OBJECTIVE: To explore the relationship between the effect of acupuncture and layered localization of computed tomography (CT) in treatment of lumbar disc herniation. METHODS: Based on the CT layered localization, the herniated lumbar discs were positioned in 5 layers, A, B, C, D and E among 300 patients with lumbar disc herniation. Combined with the horizontal and the frontal planes, the three-dimensional location was formed. Acupuncture was delivered at acupoints including bilateral Shenshu (BL23), Dachangshu (BL25), and Huantiao (GB30), Weizhong (BL40) on the affected side. One intervention of acupuncture was 30 min, once daily; 1 course of treatment was composed of 10 interventions and 2 courses were required. Before and after treatment, Japanese orthopaedic association (JOA) score was recorded, and the effect was evaluated. The curative effect was classified and compared with the CT layered localization. RESULTS: Of 300 patients, 226 cases were effective and the effective rate was 75.33%. The JOA scores of all patients, and in the effective group and the non-effective group were higher compared with the scores before treatment (P<0.05). With the layered localization considered, acupuncture was more effective on the cases positioned in C layer. Regarding the horizontal plane, the effect was better on the cases with zone 1 and zone 1-2 involved. In terms of the grade of frontal plane, acupuncture was more effective on the cases graded Ⅰ and Ⅱ. CONCLUSION The clinical effect of acupuncture on lumbar disc herniation is related with the layer and the horizontal zone of herniated disc positioned, as well as to the grade of the frontal plane.
Humans ; Acupuncture Therapy ; Intervertebral Disc Displacement/diagnostic imaging* ; Male ; Female ; Middle Aged ; Adult ; Tomography, X-Ray Computed ; Lumbar Vertebrae/diagnostic imaging* ; Acupuncture Points ; Aged ; Young Adult ; Treatment Outcome

BACKGROUND: There is little literature describing the artificial intelligence (AI)-aided diagnosis of severe pneumonia (SP) subphenotypes and the association of the subphenotypes with the ventilatory treatment efficacy. The aim of our study is to illustrate whether clinical and biological heterogeneity, such as ventilation and gas-exchange, exists among patients with SP using chest computed tomography (CT)-based AI-aided latent class analysis (LCA). METHODS: This retrospective study included 413 patients hospitalized at Xinhua Hospital diagnosed with SP from June 1, 2015 to May 30, 2020. AI quantification results of chest CT and their combination with additional clinical variables were used to develop LCA models in an SP population. The optimal subphenotypes were determined though evaluating statistical indicators of all the LCA models, and clinical implications of them such as guiding ventilation strategies were further explored by statistical methods. RESULTS: The two-class LCA model based on AI quantification results of chest CT can describe the biological characteristics of the SP population well and hence yielded the two clinical subphenotypes. Patients with subphenotype-1 had milder infections ( P <0.001) than patients with subphenotype-2 and had lower 30-day ( P <0.001) and 90-day ( P <0.001) mortality, and lower in-hospital ( P = 0.001) and 2-year ( P <0.001) mortality. Patients with subphenotype-1 showed a better match between the percentage of non-infected lung volume (used to quantify ventilation) and oxygen saturation (used to reflect gas exchange), compared with patients with subphenotype-2. There were significant differences in the matching degree of lung ventilation and gas exchange between the two subphenotypes ( P <0.001). Compared with patients with subphenotype-2, those with subphenotype-1 showed a relatively better match between CT-based AI metrics of the non-infected region and oxygenation, and their clinical outcomes were effectively improved after receiving invasive ventilation treatment. CONCLUSIONS A two-class LCA model based on AI quantification results of chest CT in the SP population particularly revealed clinical heterogeneity of lung function. Identifying the degree of match between ventilation and gas-exchange may help guide decisions about assisted ventilation.
Humans ; Tomography, X-Ray Computed/methods* ; Male ; Female ; Retrospective Studies ; Middle Aged ; Artificial Intelligence ; Aged ; Pneumonia/diagnosis* ; Latent Class Analysis ; Adult

BACKGROUND: The correlation between the morphological structure of the patellofemoral joint (PFJ) and the severity of knee joint osteoarthritis (KOA) remains uncertain. This study aims to investigate the correlation between the severity of knee joint osteoarthritis and the alignment of patellofemoral and patellar height on radiographs. METHODS: This multi-center, retrospective study analyzed the magnetic resonance imaging (MRI) scans and anteroposterior radiographs of 534 adult outpatients with KOA. To evaluate the radiographic severity of KOA, anteroposterior radiographs of the knee and the Kellgren-Lawrence (K-L) grade were used. Knee MRI scans were used to measure the patellar length ratio (PLR), sulcus angle (SA), lateral patellar tilt angle (LPTA), and the distance between tibial tuberosity and trochlear groove (TT-TG). We examined the association between the configuration of the PFJ, arrangement, and harshness of the KOA. Information on participants' demographics, such as age, sex, side, height, and weight, was collected. A chi-squared test was used for the correlation of radiographic severity of KOA with sex and the affected side. Spearman correlation was used for patellofemoral alignment or morphology and the radiographic severity of lateral KOA. Multiple linear regression models were used for the association between LPTA, SA, TT-TG, and severity of KOA after accounting for demographic variables. RESULTS: The study comprised of 534 patients; of these, 339 (63%) were female. A total of 586 knees were evaluated in this study. Age showed a strong positive correlation with KOA severity ( r  = 0.516, P <0.01), whereas LPTA showed a strong negative correlation ( r  = -0.662, P <0.01). Additionally, SA ( r  = 0.616, P <0.05), and TT-TG showed a strong positive correlation ( r  = 0.770, P <0.01) with tibiofemoral osteoarthritis (TFOA) severity. Multiple linear regression analysis indicated that knee osteoarthritis severity (β = -2.946, P <0.001) and side (β = -0.839, P  = 0.001) was associated with LPTA; knee osteoarthritis severity (β = 5.032, P <0.001) and age (β = -0.095, P <0.001) was associated with SA; knee osteoarthritis severity (β = 2.445, P <0.001), sex (β = -0.326, P  = 0.041), body mass index (β = -0.061, P  = 0.017) and age (β = -0.025, P <0.001) was associated with TT-TG. CONCLUSION Radiographic severity of KOA was positively associated with age, SA, and TT-TG but negatively associated with LPTA.
Humans ; Female ; Male ; Osteoarthritis, Knee/pathology* ; Middle Aged ; Retrospective Studies ; Aged ; Patellofemoral Joint/pathology* ; Magnetic Resonance Imaging ; Adult ; Patella/pathology* ; Radiography

Large language models (LLMs) such as ChatGPT, Claude, Llama, and Qwen are emerging as transformative technologies for the diagnosis and treatment of various diseases. With their exceptional long-context reasoning capabilities, LLMs are proficient in clinically relevant tasks, particularly in medical text analysis and interactive dialogue. They can enhance diagnostic accuracy by processing vast amounts of patient data and medical literature and have demonstrated their utility in diagnosing common diseases and facilitating the identification of rare diseases by recognizing subtle patterns in symptoms and test results. Building on their image-recognition abilities, multimodal LLMs (MLLMs) show promising potential for diagnosis based on radiography, chest computed tomography (CT), electrocardiography (ECG), and common pathological images. These models can also assist in treatment planning by suggesting evidence-based interventions and improving clinical decision support systems through integrated analysis of patient records. Despite these promising developments, significant challenges persist regarding the use of LLMs in medicine, including concerns regarding algorithmic bias, the potential for hallucinations, and the need for rigorous clinical validation. Ethical considerations also underscore the importance of maintaining the function of supervision in clinical practice. This paper highlights the rapid advancements in research on the diagnostic and therapeutic applications of LLMs across different medical disciplines and emphasizes the importance of policymaking, ethical supervision, and multidisciplinary collaboration in promoting more effective and safer clinical applications of LLMs. Future directions include the integration of proprietary clinical knowledge, the investigation of open-source and customized models, and the evaluation of real-time effects in clinical diagnosis and treatment practices.
Humans ; Large Language Models ; Tomography, X-Ray Computed

Artificial intelligence (AI), particularly deep learning, has demonstrated remarkable performance in medical imaging across a variety of modalities, including X-ray, computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, positron emission tomography (PET), and pathological imaging. However, most existing state-of-the-art AI techniques are task-specific and focus on a limited range of imaging modalities. Compared to these task-specific models, emerging foundation models represent a significant milestone in AI development. These models can learn generalized representations of medical images and apply them to downstream tasks through zero-shot or few-shot fine-tuning. Foundation models have the potential to address the comprehensive and multifactorial challenges encountered in clinical practice. This article reviews the clinical applications of both task-specific and foundation models, highlighting their differences, complementarities, and clinical relevance. We also examine their future research directions and potential challenges. Unlike the replacement relationship seen between deep learning and traditional machine learning, task-specific and foundation models are complementary, despite inherent differences. While foundation models primarily focus on segmentation and classification, task-specific models are integrated into nearly all medical image analyses. However, with further advancements, foundation models could be applied to other clinical scenarios. In conclusion, all indications suggest that task-specific and foundation models, especially the latter, have the potential to drive breakthroughs in medical imaging, from image processing to clinical workflows.
Humans ; Artificial Intelligence ; Deep Learning ; Diagnostic Imaging/methods* ; Magnetic Resonance Imaging ; Tomography, X-Ray Computed ; Positron-Emission Tomography

Objective: To investigate the application effectiveness of the artificial intelligence(AI) based Generative Pre-treatment tool of Skeletal Pathology (GPTSP) in measuring functional lumbar radiographic examinations. Methods: This is a retrospective case series study,reviewing the clinical and imaging data of 34 patients who underwent lumbar dynamic X-ray radiography at Department of Orthopedics, the Second Hospital of Shandong University from September 2021 to June 2023. Among the patients, 13 were male and 21 were female, with an age of (68.0±8.0) years (range:55 to 88 years). The AI model of the GPTSP system was built upon a multi-dimensional constrained loss function constructed based on the YOLOv8 model, incorporating Kullback-Leibler divergence to quantify the anatomical distribution deviation of lumbar intervertebral space detection boxes, along with the introduction of a global dynamic attention mechanism. It can identify lumbar vertebral body edge points and measure lumbar intervertebral space. Furthermore, spondylolisthesis index, lumbar index, and lumbar intervertebral angles were measured using three methods: manual measurement by doctors, predefined annotated measurement, and AI-assisted measurement. The consistency between the doctors and the AI model was analyzed through intra-class correlation coefficient (ICC) and Kappa coefficient. Results: AI-assisted physician measurement time was (1.5±0.1) seconds (range: 1.3 to 1.7 seconds), which was shorter than the manual measurement time ((2 064.4±108.2) seconds,range: 1 768.3 to 2 217.6 seconds) and the pre-defined annotation measurement time ((602.0±48.9) seconds,range: 503.9 to 694.4 seconds). Kappa values between physicians' diagnoses and AI model's diagnoses (based on GPTSP platform) for the lumbar slip index, lumbar index, and intervertebral angles measured by three methods were 0.95, 0.92, and 0.82 (all P<0.01), with ICC values consistently exceeding 0.90, indicating high consistency. Based on the doctor's manual measurement, compared with the predefined label measurement, altering AI assistance, doctors measurement with average annotation errors reduced from 2.52 mm (range: 0.01 to 6.78 mm) to 1.47 mm(range: 0 to 5.03 mm). Conclusions: The GPTSP system enhanced efficiency in functional lumbar analysis. AI model demonstrated high consistency in annotation and measurement results, showing strong potential to serve as a reliable clinical auxiliary tool.
Humans ; Female ; Retrospective Studies ; Male ; Lumbar Vertebrae/diagnostic imaging* ; Middle Aged ; Aged ; Aged, 80 and over ; Artificial Intelligence ; Radiography ; Spondylolisthesis/diagnostic imaging*

OBJECTIVES

To present a rare case of a 17-year-old girl with multiple odontogenic keratocysts, skeletal abnormalities, central nervous system and cutaneous anomalies.

METHODS

Design:Case Report

Setting:Tertiary Government Training Hospital

Patient: One

RESULTS

A 17-year-old Filipino girl presented with a one-year history of progressive left mandibular swelling. Orthopantomography revealed multiple cysts involving the mandible and maxillae. Histopathologic examination of incision biopsy specimens confirmed odontogenic keratocysts. Other physical examination findings included coarse face and multiple palmar and plantar pits. Radiologic investigations demonstrated calcification of the falx cerebri and tentorium cerebelli, bifid rib and cervicothoracic scoliosis. Based on clinical, radiological, and histopathological findings, a diagnosis of Gorlin-Goltz syndrome was established. The patient underwent enucleation and curettage of the cysts with peripheral ostectomy, and there was no recurrence on repeat orthopantomography at six months and two years post-operatively. However, on the fourth year, the patient claimed there was a mandibular cyst which was not verified as she was lost to follow-up.

CONCLUSION

This case highlights the importance of recognizing multiple odontogenic keratocysts as a potential manifestation of Gorlin-Goltz Syndrome. Early diagnosis enables appropriate management and long term surveillance to monitor for other manifestations of this syndrome that may occur later in life.

Human ; Female ; Adolescent: 13-18 Yrs Old ; Basal Cell Nevus Syndrome ; Mandible ; Radiography, Panoramic ; Focal Dermal Hypoplasia ; Ribs ; Scoliosis ; Spinal Cord ; Women ; History ; Lost To Follow-up ; Diagnosis

Duplication of common bile duct is a rare anatomic congenital anomaly of the biliary tree that may present with many types. Such cases are usually clinically silent unless presented with other concomitant conditions that may also be symptomatic in normal variants. Obstruction of the duplicated common bile ducts due to bile duct stones or choledocholithiasis have also been seldom reported. Hence, we present a case of a 42-year-old female, presenting with abdominal pain and jaundice, with incidental finding of duplication of common bile duct Type IV, with choledocholithiases of duplicates on intra-operative cholangiogram. This will be the first in its kind to adequately document and report the intra-operative findings of this anatomic congenital anomaly in our locale.

Human ; Female ; Adult: 25-44 Yrs Old ; Choledocholithiasis ; Cholangiography ; Common Bile Duct ; Biliary Tract ; Bile Ducts ; Abdominal Pain ; Bile

BACKGROUND

Mobile clinics offer crucial healthcare services, including X-ray examinations, to underserved communities. Minimizing image repeats in this setting is vital due to radiation exposure, patient inconvenience, and cost implications.

OBJECTIVES

This study investigated the prevalence and causes of image repeat in conventional radiography performed within mobile clinics in the Philippines.

METHODS

A retrospective review analyzed data from five mobile clinics located in two highly urbanized cities in the Philippines from July to December 2023). Radiology staff assessed image quality, with suboptimal images requiring retakes. Reasons for rejection were categorized.

RESULTS

Out of 871 radiographs taken, 118 (13.55%) were repeated. Vertebrae and pelvic girdle images had the highest repeat rates (33.33%). Positioning errors were the most common cause (44.07%), followed by underexposure and overexposure.

CONCLUSION

This study identified a concerning repeat rate (13.55%) for mobile X-rays, primarily due to improper patient positioning, particularly for specific body parts. Targeted training programs and stricter protocols for mobile clinic staff are needed. Radiography education should also emphasize these skills, potentially through collaboration with mobile clinic operators to ensure graduates are prepared for the unique challenges of this environment.

Mobile Health Units ; Patient Positioning ; Radiography ; X-rays ; X-ray Film

Objective:To investigate the anatomic origin of juvenile nasopharyngeal angiofibroma（JNA） through radiologic analysis of tumor invasion patterns, providing insights into tumor etiology and surgical recurrence prevention. Methods:This retrospective cohort study included primary JNA cases at the Department of Otorhinolaryngology, Eye and ENT Hospital of Fudan University from March 2015 to September 2024. All patients underwent preoperative high-resolution CT（HRCT） scans, and some underwent enhanced magnetic resonance imaging. The study retrospectively analyzed the patients' imaging data to examine tumor invasion into the pterygopalatine fossa and the vidian canal. These sites were categorized into non-invaded, partially invaded, and completely invaded for the pterygopalatine fossa and the vidian canal. The study analyzed the proportions of invasion at these sites to further speculate on the origin of JNA. Results:A total of 105 JNA patients were included in the study. Among them, 100% of the patients had complete tumor invasion in the pterygopalatine fossa. For the vidian canal, the proportions of complete invasion, partial invasion, and non-invasion were 54.3%, 27.6%, and 18.1%, respectively. As the staging of JNA tumors increased, the proportion of vidian canal invasion also increased. Conclusion:Our evidence suggests that the pterygopalatine fossa, rather than the vidian canal, might be the likely origin of JNA, which is enlightening for the study of the etiological mechanisms of JNA.
Humans ; Nasopharyngeal Neoplasms/pathology* ; Retrospective Studies ; Angiofibroma/pathology* ; Neoplasm Invasiveness ; Pterygopalatine Fossa/pathology* ; Female ; Magnetic Resonance Imaging ; Male ; Tomography, X-Ray Computed ; Adolescent