The study, led by Urvashi, Rishu, and Vinay Kukreja from the Centre for Research Impact and Outcome at Chitkara University Institute of Engineering and Technology, follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, or PRISMA, guidelines to ensure transparency and reproducibility. The researchers began with an initial pool of 411 articles gathered from Google Scholar, IEEE Xplore, ScienceDirect, and Scopus using structured search strings. Through a rigorous multi-stage screening process that included duplicate removal, title and abstract screening, full-text evaluation, and formal quality assessment, the team narrowed the corpus to fifty studies for in-depth analysis. These selected works were then examined for methodological trends, reported recognition accuracies, dataset usage patterns, the pace of research growth, and validation practices.

The scientific premise underlying this field is not new. Psychologists have long observed that emotional states manifest in body movement. Landmark studies showed that people with depression exhibit slower, less expressive walking patterns, and that observers can correctly attribute emotions such as happiness, sadness, anger, and fear to strangers simply by watching them move, even when only point-light displays of the moving joints are visible. What has changed is the technological capability to detect these signals automatically. Advances in sensing technologies, from markerless depth cameras and motion capture systems to wearable inertial measurement units and even floor vibration sensors, combined with the maturation of deep learning, have transformed gait-based emotion recognition from a curiosity of behavioral science into a viable computational discipline.

The review’s quantitative findings reveal a field now dominated by neural approaches. Deep learning methods appeared in forty-seven percent of the analyzed studies, reflecting the field’s decisive shift away from handcrafted features toward representations learned directly from data. Hybrid models, which combine deep learning components with classical machine learning techniques or multiple network architectures, accounted for thirty-one percent of the articles. Purely classical machine learning approaches, once the backbone of the field, now appear in just twenty-two percent of studies. This distribution mirrors broader trends in computer vision and affective computing, where end-to-end learned pipelines have consistently outperformed manually engineered feature descriptors such as gait energy images, joint angle statistics, or discrete cosine transform coefficients.

Perhaps the most striking technical finding concerns which architectures perform best. Graph convolutional neural networks achieved the highest average recognition accuracy at 89.50 percent, followed closely by spatio-temporal graph convolutional networks at 88 percent. The success of these graph-based models is no accident. Human walking is inherently a structured, relational phenomenon: joints are connected by bones, and their trajectories evolve over time. Graph convolutional networks represent the human skeleton as a graph of nodes and edges, allowing the network to learn how local joint configurations and global body posture jointly encode emotion. Spatio-temporal variants extend this idea by adding temporal edges that connect the same joint across consecutive frames, enabling the model to capture both the static shape of the body and the dynamic flow of movement, precisely the two information channels that psychophysics experiments have identified as critical for human perception of emotion from gait.

The review also documents a clear hierarchy among benchmark datasets. The Emotion-Gait dataset dominated the literature, appearing in 24.10 percent of the analyzed studies. The Carnegie Mellon University Motion Capture Database, known as CMU-MoCap, and the Edinburgh Locomotion Mocap Dataset, often paired with a deception detection framework abbreviated as DFD, followed with 12.20 percent each. This concentration highlights both a strength and a vulnerability of the field. Standardized benchmarks enable fair comparison between methods, but heavy reliance on a small number of datasets, many of which contain acted or posed emotional walks rather than naturally occurring emotions, raises questions about how well laboratory performance will translate to real-world deployment. The authors note that validation practices across the field remain uneven, with considerable variation in cross-validation schemes, evaluation metrics, and participant demographics.

The methodological pipeline described across the reviewed studies typically unfolds in several stages. Data is first acquired through one of several modalities: optical motion capture systems providing three-dimensional joint coordinates, consumer depth sensors such as the Microsoft Kinect, wearable accelerometers and gyroscopes mounted on the body or embedded in smartphones and smartwatches, or plantar pressure and floor vibration platforms. Preprocessing then segments individual gait cycles and normalizes for walking speed and body proportions. Feature extraction follows, ranging from kinematic descriptors such as stride length, cadence, joint angles, and movement smoothness, to learned representations produced by convolutional or recurrent layers. Finally, classification maps these features onto emotional categories, most commonly discrete labels drawn from Ekman’s basic emotions or dimensional representations along axes of arousal, valence, and dominance.

Beyond the raw accuracy figures, the review situates gait-based emotion recognition within a wider multimodal ecosystem. Emotion is expressed through faces, voices, physiological signals, and body movement simultaneously, and the most robust systems increasingly fuse information across these channels. The reviewed literature includes hybrid frameworks that combine gait data with electroencephalography, speech, facial expressions, and contextual information. Multimodal fusion offers redundancy and resilience, allowing systems to maintain performance when any single channel is degraded. At the same time, the review points to persistent challenges: small and demographically narrow datasets, difficulty distinguishing felt emotions from merely expressed ones, sensitivity to clothing, viewing angle, and environmental conditions, and the computational demands of real-time processing on mobile and embedded platforms.

The application landscape emerging from the review is strikingly broad. In healthcare, gait emotion analysis shows promise for monitoring depression, anxiety, and mood disorders, offering an unobtrusive complement to subjective clinical assessments; several reviewed studies demonstrated that depression risk could be detected from gait data alone, and that smartwatch signals could support emotion recognition in Parkinson’s disease patients. In human-robot interaction, emotionally aware robots could navigate crowds more safely by anticipating the affective states of pedestrians, adjusting their behavior to the perceived mood of the people around them. Smart homes and assistive technologies could adapt lighting, music, or alerts to a resident’s emotional state inferred from their natural walking. Automotive applications include driver state monitoring through wearable sensors, while security and forensic domains have explored emotional gait cues for investigative purposes.

The review does not shy away from the ethical dimensions of this technology. The authors and the broader literature they survey acknowledge that continuous, passive emotion monitoring raises substantial privacy concerns. Unlike facial recognition, gait can be captured at a distance, without the subject’s awareness or cooperation, and even through clothing-invariant, privacy-preserving pipelines. Several researchers have accordingly explored architectures designed specifically to decouple identity information from emotional content, so that systems can recognize affect without uniquely identifying individuals. Regulatory frameworks for emotional surveillance remain immature, and the review implicitly underscores the need for careful governance as these systems approach commercial deployment, particularly in workplaces, public spaces, and healthcare settings where consent dynamics are asymmetric.

Looking forward, the authors identify several research frontiers. Larger, more diverse, and more ecologically valid datasets are urgently needed, ideally capturing spontaneous rather than performed emotion in real-world environments. Self-supervised and transfer learning approaches could reduce dependence on labeled data, and recent work on learning gait emotion representations from unlabeled skeleton sequences points in this direction. Transformer-based architectures, already displacing convolutional models in many vision tasks, are beginning to appear in gait emotion recognition and may push accuracies beyond the current ceiling near ninety percent. Explainability also demands attention: clinicians and end users will need to understand which kinematic cues drive a system’s judgment of sadness or anger before trusting it in consequential decisions.

For a field that began with psychologists noting that sad people walk differently, the trajectory is remarkable. What this systematic review makes clear is that the machine perception of emotion from movement has crossed a threshold of maturity. With graph-based deep architectures consistently achieving near-human recognition rates, standardized benchmarks in place, and application domains from mental health monitoring to socially aware robotics actively under development, gait-based emotion recognition is poised to move from the laboratory into everyday ambient intelligence. The challenges that remain, in data, validation, and ethics, are substantial, but the review provides the field with something it has lacked: a rigorous, transparent map of where it stands and where it must go next.

Breast cancer, notorious for its high mortality rate among women worldwide, presents unique challenges that vary widely by region. While Western countries often dominate the narrative in medical innovation, this review unequivocally demonstrates that Asian populations encounter distinct obstacles in mammographic diagnostics—rooted primarily in physiological differences such as higher breast density. These variations critically affect the performance of DL-based diagnostic systems which, until now, have been predominantly trained on datasets from Caucasian populations.

The authors undertook a rigorous systematic review following PRISMA guidelines, meticulously screening over a thousand scientific records from top-tier databases including Scopus and Web of Science. Spanning literature published between 2018 and 2025, the review narrowed down to 287 studies most relevant to deep learning applications in mammography. Their selection criteria underscore a growing trend: the surge in DL-based computer-aided diagnostic (CAD) systems that leverage convolutional neural networks and other neural architectures to enhance lesion classification, segmentation, and breast density assessment.

Among the key findings is the overwhelming emphasis on lesion classification, a cornerstone task wherein neural networks discern malignant from benign formations. However, a conspicuous scarcity of research addresses other vital components such as tumor detection, precise segmentation, and dynamic breast density quantification. These tasks are essential for improving diagnostic specificity and sensitivity but have been comparatively neglected in the literature.

Asian datasets, representing a demographic with notably denser breast tissue, emerge as a critical locus of study in this review. DL models trained primarily on Caucasian imagery falter when transferred to Asian populations, a phenomenon attributed to intrinsic anatomical and image-acquisition disparities. The compendium of Asian studies highlighted problems including limited availability of annotated datasets—a fundamental bottleneck for supervised learning—and insufficient representation of varied imaging modalities, which limits the robustness of predictive models.

The review also delves into the nuanced preprocessing techniques and augmentation strategies employed to overcome the inherent challenges associated with mammogram data. From noise reduction to contrast enhancement and advanced data augmentation—such as rotation, scaling, and synthetic image generation—researchers have applied diverse methodologies to bolster the generalizability of DL models. Yet, the authors emphasize that these efforts are often piecemeal and not standardized across studies, complicating cross-comparison and clinical translation.

One of the most revealing aspects of the review is the deployment of focus maps to visualize the geographical and topical distribution of DL research efforts. These visual tools starkly illustrate a global bias, with more than 80% of publicly available datasets and resulting studies centered on Caucasian populations. This imbalance not only limits the efficacy of DL models in multiethnic applications but may inadvertently exacerbate healthcare disparities, a concern of paramount importance given the global burden of breast cancer.

Moreover, the authors critically analyze the BI-RADS (Breast Imaging-Reporting and Data System) classification—a universally accepted radiological lexicon—and identify a significant gap in multi-class classification within deep learning studies. Most research simplifies the task to binary classification (benign vs. malignant), a reductionist approach that undermines the granularity needed for nuanced clinical decision-making and risk stratification.

The synthesis uncovers a pressing need for collaborative frameworks aiming at the curation of expansive, diverse mammography datasets encompassing various ethnic groups and geographic regions. Such initiatives would not only democratize access to high-quality data but also facilitate the development of deep learning models that are robust, adaptable, and clinically valid worldwide.

Importantly, the review calls for rigorous cross-populational validation pipelines to prevent the pitfalls of model overfitting and ensure that diagnostic algorithms maintain high sensitivity and specificity across heterogeneous cohorts. Clinical trials and prospective studies involving women from multiple demographic backgrounds must be mandated to verify the translational power of new CAD technologies.

At the core of these revelations lies a call to the global research community: inclusivity and diversity in training data are not merely ethical imperatives but scientific necessities. By embracing demographic heterogeneity, researchers can harness the full potential of deep learning to revolutionize breast cancer detection and screening effectiveness—saving countless lives.

This systematic review acts as both a reflection and a roadmap. It reflects the remarkable strides made in leveraging deep learning for breast cancer diagnostics and illuminates the persistent, subtle biases embedded within current methodologies. Simultaneously, it maps out clear directions for future inquiry—prioritizing ethnic diversity, promoting methodological standardization, and fostering international cooperation.

As breast cancer remains a paramount public health challenge, innovations in AI must be carefully tailored to accommodate anatomical and epidemiological variances that characterize disparate global populations. Only through such conscientious efforts can deep learning-powered mammography achieve its envisioned role: an equitable, precise, and life-saving diagnostic tool accessible to all women, regardless of their ethnicity or geographic location.

The integrative insights offered by this review underscore the multidimensional nature of deploying AI in medicine—an enterprise demanding more than technical sophistication, but also cultural sensitivity and commitment to fairness. In this light, it stands as a pivotal contribution to the evolving discourse on AI in healthcare, compelling researchers, clinicians, and policymakers to rethink, recalibrate, and renew their strategies for breast cancer detection.

Ultimately, the transformational potential of deep learning in mammography hinges on our ability to transcend data silos, confront systemic biases, and embrace diversity as a foundational principle. The future of breast cancer diagnostics depends not only on algorithmic innovation but on global inclusivity—making this comprehensive review both timely and indispensable.

Subject of Research: Deep learning techniques applied to mammography for breast cancer detection, focusing on global and Asian perspectives.

Article Title: A systematic literature review on mammography: deep learning techniques for breast cancer detection with global and Asian perspectives.

Article References: Amin, A., U, D.A., Koteshwara, P. et al. A systematic literature review on mammography: deep learning techniques for breast cancer detection with global and Asian perspectives. BMC Cancer 25, 1627 (2025). https://doi.org/10.1186/s12885-025-14876-5

Image Credits: Scienmag.com

DOI: https://doi.org/10.1186/s12885-025-14876-5

Head and neck cancers pose a unique clinical challenge due to their anatomical complexity and treatment modalities, including surgery, chemotherapy, and notably, radiotherapy. Radiation therapy, while a cornerstone in managing these malignancies, is notoriously linked with collateral damage to the thyroid gland, provoking a spectrum of thyroid dysfunctions ranging from subclinical hypothyroidism to overt thyroid hormone derangements. These dysfunctions potentially complicate patient outcomes by modifying systemic physiology, immune responses, and metabolic balance, which are critical in cancer progression and therapy tolerance.

The systematic review at hand synthesized data spanning over two decades, from January 2000 to October 2024, drawing from multiple comprehensive databases such as Medline, Web of Science, and Embase. The rigorous methodology adhered strictly to PRISMA guidelines, ensuring that the included studies were evaluated with robust criteria to mitigate bias and confounding variables. The Newcastle-Ottawa Scale and the Cochrane Risk of Bias tool were employed for quality assessment, underpinning the meta-analysis with a foundation of scientific rigor.

From the initial pool of studies, six fulfilled the stringent inclusion criteria for systematic review, with four offering quantifiable hazard ratios suitable for meta-analytic pooling. These four studies collectively assessed 671 patients and provided crucial comparative insights between those exhibiting thyroid dysfunction and their euthyroid counterparts, focusing particularly on overall survival metrics—a primary endpoint in oncological prognostication.

Analyzing the pooled data through a fixed-effect model initially suggested a negligible protective effect of thyroid dysfunction on survival (HR of 0.99), a finding paradoxically associated with statistically significant results yet shadowed by pronounced heterogeneity among the studies (I² = 81.5%). This statistical inconsistency prompted the application of a random-effects model, more apt for accommodating inter-study variability. Remarkably, this model revealed no statistically significant association between thyroid dysfunction and overall survival (HR of 1.45, p=0.36), thus challenging earlier assumptions and highlighting the complexity inherent in the interplay between endocrine disturbance and cancer outcomes.

The notable heterogeneity detected reflects potential disparities in study designs, thyroid dysfunction definitions, patient demographics, cancer subtypes, and therapeutic regimes, all of which could contribute to conflicting results. The variance underscores an urgent need for standardized criteria across future investigations to better elucidate thyroid dysfunction’s role in HNC prognosis.

This meta-analysis importantly highlights the pervasive challenge of inconsistent reporting in clinical research fields intersecting oncology and endocrinology. It calls for harmonized definitions of thyroid dysfunction, including biochemical thresholds and clinical symptomatology, to ensure comparability and reproducibility. Moreover, it emphasizes the importance of adjusted effect estimates that account for confounders such as age, tumor stage, and radiotherapy dose, which might independently impact survival.

The absence of a clear link between thyroid status and survival prompts a reevaluation of routine thyroid function monitoring and management strategies in HNC patients. While thyroid dysfunction is prevalent post-radiotherapy, this analysis suggests that its presence alone may not predict survival outcomes, although its impact on quality of life and treatment toxicity remains undisputed.

Looking ahead, the study advocates for large-scale, prospective cohort studies equipped with granular data capturing thyroid function dynamics throughout the cancer treatment trajectory. Such studies could explore causal pathways, identify susceptible patient subgroups, and possibly integrate molecular biomarkers that might refine risk stratification and therapeutic decisions.

Furthermore, the biological mechanisms underlying thyroid dysfunction’s influence on tumorigenesis and cancer progression are poorly understood. Thyroid hormones are known to modulate cellular metabolism, angiogenesis, and immune surveillance, all pivotal in cancer pathophysiology. Enhanced translational research efforts focusing on these pathways may unlock new therapeutic avenues or prognostic indicators.

The authors of this meta-analysis also draw attention to the methodological considerations vital in meta-analytic studies involving complex clinical parameters. Effect size estimation, appropriate model selection acknowledging heterogeneity, and comprehensive bias assessment collectively underpin the credibility of synthesized evidence in informing clinical practice.

Clinicians managing HNC patients should remain vigilant regarding thyroid health, ensuring timely detection and treatment of thyroid dysfunction. However, therapeutic decisions should consider current evidence suggesting that correcting thyroid abnormalities might not translate directly into improved survival but should aim to enhance overall wellness and treatment tolerance.

In summary, this meticulous meta-analysis advances our understanding of the thyroid-cancer nexus in head and neck oncology, delineating the uncertainties and gaps in existing literature while charting a path forward for research and clinical management. It underscores the imperative for continued interdisciplinary collaboration to optimize patient outcomes in this multifaceted disease arena.

As the oncology community strives to personalize and refine cancer care, unraveling the contributions of endocrine factors such as thyroid dysfunction remains a promising yet challenging frontier. This analysis serves as a crucial stepping stone toward that goal, reaffirming the complexity of cancer biology and the necessity for comprehensive, high-quality evidence to guide therapeutic strategies.

Given the profound implications for survival and quality of life in HNC survivors, enhanced surveillance protocols and clinical trials investigating endocrine interventions may be future cornerstones in integrated cancer care paradigms. The current findings urge caution against oversimplification and encourage nuanced, evidence-based approaches informed by continued rigorous research.

In conclusion, the intersection of thyroid dysfunction and head and neck cancer survival emerges from this extensive review as a domain marked by complexity and variability, necessitating further robust, methodologically sound investigations to define definitive clinical implications and optimize patient outcomes in the years ahead.

Subject of Research:
Impact of thyroid dysfunction on clinical outcomes in head and neck cancer patients and its association with survival metrics.

Article Title:
Impact of thyroid dysfunction on clinical outcome in head and neck cancer: a systematic review and meta-analysis

Article References:
N. C, S., Roberts, T.S., Opperman, J.F. et al. Impact of thyroid dysfunction on clinical outcome in head and neck cancer: a systematic review and meta-analysis. BMC Cancer 25, 1605 (2025). https://doi.org/10.1186/s12885-025-15004-z

Image Credits:
Scienmag.com

DOI:
https://doi.org/10.1186/s12885-025-15004-z