Image-based Digital Tools for Diagnosis and Surgical Treatment: Applications, Challenges, and Prospects Authors

Laura Cercenelli^1*Adrian Elmi-Terander²Thomas Maal³Francesca Manni⁴Virginia Mamone⁵

• ¹Biomedical Technology Unit, eDIMES Lab - Laboratory of Bioengineering, Department of Medical and Surgical Sciences, University of Bologna,, Bologna, Italy
• ²Department of Clinical Neuroscience, Karolinska Institutet (KI), Stockholm, Sweden
• ³Radboud University Medical Centre, Nijmegen, Gelderland, Netherlands
• ⁴Eindhoven University of Technology, Eindhoven, Netherlands
• ⁵Cisanello Hospital, Pisa, Italy

The study by Abidin et al. (2024) presents a comprehensive review of brain tumor segmentation using multi-modal MRI and deep learning techniques. Their survey categorizes state-of-the-art models into CNN-based, transformer-based, and hybrid architectures, underscoring the strengths and weaknesses of each approach.Learning Network (PL-Net), which optimizes medical image segmentation by integrating coarse-tofine semantic learning without increasing computational complexity. Across these studies, a shared emphasis emerges on refining feature extraction and dealing with data complexity through attention mechanisms or radiomic modeling. Several approaches tackle the ongoing challenges of data imbalance and annotation scarcity, particularly in high-detail segmentation tasks. Additionally, transformer-based methods consistently appear as a unifying trend aimed at capturing long-range dependencies and improving contextual awareness.The rapid evolution of 3D imaging, modeling and printing technologies is creating unparalleled opportunities to optimize surgical planning, improve patient outcomes, and driving progress in regenerative medicine.In this context, Henckel et al. ( 2023) advanced the validation of low-dose 3D-CT imaging for acetabular implant orientation in orthopedic surgery. Their findings confirmed that 3D-CT is a highly accurate and precise modality for measuring cup inclination and version in total hip arthroplasty, outperforming conventional 2D CT methods. The results support 3D-CT as a standard for postoperative evaluation and surgical system optimization.Similarly, Wu et al .(2023) developed novel 2D and 3D CT-based injury models for predicting the risk of femoral neck fracture in patients with femoral head fractures. Their quantitative parameters-such as percentage of maximum defect length and fracture area-showed high diagnostic accuracy and can serve as reliable predictors of fracture risk. This approach provides clinicians with valuable decision-making tools in trauma management and preoperative risk assessment.In the domain of 3D printing, Capellini et al. (2024) presented a brief research report on the application of 3D printed models for the management of complex congenital heart disease. Their work underscores how physical models derived from high-resolution MRI and CT datasets allow for a detailed and tactile understanding of intricate pediatric cardiac anatomies. Among the techniques explored, selective laser sintering (SLS) proved to be the most cost-effective and timeefficient solution. 2023) explored bioartificial scaffolds produced using solvent casting, electrospinning, and 3D printing within the field of regenerative medicine. Their findings demonstrate that hybrid scaffolds-merging various fabrication methods-achieve excellent cell viability and mechanical properties similar to native soft tissues.Moving to veterinary surgery, Chambers et al. (2023) evaluated a custom 3D-printed cutting guide for canine caudal maxillectomy, finding that it improved surgical accuracy for both experienced and novice surgeons. While it slightly increased procedure time, the enhanced precision, especially in achieving oncologic margins, suggests that such digital tools can assist less experienced surgeons and improve outcomes in veterinary oncology.The studies in this area collectively highlight the growing reliance on patient-specific models, whether for orthopedic planning and post-operative evaluation, trauma prediction, or surgical simulation. A consistent theme is the shift toward integrating anatomical fidelity with fabrication feasibility-balancing precision and efficiency. Moreover, the translational relevance of these models is emphasized both in human and veterinary medicine, underscoring the broad applicability of 3D digital workflows.The integration of AR into surgery and patient education is another transformative development improving surgical accuracy and enriching communication between clinicians and patients.provide a systematic review of AR applications in orthopedic and maxillofacial oncological surgeries, detailing its potential to enhance precision through improved visualization.The study emphasizes the need for further clinical validation and the integration of external navigation systems to improve accuracy.Similarly, Urlings et al. ( 2023) investigate the role of AR in patient education for intracranial aneurysms, highlighting how immersive visualizations can enhance patient understanding and shared decision-making.Both studies highlight the visualization benefits of AR, while also pointing to common implementation hurdles-particularly the challenges of integrating AR with surgical navigation systems and the need for robust clinical validation. Whether applied in the operating room or during patient consultations, AR consistently emerges as a powerful tool for enhancing spatial understanding. However, technical limitations continue to hinder its broader adoption in clinical settings.The contributions within this Research Topic underscore the transformative impact of digital imaging tools in diagnosis and surgical treatment. From AI-driven segmentation techniques to AR-enhanced surgical interventions, these advancements promise a future where precision medicine is more accessible, efficient, and patient-centered. Across all sections, a shared trajectory emerges: the shift toward more personalized, data-driven, and visually guided approaches to healthcare. While each study presents specific technological innovations, they collectively reflect a broader trend of converging digital methodologies-such as the use of transformers in segmentation, patient-specific 3D models for planning, and immersive AR for both surgeons and patients. These tools not only enhance accuracy and confidence in clinical decisionmaking but also foster interdisciplinary collaboration among clinicians, engineers, and data scientists. Despite these promising advancements, challenges remain on the path to real-world clinical translation. In AI-based segmentation tools, the major issues deal with computational efficiency, the need for large, annotated datasets, and model interpretability. In surgery, AR faces limitations in realtime tracking accuracy and workflow integration. For 3D modeling and printing, the translation of digital data into physical models requires standardized protocols and cost-effectiveness evaluations for broader clinical adoption. Moreover, regulatory hurdles and data governance policies pose significant obstacles, especially for AI applications and patient-specific devices. Bridging the gap between innovation and implementation will require not only technical refinement but also rigorous validation, regulatory clarity, and integration into clinical training and reimbursement frameworks.The insights from the included studies suggest that an integrated ecosystem-where data, tools, and expertise converge-can accelerate the transition from research innovation to bedside impact. Continued research and cross-disciplinary collaborations between computer scientists, engineers, radiologists, and surgeons will be essential for translating innovations from the lab into clinical practice and for achieving the full potential of image-based digital tools in delivering safer, more effective, and tailored patient care. be instrumental in refining these technologies and addressing the challenges that hinder their full potential for safer, more efficient, and tailored patient care.