This study investigates the feasibility of reducing manual image annotation costs in medical image database construction by utilizing a step by step approach where the Artificial Intelligence model (AI model) trained on a previous batch of data automatically pre-annotates the next batch of image data, taking ultrasound image of thyroid nodule annotation as an example. The study used YOLOv8 as the AI model. During the AI model training, in addition to conventional image augmentation techniques, augmentation methods specifically tailored for ultrasound images were employed to balance the quantity differences between thyroid nodule classes and enhance model training effectiveness. The study found that training the model with augmented data significantly outperformed training with raw images data. When the number of original images number was only 1,360, with 7 thyroid nodule classifications, pre-annotation using the AI model trained on augmented data could save at least 30% of the manual annotation workload for junior physicians. When the scale of original images number reached 6,800, the classification accuracy of the AI model trained on augmented data was very close with that of junior physicians, eliminating the need for manual preliminary annotation.

Transrectal ultrasound, particularly in the combination of high-frequency ultrasound and MR-TRUS fusion technologies, provides a highly precise and effective method for correlation and targeted biopsy of suspicious intraprostatic lesions detected by MRI. Advances in imaging technology, driven by 29 Mhz micro-ultrasound transducers, robotic-assisted systems, and the integration of AI-based analyses, promise further improvements in diagnostic accuracy and a reduction in unnecessary biopsies. Further technological advancements and improved TRUS training could contribute to a decentralized and cost-effective diagnostic evaluation of prostate cancer in the future.

Surgical tool tip localization and tracking are essential components of surgical and interventional procedures. The cross sections of tool tips can be considered as acoustic point sources to achieve these tasks with deep learning applied to photoacoustic channel data. However, source localization was previously limited to the lateral and axial dimensions of an ultrasound transducer. In this article, we developed a novel deep learning-based 3-D photoacoustic point source localization system using an object detection-based approach extended from our previous work. In addition, we derived theoretical relationships among point source locations, sound speeds, and waveform shapes in raw photoacoustic channel data frames. We then used this theory to develop a novel deep learning instance segmentation-based 3-D point source localization system. When tested with 4000 simulated, 993 phantom, and 1983 ex vivo channel data frames, the two systems achieved F1 scores as high as 99.82%, 93.05%, and 98.20%, respectively, and Euclidean localization errors (mean ± one standard deviation) as low as ${1.46} \; \pm \; {1.11}$ mm, ${1.58} \; \pm \; {1.30}$ mm, and ${1.55} \; \pm \; {0.86}$ mm, respectively. In addition, the instance segmentation-based system simultaneously estimated sound speeds with absolute errors (mean ± one standard deviation) of ${19.22} \; \pm \; {26.26}$ m/s in simulated data and standard deviations ranging 14.6-32.3 m/s in experimental data. These results demonstrate the potential of the proposed photoacoustic imaging-based methods to localize and track tool tips in three dimensions during surgical and interventional procedures.

Integrating artificial intelligence (AI) in the dental field can potentially enhance the efficiency of dental care. However, few studies have investigated whether AI software can achieve results comparable to those obtained by dental practitioners (general practitioners (GPs) and specialists) when assessing alveolar bone loss in a clinical setting. Thus, this study compared the performance of AI in assessing periodontal bone loss with those of GPs and specialists. This comparative cross-sectional study evaluated the performance of dental practitioners and AI software in assessing alveolar bone loss. Radiographs were randomly selected to ensure representative samples. Dental practitioners independently evaluated the radiographs, and the AI software "Second Opinion Software" was tested using the same set of radiographs evaluated by the dental practitioners. The results produced by the AI software were then compared with the baseline values to measure their accuracy and allow direct comparison with the performance of human specialists. The survey received 149 responses, where each answered 10 questions to compare the measurements made by AI and dental practitioners when assessing the amount of bone loss radiographically. The mean estimates of the participants had a moderate positive correlation with the radiographic measurements (rho = 0.547, <i>p</i> < 0.001) and a weaker but still significant correlation with AI measurements (rho = 0.365, <i>p</i> < 0.001). AI measurements had a stronger positive correlation with the radiographic measurements (rho = 0.712, <i>p</i> < 0.001) compared with their correlation with the estimates of dental practitioners. This study highlights the capacity of AI software to enhance the accuracy and efficiency of radiograph-based evaluations of alveolar bone loss. Dental practitioners are vital for the clinical experience but AI technology provides a consistent and replicable methodology. Future collaborations between AI experts, researchers, and practitioners could potentially optimize patient care.

This study aimed to evaluate the diagnostic performance of an artificial intelligence (AI)-based platform (Diagnocat) in detecting periapical radiolucencies (PARLs) in cone-beam computed tomography (CBCT) scans of molars. Specifically, we assessed Diagnocat's performance in detecting PARLs in non-root-filled molars and compared its diagnostic performance between preoperative and postoperative scans. This retrospective study analyzed preoperative and postoperative CBCT scans of 134 molars (327 roots). PARLs detected by Diagnocat were compared with assessments independently performed by two experienced endodontists, serving as the reference standard. Diagnostic performance was assessed at both tooth and root levels using sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), F1 score, and the area under the receiver operating characteristic curve (AUC-ROC). In preoperative scans of non-root-filled molars, Diagnocat demonstrated high sensitivity (teeth: 93.9%, roots: 86.2%), moderate specificity (teeth: 65.2%, roots: 79.9%), accuracy (teeth: 79.1%, roots: 82.6%), PPV (teeth: 71.8%, roots: 75.8%), NPV (teeth: 91.8%, roots: 88.8%), and F1 score (teeth: 81.3%, roots: 80.7%) for PARL detection. The AUC was 0.76 at the tooth level and 0.79 at the root level. Postoperative scans showed significantly lower PPV (teeth: 54.2%; roots: 46.9%) and F1 scores (teeth: 67.2%; roots: 59.2%). Diagnocat shows promise in detecting PARLs in CBCT scans of non-root-filled molars, demonstrating high sensitivity but moderate specificity, highlighting the need for human oversight to prevent overdiagnosis. However, diagnostic performance declined significantly in postoperative scans of root-filled molars. Further research is needed to optimize the platform's performance and support its integration into clinical practice. AI-based platforms such as Diagnocat can assist clinicians in detecting PARLs in CBCT scans, enhancing diagnostic efficiency and supporting decision-making. However, human expertise remains essential to minimize the risk of overdiagnosis and avoid unnecessary treatment.

Intraoperative ultrasound (ioUS) is a valuable tool in brain tumor surgery due to its versatility, affordability, and seamless integration into the surgical workflow. However, its adoption remains limited, primarily because of the challenges associated with image interpretation and the steep learning curve required for effective use. This study aimed to enhance the interpretability of ioUS images by developing a real-time brain tumor detection system deployable in the operating room. We collected 2D ioUS images from the BraTioUS and ReMIND datasets, annotated with expert-refined tumor labels. Using the YOLO11 architecture and its variants, we trained object detection models to identify brain tumors. The dataset included 1732 images from 192 patients, divided into training, validation, and test sets. Data augmentation expanded the training set to 11,570 images. In the test dataset, YOLO11s achieved the best balance of precision and computational efficiency, with a mAP@50 of 0.95, mAP@50-95 of 0.65, and a processing speed of 34.16 frames per second. The proposed solution was prospectively validated in a cohort of 20 consecutively operated patients diagnosed with brain tumors. Neurosurgeons confirmed its seamless integration into the surgical workflow, with real-time predictions accurately delineating tumor regions. These findings highlight the potential of real-time object detection algorithms to enhance ioUS-guided brain tumor surgery, addressing key challenges in interpretation and providing a foundation for future development of computer vision-based tools for neuro-oncological surgery.

Accurate preoperative detection and analysis of lymph node metastasis (LNM) in head and neck squamous cell carcinoma (HNSCC) is essential for the surgical planning and execution of a neck dissection and may directly affect the morbidity and prognosis of patients. Additionally, predicting extranodal extension (ENE) using pre-operative imaging could be particularly valuable in oropharyngeal HPV-positive squamous cell carcinoma, enabling more accurate patient counseling, allowing the decision to favor primary chemoradiotherapy over immediate neck dissection when appropriate. Currently, radiological images are evaluated by radiologists and head and neck oncologists; and automated image interpretation is not part of the current standard of care. Therefore, the value of preoperative image recognition by artificial intelligence (AI) with the large language model (LLM) ChatGPT-4 V was evaluated in this exploratory study based on neck computed tomography (CT) images of HNSCC patients with cervical LNM, and corresponding images without LNM. The objective of this study was to firstly assess the preoperative rater accuracy by comparing clinician assessments of imaging-detected extranodal extension (iENE) and the extent of neck dissection to AI predictions, and secondly to evaluate the pathology-based accuracy by comparing AI predictions to final histopathological outcomes. 45 preoperative CT scans were retrospectively analyzed in this study: 15 cases in which a selective neck dissection (sND) was performed, 15 cases with ensuing radical neck dissection (mrND), and 15 cases without LNM (sND). Of note, image analysis was based on three single images provided to both ChatGPT-4 V and the head and neck surgeons as reviewers. Final pathological characteristics were available in all cases as HNSCC patients had undergone surgery. ChatGPT-4 V was tasked with providing the extent of LNM in the preoperative CT scans and with providing a recommendation for the extent of neck dissection and the detection of iENE. The diagnostic performance of ChatGPT-4 V was reviewed independently by two head and neck surgeons with its accuracy, sensitivity, and specificity being assessed. In this study, ChatGPT-4 V reached a sensitivity of 100% and a specificity of 34.09% in identifying the need for a radical neck dissection based on neck CT images. The sensitivity and specificity of detecting iENE was 100% and 34.15%, respectively. Both human reviewers achieved higher specificity. Notably, ChatGPT-4 V also recommended a mrND and detected iENE on CT images without any cervical LNM. In this exploratory study of 45 preoperative CT Neck scans before a neck dissection, ChatGPT-4 V substantially overestimated the degree and severity of lymph node metastasis in head and neck cancer. While these results suggest that ChatGPT-4 V may not yet be a tool providing added value for surgical planning in head and neck cancer, the unparalleled speed of analysis and well-founded reasoning provided suggests that AI tools may provide added value in the future.

Accurate measurement of hallux valgus angle (HVA) and intermetatarsal angle (IMA) is essential for diagnosing hallux valgus and determining appropriate treatment strategies. Traditional manual measurement methods, while standardized, are time-consuming, labor-intensive, and subject to evaluator bias. Recent advancements in deep learning have been applied to hallux valgus angle estimation, but the development of effective algorithms requires large, well-annotated datasets. Existing X-ray datasets are typically limited to cropped foot regions images, and only one dataset containing very few samples is publicly available. To address these challenges, we introduce HVAngleEst, the first large-scale, open-access dataset specifically designed for hallux valgus angle estimation. HVAngleEst comprises 1,382 X-ray images from 1,150 patients and includes comprehensive annotations, such as foot localization, hallux valgus angles, and line segments for each phalanx. This dataset enables fully automated, end-to-end hallux valgus angle estimation, reducing manual labor and eliminating evaluator bias.

This systematic review investigates the potential of artificial intelligence (AI) in improving the accuracy and efficiency of prostate-specific membrane antigen positron emission tomography (PSMA PET) scans for detecting metastatic prostate cancer. A comprehensive literature search was conducted across Medline, Embase, and Web of Science, adhering to PRISMA guidelines. Key search terms included "artificial intelligence," "machine learning," "deep learning," "prostate cancer," and "PSMA PET." The PICO framework guided the selection of studies focusing on AI's application in evaluating PSMA PET scans for staging lymph node and distant metastasis in prostate cancer patients. Inclusion criteria prioritized original English-language articles published up to October 2024, excluding studies using non-PSMA radiotracers, those analyzing only the CT component of PSMA PET-CT, studies focusing solely on intra-prostatic lesions, and non-original research articles. The review included 22 studies, with a mix of prospective and retrospective designs. AI algorithms employed included machine learning (ML), deep learning (DL), and convolutional neural networks (CNNs). The studies explored various applications of AI, including improving diagnostic accuracy, sensitivity, differentiation from benign lesions, standardization of reporting, and predicting treatment response. Results showed high sensitivity (62% to 97%) and accuracy (AUC up to 98%) in detecting metastatic disease, but also significant variability in positive predictive value (39.2% to 66.8%). AI demonstrates significant promise in enhancing PSMA PET scan analysis for metastatic prostate cancer, offering improved efficiency and potentially better diagnostic accuracy. However, the variability in performance and the "black box" nature of some algorithms highlight the need for larger prospective studies, improved model interpretability, and the continued involvement of experienced nuclear medicine physicians in interpreting AI-assisted results. AI should be considered a valuable adjunct, not a replacement, for expert clinical judgment.