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PubMed2026

The voice clone intelligibility benefit in noise in middle-aged listeners.

Research with younger adults showed that cloned voices are more intelligible than human voices in noise, with a benefit of 13.4%. This study tested whether this benefit extends to 40 middle-aged listeners (45-65 years), as this population may show emerging difficulties with speech-in-noise. Participants recognised sentences by ten human voices and ten voice clones in four noise levels. Cloned voices were 11.8% more intelligible, with benefits enhanced at the two most severe noise levels (15.9% at -6 dB and 17.5% at -3 dB), suggesting cloned speech enhanced perception in middle-aged listeners, potentially by reducing listening effort and compensating for emerging age-related auditory-cognitive decline.

Humans↗
PubMed2026

Comparing Traditional Motor Speech Practice to Contextualized Speech Practice in Preschoolers With Childhood Apraxia of Speech.

PURPOSE: The aim of this study was to compare retention of real-word targets across practice conditions (contextualized vs. motor-only) within a modified integral stimulation treatment for preschoolers with childhood apraxia of speech (CAS). METHOD: A single-subject experimental design with alternating treatments was used with matched target sets randomly assigned to contextualized practice, motor-only practice, or no treatment. Three preschoolers with CAS completed 18 therapy sessions, each consisting of two 25-min blocks: one contextualized practice and one motor-only practice. Order of practice was randomized each visit. Changes in percent phonemes correct (PPC) and lexical stress accuracy, derived from blinded transcription, were explored with visual analysis and effect sizes (standardized mean difference, d statistic). RESULTS: Meaningful improvements (d > 1) were observed in PPC across words treated in contextualized practice for all three children immediately posttreatment and for two of three children at the 1-month follow-up. Meaningful improvements in the motor-only condition were observed in two of three children immediately posttreatment and at follow-up. No meaningful changes were observed in lexical stress across any conditions in any participant. CONCLUSIONS: This study provides preliminary support for the feasibility of a modified integral stimulation therapy that incorporates elements of linguistically grounded therapies (linguistic retrieval, recasts, expansions) that may facilitate target retention in some preschoolers with CAS. However, other elements should be explored in conjunction with integral stimulation to maximize clinical outcomes. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.33228981.

Humans↗
PubMed2026

Novel Proactive Speech-Language Intervention Is More Effective Than Usual Care: Randomized Controlled Trial of Babble Boot Camp for Infants With Classic Galactosemia.

PURPOSE: Speech and language disorders cannot be diagnosed and treated until children are approximately 2-4 years old. To investigate whether these disorders can be prevented, we developed and trialed Babble Boot Camp (BBC), the first proactive sustained intervention starting with precursor skills including cooing and babbling. METHOD: Participants were two randomly assigned groups of 22 infants with classic galactosemia, a metabolic disease with known risks for severe speech and language disorders. One group started BBC at under 6 months of age, and the other started at 15 months of age, both completing BBC at 24 months of age. Coached by a speech-language pathologist in weekly telehealth sessions, caregivers implemented BBC activities and routines daily at home. A typical control group and a group of children with classic galactosemia who received usual care participated as well. All children completed standardized assessments of speech and language at postintervention. RESULTS: Assessment scores showed that BBC was more effective than usual care for both intervention groups. Greatest benefits were seen in the group that started at or before 6 months of age, with a proportion of clinically concerning scores equal to that in the typically developing peers. No effects of sex, genotype, or milk consumption were evident in the outcomes. CONCLUSIONS: Findings motivate a paradigm shift from deficit-based to proactive approaches for infants with classic galactosemia. BBC is extensible to many other disorders, with trials currently underway for infants with Down syndrome and infants born preterm.

Humans↗
PubMed2026

Evaluating Patient Satisfaction and Oral Health Impact Profile-14 (OHIP-14): A Multicenter Crossover Study Comparing Selective Pressure Impression Conventional Dentures with Mucostatic Digital Dentures.

PURPOSE: To compare patient satisfaction and oral health impact between individuals receiving complete dentures made by digital methods and those using conventional techniques. MATERIALS AND METHODS: In this randomized crossover clinical trial, 23 patients aged 40 years and older with completely edentulous arches were enrolled at three treatment centers. Each participant received two sets of complete dentures: one set created using conventional methods (selective pressure impression) and the other through digital techniques (mucostatic digital impression). The order of denture placement was randomized, with each set used for 4 weeks. A trained specialist administered treatments alongside research tools, including a general information questionnaire, a denture satisfaction survey, and the OHIP-14 interview tool. Statistical analysis was conducted using Mann-Whitney U test. RESULTS: Participants with digital dentures reported significantly higher satisfaction regarding treatment duration, comfort, confidence, chewing ability, esthetics, and overall satisfaction compared to those with conventional dentures. There were no significant differences in satisfaction concerning speech and pronunciation. Overall, the oral health impact on quality of life was similar between denture types, but participants indicated improved quality of life while using dentures compared to being edentulous. CONCLUSIONS: Patients with digital dentures exhibited greater satisfaction across various domains compared to those with conventional dentures, despite similar satisfaction levels in speech and pronunciation. The impact on quality of life was comparable between both types, as measured by the OHIP-14.

Humans↗
PubMed2026

Modulating sentence comprehension in people with aphasia through anodal tDCS: A double-blind randomized cross-over study.

This double-blind randomized cross-over study investigated the effects of perilesional anodal transcranial direct current stimulation (AtDCS) combined with speech-language therapy on sentence comprehension in eight individuals with chronic nonfluent agrammatic aphasia. The behavioral therapy consisted of an intensive comprehension treatment including drilling in sentence-to-picture matching and Mapping Therapy. Each participant underwent both the anodal tDCS and sham stimulation conditions (five received sham first followed by real stimulation, and the remaining three the reverse sequence), with each condition paired with the same behavioral treatment and separated by a four-month washout period. Stimulation was applied over the perilesional area (left BA6) for 20 min during daily 40-min therapy sessions over four consecutive weeks. Sentence comprehension was assessed with the RiComprendo battery and functional communication with the Communicative Effectiveness Index (CETI). Data were analyzed using paired t-tests, Bayesian analyses, and linear mixed-effects models to control for baseline performance and individual variability. Both stimulation conditions produced significant pre-to-post improvements in sentence comprehension, particularly for syntactically complex structures such as passives and center-embedded object relatives. However, gains were overall greater following AtDCS, as reflected in larger effect sizes, stronger Bayes factors, and a significant treatment effect in the mixed-effects models. Only the AtDCS condition yielded significant improvements in self-perceived comprehension abilities on the CETI. These findings suggest that AtDCS over perilesional cortical areas may boost the effects of traditional language therapy on sentence comprehension, supporting its feasibility and potential as an adjuvant intervention in post-stroke aphasia rehabilitation.

Humans↗
PubMed2026

Characterizing Caregiver-Child Interactions Through a Transactional Lens: A Baseline Analysis of a Caregiver-Implemented Intervention.

PURPOSE: This study was motivated by the transactional model of development and examined the reciprocal influences that children and caregivers have on caregiver-child interactions (CCXs) prior to a caregiver-implemented intervention. We tested whether child communication characteristics were associated with caregiver strategy use and whether these strategies, in turn, influenced children's communication to understand how caregivers and children mutually shaped the language learning environment. METHOD: Caregiver-child dyads (N = 105) were participants in a randomized controlled trial. CCXs were collected when children were approximately 30 months of age, transcribed, and coded for four caregiver language facilitation strategies and child communication variables. RESULTS: Least Absolute Shrinkage and Selection Operator regression and postselection inference indicated that child communication characteristics in CCXs were associated with both the frequency and type of strategies caregivers used. Children's overall communication acts were significantly associated with caregiver use of vocabulary strategies, whereas children's vocabulary diversity was significantly associated with caregiver use of sentence strategies. Mixed-effects logistic regression demonstrated that all four caregiver strategies significantly increased the likelihood of spontaneous lexical overlap in subsequent child turns. CONCLUSIONS: Prior to the intervention, caregivers and children reciprocally shaped the language environment. This supports a transactional perspective and warrants further consideration of reciprocal influences when assessing the impact of caregiver-implemented interventions. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.32995796.

Humans↗
PubMed2026

Functional Reading Activities to Motivate and Empower: Maintenance of Reading Outcomes for Young Adults With Intellectual and Developmental Disabilities Following a Randomized Controlled Trial.

PURPOSE: This study examined whether the effects of Functional Reading Activities to Motivate and Empower (FRAME), a functional, strategy-based reading comprehension intervention for young adults with intellectual and developmental disabilities (IDDs), were maintained 6 months following the completion of the intervention and explored participants' perceptions of the intervention's feasibility, relevance, and perceived impact. METHOD: Participants were 44 young adults with IDDs (ages 18-26 years) who participated in a previously reported randomized controlled trial (FRAME participants: n = 23; controls: n = 21). Trial outcomes were assessed via telepractice at pretest, posttest, and 6-month follow-up. Six-month maintenance analyses focused on outcomes that demonstrated significant posttest group differences: use of (a) reading comprehension strategies (proximal) and (b) reading comprehension questions (distal). Participant perceptions (social validity) were collected post-intervention from FRAME participants using a structured interview protocol with closed- and open-ended items. RESULTS: At the 6-month follow-up, FRAME participants demonstrated sustained but reduced improvements in strategy use relative to controls (p = .040). Between-groups differences were not maintained for reading comprehension questions (p = .091). Participants reported high acceptability and perceived relevance of FRAME, with qualitative themes reflecting perceived improvements in comprehension, self-improvement, and increased independence. CONCLUSION: Findings suggest that FRAME supports sustainable gains in reading comprehension strategy use and is perceived as meaningful and feasible for young adults with IDDs, although additional supports may be needed to promote sustained improvements in distal comprehension outcomes. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.33307218.

Humans↗
PubMed2026

Characterizing Submental Neuromuscular Activity of Swallowing Rehabilitation: An Electromyographic Evaluation of Rehabilitative Maneuvers in Healthy Adults.

PURPOSE: The effortful swallow (ES), the Mendelsohn maneuver (MM), and isometric tongue presses (TPs) are widely used swallowing maneuvers/exercises to improve elements of swallowing, such as muscle strength and biomechanics. However, the underlying neuromuscular mechanisms of these exercises remain unclear, potentially limiting our ability to specify treatment targets and improve treatment efficacy. This study aimed to compare submental neuromuscular activation patterns during the ES, MM, TPs, and typical swallows in healthy young and older adults. METHOD: As part of a larger randomized crossover validation study, 60 healthy adults (30 young and 30 older) completed typical swallows and three maneuvers using a wearable submental surface electromyographic (sEMG) system (i-Phagia). Outcome variables included (a) normalized mean sEMG amplitude and (b) time to peak sEMG amplitude. Linear mixed models were used to examine effects of task, age, and sex on both outcomes. RESULTS: Normalized mean amplitude was significantly different across tasks. Post hoc pairwise comparisons confirmed that all three maneuvers produced higher normalized mean sEMG amplitude than typical swallows, with the ES eliciting the highest amplitude across age groups. Time to peak amplitude differed significantly across tasks, with typical swallows requiring the shortest time to reach peak amplitude, followed by the ES, MM, and TP. CONCLUSIONS: The ES required the highest neuromuscular effort with the shortest time to reach peak amplitude, suggesting its potential for targeting submental muscle power. Typical swallows required the least neuromuscular effort with the shortest time to reach peak amplitude, suggesting their potential for training submental muscle speed. The MM and TP may also improve submental muscle strength; however, given their temporal requirements (longer durations), they may be more beneficial for targeting coordination and endurance, though more research in this area is warranted. These findings underscore the importance of task-specific neuromuscular profiling to inform mechanism-based swallowing rehabilitation. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.32948549.

Humans↗
PubMed2026

Beyond antigen matching: compatibility intelligence theory for transfusion as an emergent biological system.

BACKGROUND: Despite major advances in serologic testing, extended phenotyping, and blood group genomics, clinically similar transfusion exposures may result in markedly different immune and clinical outcomes. Existing compatibility strategies do not fully explain this biological variability. OBJECTIVES: To examine transfusion compatibility as an emergent donor-recipient biological state and propose a systems-level conceptual framework that integrates established biological determinants into a testable model for future precision transfusion medicine. METHODS: This narrative review critically synthesizes current evidence from blood group genomics, recipient immunobiology, inflammation, disease-specific biology, transfusion medicine, and computational prediction. The proposed framework distinguishes Compatibility Intelligence Theory (CIT) as a biological interpretation from Precision Transfusion Intelligence (PTI) as its potential clinician-supervised translational application. RESULTS: The review argues that transfusion compatibility is shaped by interactions among donor genetics, recipient immune biology, inflammatory physiology, disease context, transfusion history, and longitudinal adaptation rather than by antigen matching alone. CIT provides an organizational framework for integrating these determinants, whereas PTI describes a possible clinician-supervised translation. To address current feasibility, the revised framework separates variables into routinely measurable, contextually available but incompletely standardized, and research-stage domains, and proposes a staged strategy for deriving rather than assuming their quantitative weights. Any clinical implementation would require comparative validation against current serologic, phenotypic, and genotype-based practice. CONCLUSIONS: Compatibility Intelligence Theory offers a testable systems-level framework for understanding transfusion compatibility without replacing established transfusion practices. The framework is not presented as a ready-to-use score: currently measurable variables can be organized for structured risk review, whereas inflammatory, immunogenetic, and multi-omic inputs require prospective standardization and validation. If future studies demonstrate incremental predictive and patient-centered benefit, CIT-informed PTI could support an adaptive, evidence-based extension of current precision transfusion practice.

Humans↗
PubMed2026

Artificial Intelligence Technologies in Nursing Clinical Decision-Making: An Umbrella Review.

AIM: To describe contemporary peer-reviewed literature on artificial intelligence in nurses' clinical decision-making. METHODS: An umbrella review of literature reviews. DATA SOURCES: Four major databases were searched for reviews published between 2019 and 2024. RESULTS: Sixteen literature reviews reported on 965 nursing artificial intelligence primary studies. The studies focused on technology development and emerging performance evaluations, whilst real-world testing or implementation in nursing clinical settings was rare. Rigorous comparative analyses were lacking. While artificial intelligence demonstrates promise in decision-making, challenges such as a lack of controlled studies, algorithmic bias, limited reproducibility and insufficient clinical trials hinder its practical impact. Ethical concerns, transparency and patient data privacy issues pose barriers to AI integration in nursing practice. Ethical and legal guidelines for patient privacy are needed and should be taught along with AI literacy training for nurses. CONCLUSIONS: Artificial intelligence has the potential to enhance clinical nursing decision-making, although evidence is limited by too few examples of nurse participation during development. Underutilisation in administrative nursing functions hinders implementation. Nurses should assume a central role in the design and development of AI applications to ensure that these technologies address the realities of nursing practice. With such improvements, artificial intelligence can transform nursing practice, improve nurses' clinical decision-making and ultimately enhance consumer healthcare outcomes. PATIENT OR PUBLIC INVOLVEMENT: No Patient or Public Involvement. REPORTING METHOD: While there is no reporting checklist for umbrella reviews, the PRISMA guide for systematic reviews was followed.

Artificial Intelligence↗
PubMed2026

How AI-supported intelligent systems support infection prevention and control training in healthcare: A systematic review of educational functions and outcomes.

AIMS: Artificial intelligence (AI)-supported intelligent systems have been increasingly incorporated into infection prevention and control (IPC) education and training, primarily to support the monitoring of observable behaviors and the provision of feedback. However, existing evidence has focused largely on short-term compliance outcomes, with limited synthesis of the educational role of AI-supported intelligent systems in supporting sustained IPC competence. This systematic review examined how AI-supported intelligent systems have been designed and used to support IPC education and training, with a focus on system characteristics, educational functions, and reported outcomes. DESIGN: A systematic literature search was conducted across the PubMed/MEDLINE, Embase, Cochrane, and CINAHL databases. DATA SOURCES: A total of 18 studies met the inclusion criteria. Findings were qualitatively synthesized according to system design characteristics, educational functions, and outcome domains. REVIEW METHODS: Methodological quality was appraised using the Mixed Methods Appraisal Tool. RESULTS: Most AI-supported intelligent systems focused on hand hygiene and relied on fully automated monitoring systems to capture behaviors and provide performance feedback. Educational functions were predominantly limited to performance assessment, automated feedback, and reminders. Outcomes were mainly measured using compliance or performance metrics, whereas sustained behavioral change and decision quality were rarely assessed. CONCLUSIONS: AI-supported intelligent systems have been used primarily to reinforce short-term IPC performance and compliance. However, their current applications for supporting sustained competence over time remain limited. The findings of this review suggest that AI-supported intelligent systems may serve as maintenance-oriented educational support by extending learning beyond initial instruction through repeated practice and feedback. Future research should prioritize outcome measures that capture the durability of performance and decision-making processes to better align AI-supported intelligent systems used in IPC education and training with the educational demands of clinical practice.

Humans↗
PubMed2026

The application of artificial intelligence in healthcare practice: A mapping review of systematic reviews.

Artificial intelligence (AI) is rapidly transforming healthcare practice, with growing evidence supporting its use in diagnosis, prognosis, treatment planning, and operational decision-making. The proliferation of systematic reviews in recent years underscores the need for an updated synthesis of the literature to inform research, policy, and practice. We searched PubMed, Web of Science, Scopus, IEEE Xplore, and CINAHL for systematic reviews and meta-analyses published between 2019 and February 2026. Eligible reviews focused on AI applications in healthcare practice, were peer-reviewed, and written in English. A total of 368 reviews met the inclusion criteria. Publication volume increased steadily, peaking in 2025. AI research was concentrated in high-density domains, such as radiology, oncology, and critical care. Across reviews, diagnostic imaging, electronic health record (EHR) data, and biomarkers/laboratory results accounted for 68% of training data sources, though newer data types, such as wearable device and sensor data, emerged from 2022 onward. Diagnosis, prognosis, and treatment comprised over 80% of AI applications, with novel uses emerging in recent years, such as AI-assisted clinical documentation (e.g., ambient documentation tools) and patient education. Ethical concerns were reported in 78.5% of reviews, with privacy, model accuracy, data and algorithmic bias, and explainability as recurrent themes. The proportion of reviews reporting ethical concerns increased from 2021 to 2025. AI applications in healthcare are expanding in scope, diversifying in data sources, and evolving toward novel clinical and operational uses. The human-centered AI or augmented intelligence paradigm, integrating computational precision with clinical expertise, holds significant promise but will require parallel advances in governance, regulatory frameworks, and ethical oversight to ensure safe adoption.

Artificial Intelligence↗
PubMed2026

AI-driven snapshot hyperspectral imaging for on-line sorting systems in food industry: From real-time sensing to intelligent decision-making.

High-throughput food sorting requires rapid, non-destructive detection of external defects, foreign materials, and internal quality attributes in heterogeneous food matrices. Conventional scanning hyperspectral imaging may suffer from motion-induced spatial-spectral mismatches, whereas snapshot hyperspectral imaging (S-HSI) captures spectral images within a single integration time. However, its advantage is limited by trade-offs in resolution, signal-to-noise ratio (SNR), reconstruction uncertainty, and calibration stability, which are further amplified by variable tissue structure, surface reflection, moisture, and fat distribution in foods. This review critically examines artificial intelligence (AI)-driven S-HSI for on-line food sorting within a sensing-representation-decision-execution framework. Compact architectures are compared according to their physical constraints, food-sorting suitability, and ability to support mapping between spectral responses and physicochemical quality attributes. AI strategies are reviewed for spectral reconstruction, image restoration, spatial-spectral representation, band selection, uncertainty-aware decision-making, and edge implementation. AI can partially compensate for snapshot-specific limitations, but current evidence remains largely limited to laboratory or prototype studies. Future work should link system performance to food safety and quality outcomes by reporting throughput, decision latency, calibration drift, missed-detection risk, false-rejection cost, and closed-loop sorting success.

Hyperspectral Imaging↗
PubMed2026

Identifying and Prioritizing Core Components of Relationship Education Programs: a Case Study of an Artificial Intelligence (AI) Assisted Systematic Review.

The field of prevention science seeks to identify and implement effective strategies to address social, emotional, and health challenges. A critical aspect of this endeavor is determining the core components of prevention programs that drive positive outcomes. This article presents a case study utilizing artificial intelligence (AI)-assisted systematic review methods to identify key components of healthy marriage and relationship education programs. Given the growing body of research in this domain, AI tools offer a promising means to enhance the efficiency and accuracy of literature reviews. This study employed AI to screen, code, and validate research articles, demonstrating its effectiveness in expediting systematic reviews while maintaining high accuracy in inclusion screening. This case study involved a systematic review of 22,028 resources (identified from PsycINFO, Academic Search Ultimate, and Google) and a final data set of 268 relevant studies. AI screening was integral in effectively conducting multiple rounds of screening. However, findings also highlight challenges in AI-assisted qualitative data abstraction, underscoring the continued need for human expertise in complex coding tasks. The study contributes to the ongoing discourse on integrating AI into prevention science methodologies and offers insights for optimizing AI applications in systematic reviews.

Artificial Intelligence↗
PubMed2026

Artificial intelligence for dental caries detection: An umbrella review.

Artificial intelligence (AI) has been proposed as a tool to improve dental caries detection across imaging modalities; however, its clinical value remains uncertain. This umbrella review aimed to synthesize and critically appraise systematic reviews evaluating AI for caries detection and diagnosis. An umbrella review was conducted following PRIOR guidance (PROSPERO CRD420261340728). Searches were performed in MEDLINE, Embase, Scopus, Web of Science, and Google Scholar up to 15 March 2026. Methodological quality was assessed using AMSTAR 2, and overlap of primary studies was quantified using the corrected covered area (CCA). Seventeen systematic reviews were included, of which five reported diagnostic test accuracy meta-analyses using bivariate or HSROC models. Across these meta-analyses, pooled sensitivity ranged from 0.76 to 0.94 and specificity from 0.85 to 0.91. Most systems were based on deep learning models applied to bitewing radiographs and intraoral photographs. However, substantial heterogeneity was observed in imaging modalities, lesion thresholds, analytical tasks, and evaluation metrics. In addition, a high degree of overlap across reviews and recurrent methodological limitations, including reliance on retrospective datasets, limited external validation, and inconsistent reporting, substantially weaken the reliability of the evidence. Although AI models demonstrate high diagnostic performance under experimental conditions, current evidence does not support their use as stand-alone diagnostic tools. Their clinical applicability remains limited, and implementation should be restricted to decision-support contexts until robust prospective validation demonstrates meaningful impact on clinical decision-making and patient outcomes.

Dental Caries↗
PubMed2026

Efficacy of current approaches to non-invasive diagnosis of skin cancer and the potential impact of artificial intelligence: A systematic review and meta-analysis.

BACKGROUND: Skin cancer is one of the most prevalent malignancies worldwide, particularly within Caucasian populations. This systematic review and meta-analysis aimed to quantitatively review the current literature on non-invasive diagnosis of skin cancer and evaluate the current evidence to support the use of tools in addition to, or in replacement of clinician face-to-face assessment. METHODS: A literature search was conducted for publications in PubMed, Medline and Embase databases. Articles describing accuracy, sensitivity, specificity and outcomes of their mode of assessment were included. A total of 208 articles met the inclusion criteria. RESULTS AND CONCLUSION: This systematic review and meta-analysis showed that the diagnostic performance of artificial intelligence (AI) in the interpretation of dermatoscopic images was high for melanoma diagnosis, basal cell carcinoma or malignancy, in comparison to dermatoscopic assessment alone by clinicians and experts. Although AI interpretation of images demonstrated higher sensitivity for melanoma diagnosis in comparison to clinical assessment combined with dermatoscopic assessment, it is unclear if this is also the case for basal cell carcinoma and squamous cell carcinoma diagnosis. Reflectance confocal microscopy, a non-invasive high resolution imaging technique, is known to have a high sensitivity for diagnosing cutaneous malignancy, and this may have applications within secondary care. Therefore, AI could help reduce resource burden and aid in clinical assessment, particularly within primary care settings.

Humans↗
PubMed2026

A systematic approach to standardizing the visual appearance of endometriotic lesions for artificial intelligence recognition.

INTRODUCTION: Numerous studies have shown that the diagnostic performance and reproducibility of visual recognition of endometriosis during laparoscopy are poor. The use of artificial intelligence (AI) seems relevant for exhaustive lesion recognition. Standardization of the visual classification of lesions, in the form of an ontology, is an essential prerequisite to enable medical experts to annotate surgical data consistently and subsequently allow engineers to train and build an artificial intelligence tool for endometriosis recognition. MATERIAL AND METHODS: A systematic search was conducted in the MEDLINE (via PubMed), EMBASE, and the Cochrane Library databases up to May 2022, aiming to identify studies describing the laparoscopic visual appearance of superficial endometriosis, endometriomas, and deep infiltrating endometriosis. The accumulated data in the literature concerning the visual appearance of the different forms of endometriosis were used to create an ontology that could be used for artificial intelligence applications. RESULTS: Out of 932 articles screened, 35 studies were selected based on the inclusion criteria of human subjects with histologically confirmed endometriosis lesions visualized via laparoscopy. The selected studies were reviewed to develop a visual ontology of endometriosis lesions observed via laparoscopy. The lesions were categorized into 4 classes and further subdivided into 11 subclasses: superficial (black, red, white, or subtle), adhesions (dense or filmy), deep (obliteration, retraction, or deformation), and ovarian (endometrioma or chocolate fluid). The positive predictive value (PPV) varied across lesion types: black lesions (PPV 47%-97%), red lesions (PPV 33%-100%), white lesions (PPV 20%-81%), and ovarian endometriosis (PPV 42%-98%). Nonspecific lesions such as adhesions (PPV 16%-50%) and subtle superficial lesions (PPV 0%-67%) presented lower PPVs. Deep endometriosis lesions, often buried within organs, required indirect signs (obliteration, retraction, deformation) for identification. CONCLUSIONS: The visual ontology proposed in this systematic search could facilitate the detection and classification of endometriosis lesions using artificial intelligence. This study highlights the challenges of reaching a consensus on lesion recognition and classification in AI projects due to the diverse visual presentations of endometriosis.

Humans↗
PubMed2026

Artificial intelligence in treatment prediction for skeletal Class III malocclusion: A systematic review.

In skeletal Class III patients, treatment options range from orthodontics to orthognathic surgery. Choosing the optimal approach requires a comprehensive clinical evaluation, which may be supported by AI tools. The aim of this study was to assess the performance of AI models in predicting the need for orthognathic surgery and in identifying predictors influencing treatment decisions. A PRISMA-guided electronic database search (PubMed, Web of Science; 2009-2024; English/French) was performed to identify studies using machine learning (ML) or deep learning (DL) on cephalometric and clinical data. After screening and assessment for eligibility, 15 studies were critically appraised. Model performance was summarized using accuracy, sensitivity, specificity, and the area under the curve (AUC). ML algorithms (particularly Random Forest and XGBoost) and DL models (ResNet-based convolutional neural networks (CNNs)) achieved high accuracy for predicting surgical need. Frequently selected predictors included Wits appraisal, ANB angle, the maxillomandibular ratio (Mx/Md), overjet, and the divergence of the lower gonial angle. AI methods show promise for assisting treatment decisions in Class III malocclusion, with Random Forest and XGBoost performing well on tabular cephalometric data and CNNs on imaging. Larger, multicentre datasets and external validation are needed to improve reliability, address bias, and support clinical implementation.

Humans↗