified benchmark (66,550 images) covering ALL, AML, CLL, CML, and healthy controls; images were standardized, ROI-cropped, and split with stratification (80/10/10). We fine-tuned multiple backbones (DenseNet-121, MobileNetV2, VGG16, InceptionV3, ResNet50, Xception, and a custom CNN) and evaluated the accuracy and F1-score, benchmarking against the recent literature. Results: On the five-class task (ALL/AML/CLL/CML/Healthy), MobileNetV2 achieved 97.9% accuracy/F1, with DenseNet-121 reaching 97.66% F1. On ALL subtypes (Benign, Early, Pre, Pro) and across tasks, DenseNet121 and MobileNetV2 were the most reliable, achieving state-of-the-art accuracy with the strongest, nucleus-centric explanations. Conclusions: XAI analyses (LIME, Grad-CAM) consistently localized leukemic nuclei and other cell-intrinsic morphology, aligning saliency with clinical cues and model performance. Compared with baselines, our approach matched or exceeded accuracy while providing stronger, corroborated interpretability on a substantially larger and more diverse dataset. Full article

(This article belongs to the Special Issue Recent Advances in AI and Hyperspectral Techniques for Medical Imaging)

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40 pages, 12777 KB

Open AccessSystematic Review

A Systematic Review of Diffusion Models for Medical Image-Based Diagnosis: Methods, Taxonomies, Clinical Integration, Explainability, and Future Directions

by Mohammad Azad, Nur Mohammad Fahad, Mohaimenul Azam Khan Raiaan, Tanvir Rahman Anik, Md Faraz Kabir Khan, Habib Mahamadou Kélé Toyé and Ghulam Muhammad

Diagnostics 2026, 16(2), 211; https://doi.org/10.3390/diagnostics16020211 - 9 Jan 2026

Background and Objectives: Diffusion models, as a recent advancement in generative modeling, have become central to high-resolution image synthesis and reconstruction. Their rapid progress has notably shaped computer vision and health informatics, particularly by enhancing medical imaging and diagnostic workflows. However, despite these [...] Read more.

Background and Objectives: Diffusion models, as a recent advancement in generative modeling, have become central to high-resolution image synthesis and reconstruction. Their rapid progress has notably shaped computer vision and health informatics, particularly by enhancing medical imaging and diagnostic workflows. However, despite these developments, researchers continue to face challenges due to the absence of a structured and comprehensive discussion on the use of diffusion models within clinical imaging. Methods: This systematic review investigates the application of diffusion models in medical imaging for diagnostic purposes. It provides an integrated overview of their underlying principles, major application areas, and existing research limitations. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines and included peer-reviewed studies published between 2013 and 2024. Studies were eligible if they employed diffusion models for diagnostic tasks in medical imaging; non-medical studies and those not involving diffusion-based methods were excluded. Searches were conducted across major scientific databases prior to the review. Risk of bias was assessed based on methodological rigor and reporting quality. Given the heterogeneity of study designs, a narrative synthesis approach was used. Results: A total of 68 studies met the inclusion criteria, spanning multiple imaging modalities and falling into eight major application categories: anomaly detection, classification, denoising, generation, reconstruction, segmentation, super-resolution, and image-to-image translation. Explainable AI components were present in 22.06% of the studies, clinician engagement in 57.35%, and real-time implementation in 10.30%. Overall, the findings highlight the strong diagnostic potential of diffusion models but also emphasize the variability in reporting standards, methodological inconsistencies, and the limited validation in real-world clinical settings. Conclusions: Diffusion models offer significant promise for diagnostic imaging, yet their reliable clinical deployment requires advances in explainability, clinician integration, and real-time performance. This review identifies twelve key research directions that can guide future developments and support the translation of diffusion-based approaches into routine medical practice. Full article

(This article belongs to the Collection Reviews on Artificial Intelligence and Natural Language Processing in Medical Diagnostics)

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13 pages, 2195 KB

Open AccessCase Report

First Whole-Genome Sequencing Analysis of Tracheobronchopathia Osteochondroplastica with Critical Vocal Cord Involvement: Proposing a Novel Pathophysiological Model

by Yeonhee Park, Joo-Eun Lee, Mi Jung Lim, Hyeong Seok Kang and Chaeuk Chung

Diagnostics 2026, 16(2), 210; https://doi.org/10.3390/diagnostics16020210 - 9 Jan 2026

Background: Tracheobronchopathia osteochondroplastica (TO) is a rare benign disorder characterized by submucosal cartilaginous and osseous nodules of the tracheobronchial tree, typically sparing the posterior membranous wall. Involvement of the vocal cords is exceedingly rare and may result in critical airway obstruction. The [...] Read more.

Background: Tracheobronchopathia osteochondroplastica (TO) is a rare benign disorder characterized by submucosal cartilaginous and osseous nodules of the tracheobronchial tree, typically sparing the posterior membranous wall. Involvement of the vocal cords is exceedingly rare and may result in critical airway obstruction. The underlying genetic and molecular mechanisms of TO remain largely unexplored. Case presentation: We report a rare case of TO extending from the vocal cords to the bronchi in a 76-year-old man who initially presented with pneumonia and later developed acute respiratory failure due to severe airway narrowing, necessitating emergency tracheostomy. Bronchoscopy and computed tomography revealed diffuse calcified nodules involving the anterior and lateral airway walls, including the subglottic region. Histopathology demonstrated chronic inflammatory cell infiltration with squamous metaplasia. To explore the molecular basis of this condition, whole-genome sequencing (WGS) was performed using peripheral blood samples—the first such application in TO. WGS identified 766 germline mutations (including 27 high-impact variants) and 66 structural variations. Candidate genes were implicated in coagulation and inflammation (KNG1), arachidonic acid metabolism and extracellular matrix remodeling (PLA2G4D), ciliary dysfunction and mineralization (TMEM67), vascular calcification (CDKN2B-AS1), smooth muscle function (MYLK4), abnormal calcification (TRPV2, SPRY2, BAZ1B), fibrotic signaling (AHNAK2), and mucosal barrier integrity (MUC12/MUC19). Notably, despite systemic germline mutations, calcification was restricted to the airway. Conclusions: This case highlights that TO with vocal cord involvement can progress beyond a benign course to cause life-threatening airway obstruction. Integrating clinical, histological, and genomic findings, we propose a novel pathophysiological model in which systemic genetic susceptibility interacts with local immune cell infiltration and fibroblast-driven extracellular matrix remodeling, resulting in airway-restricted dystrophic calcification. This first genomic characterization of TO provides new insights into its pathogenesis and suggests that multi-omics approaches may enable future precision medicine strategies for this rare airway disease. Full article

(This article belongs to the Special Issue Respiratory Diseases: Diagnosis and Management)

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2 pages, 140 KB

Open AccessReply

Reply to Ismayilli et al. Comment on “Megat Ramli et al. A Systematic Review: The Role of Artificial Intelligence in Lung Cancer Screening in Detecting Lung Nodules on Chest X-Rays. Diagnostics 2025, 15, 246”

by Puteri Norliza Megat Ramli and Azimatun Noor Aizuddin

Diagnostics 2026, 16(2), 209; https://doi.org/10.3390/diagnostics16020209 - 9 Jan 2026

We would like to sincerely thank you for your thoughtful and very helpful comments on our systematic review titled “A Systematic Review: The Role of Artificial Intelligence in Lung Cancer Screening in Detecting Lung Nodules on Chest X-Rays” [...] Full article

(This article belongs to the Section Machine Learning and Artificial Intelligence in Diagnostics)

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