Heliyon. 2024 Feb 15;10(4):e25933. doi: 10.1016/j.heliyon.2024.e25933. eCollection 2024 Feb 29.

ABSTRACT

Deepfake is a type of face manipulation technique using deep learning that allows for the replacement of faces in videos in a very realistic way. While this technology has many practical uses, if used maliciously, it can have a significant number of bad impacts on society, such as spreading fake news or cyberbullying. Therefore, the ability to detect deepfake has become a pressing need. This paper aims to address the problem of deepfake detection by identifying deepfake forgeries in video sequences. In this paper, a solution to the said problem is presented, which at first uses a stacking based ensemble approach, where features obtained from two popular deep learning models, namely Xception and EfficientNet-B7, are combined. Then by selecting a near-optimal subset of features using a ranking based approach, the final classification is performed to classify real and fake videos using a meta-learner, called multi-layer perceptron. In our experimentation, we have achieved an accuracy of 96.33% on Celeb-DF (V2) dataset and 98.00% on the FaceForensics++ dataset using the meta-learning model both of which are higher than the individual base models. Various types of experiments have been conducted to validate the robustness of the current method.

PMID:39670070 | PMC:PMC11636820 | DOI:10.1016/j.heliyon.2024.e25933

Transl Lung Cancer Res. 2024 Nov 30;13(11):2958-2971. doi: 10.21037/tlcr-24-543. Epub 2024 Nov 28.

ABSTRACT

BACKGROUND: Cachexia is observed in around 60% of patients with extensive-stage small cell lung cancer (ES-SCLC) and may play an important role in the development of resistance to immunotherapy. This study aims to evaluate the influence of cachexia on the effectiveness of immunotherapy, develop and assess a deep learning (DL)-based prediction model for cachexia, as well as its prognostic value.

METHODS: The analysis encompassed ES-SCLC patients who received the combination of first-line immunotherapy and chemotherapy from Shandong Cancer Hospital and Institute, Qilu Hospital, and Jining First People's Hospital. Survival analysis was conducted to examine the correlation between cachexia and the efficacy of immunotherapy. Medical records and computed tomography (CT) images of the third lumbar vertebra (L3) level were collected to construct the clinical model, radiomics, and DL models. The receiver operating characteristic (ROC) curve analysis was conducted to assess and analyze the efficacy of various models in detecting and evaluating the risk of cachexia.

RESULTS: A total of 231 ES-SCLC patients were enrolled in the study. Cachexia was related to inferior progression-free survival (PFS) and overall survival (OS). In internal and external validation cohorts, the area under the curve (AUC) of the DL model were 0.73 and 0.71. Conversely, the radiomics model in external validation cohort recorded an AUC of 0.67, highlighting the superior performance of the DL model and its demonstrated capability for effective generalization in external validation. All patients were categorized into two groups, namely high risk and low risk using the DL model. It was shown that patients with low-risk cachexia were associated with significantly prolonged PFS and OS.

CONCLUSIONS: The DL model not only had better performance in predicting cachexia but also correlated with survival outcomes of ES-SCLC patients who receiving initial immunotherapy.

PMID:39670020 | PMC:PMC11632437 | DOI:10.21037/tlcr-24-543

Radiography (Lond). 2024 Dec 11;31(1):275-280. doi: 10.1016/j.radi.2024.11.027. Online ahead of print.

ABSTRACT

INTRODUCTION: This study investigated the feasibility of single breath-hold (BH) diffusion-weighted MR imaging (DWI) using deep learning reconstruction (DLR) compared to navigator triggered (NT) DWI in patients with malignant liver tumors.

METHODS: This study included 91 patients who underwent both BH-DWI and NT-DWI with 3T MR system. Abdominal MR images were subjectively analyzed to compare visualization of liver edges, presence of ghosting artifacts, conspicuity of malignant liver tumors, and overall image quality. Then, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and apparent diffusion coefficient (ADC) values of malignant liver tumors were objectively measured using regions of interest.

RESULTS: Image quality except conspicuity of malignant liver tumors were significantly better in BH-DW image than in NT-DW image (p < 0.01). Regarding the conspicuity of malignant liver tumors, there was no statistically significant difference between BH-DWI and NT-DWI (p = 0.67). The conspicuity score of 1 or 2 was rendered in 19 (21 %) patients in NT-DWI group. Conversely, BH-DWI showed a score of 3 or 4 in 11 (58 %) of these 19 patients. The SNR was significantly higher in BH-DWI than in NT-DWI (29.5 ± 14.0 vs. 27.3 ± 14.7, p < 0.047). No significant difference was observed between CNR and ADC values of malignant liver tumors between BH-DWI and NT-DWI (5.67 ± 3.57 vs. 5.78 ± 3.08, p = 0.243; 997.2 ± 207.0 vs. 1021.0 ± 253.1, p = 0.547).

CONCLUSION: The BH-DWI using DLR is feasible for liver MRI by improving the SNR and overall image quality, and may play a complementary role to NT-DWI by improving the conspicuity of malignant liver tumor in patients with image distortion in NT-DWI.

IMPLICATIONS FOR PRACTICE: BH-DWI with DLR would be a preferred approach to achieving sufficient image quality in patients with an irregular triggering pattern, as an alternative to NT-DWI. A further reduction in BH duration (<15 s) should be achieved, taking into account patient tolerance.

PMID:39667265 | DOI:10.1016/j.radi.2024.11.027

Food Chem. 2024 Dec 4;468:142304. doi: 10.1016/j.foodchem.2024.142304. Online ahead of print.

ABSTRACT

Antibiotic-resistant bacteria pose considerable risks to global health, particularly through transmission in the food chain. Herein, we developed the artificial intelligence-driven quantification of antibiotic-resistant bacteria in food using a color-encoded multiplex hydrogel digital loop-mediated isothermal amplification (LAMP) system. The quenching of unincorporated amplification signal reporters (QUASR) was first introduced in multiplex digital LAMP. During amplification, primers labeled with different fluorophores were integrated into amplicons, generating color-specific fluorescent spots. While excess primers were quenched by complementary quenching probes. After amplification, fluorescent spots in red, green, and blue emerged in hydrogels, which were automatically identified and quantified using a deep learning model. Methicillin-resistant Staphylococcus aureus and carbapenem-resistant Escherichia coli in real fruit and vegetable samples were also successfully detected. This artificial intelligence-driven color-encoded multiplex hydrogel LAMP offers promising potential for the digital quantification of antibiotic-resistant bacteria in the food industry.

PMID:39667227 | DOI:10.1016/j.foodchem.2024.142304

Comput Biol Med. 2024 Dec 11;185:109521. doi: 10.1016/j.compbiomed.2024.109521. Online ahead of print.

ABSTRACT

BACKGROUND: Machine Learning (ML) models have been used to predict common mental disorders (CMDs) and may provide insights into the key modifiable factors that can identify and predict CMD risk and be targeted through interventions. This systematic review aimed to synthesise evidence from ML studies predicting CMDs, evaluate their performance, and establish the potential benefit of incorporating lifestyle data in ML models alongside biological and/or demographic-environmental factors.

METHODS: This systematic review adheres to the PRISMA statement (Prospero CRD42023401194). Databases searched included MEDLINE, EMBASE, PsycInfo, IEEE Xplore, Engineering Village, Web of Science, and Scopus from database inception to 28/08/24. Included studies used ML methods with feature importance to predict CMDs in adults. Risk of bias (ROB) was assessed using PROBAST. Model performance metrics were compared. The ten most important variables reported by each study were assigned to broader categories to evaluate their frequency across studies.

RESULTS: 117 studies were included (111 model development-only, 16 development and validation). Deep learning methods showed best accuracy for predicting CMD cases. Studies commonly incorporated features from multiple categories (n = 56), and frequently identified demographic-environmental predictors in their top ten most important variables (63/69 models). These tended to be in combination with psycho-social and biological variables (n = 15). Lifestyle data were infrequently examined as sole predictors of CMDs across included studies (4.27 %). Studies commonly had high heterogeneity and ROB ratings.

CONCLUSION: This review is the first to evaluate the utility of diagnostic ML for CMDs, assess their ROB, and evaluate predictor types. CMDs were able to be predicted, however studies had high ROB and lifestyle data were underutilised, precluding full identification of a robust predictor set.

PMID:39667056 | DOI:10.1016/j.compbiomed.2024.109521

Brief Bioinform. 2024 Nov 22;26(1):bbae643. doi: 10.1093/bib/bbae643.

ABSTRACT

Few-shot learning is a crucial approach for macromolecule classification of the cryo-electron tomography (Cryo-ET) subvolumes, enabling rapid adaptation to novel tasks with a small support set of labeled data. However, existing few-shot classification methods for macromolecules in Cryo-ET consider only marginal distributions and overlook joint distributions, failing to capture feature dependencies fully. To address this issue, we propose a method for macromolecular few-shot classification using deep Brownian Distance Covariance (BDC). Our method models the joint distribution within a transfer learning framework, enhancing the modeling capabilities. We insert the BDC module after the feature extractor and only train the feature extractor during the training phase. Then, we enhance the model's generalization capability with self-distillation techniques. In the adaptation phase, we fine-tune the classifier with minimal labeled data. We conduct experiments on publicly available SHREC datasets and a small-scale synthetic dataset to evaluate our method. Results show that our method improves the classification capabilities by introducing the joint distribution.

PMID:39668336 | DOI:10.1093/bib/bbae643

J Imaging Inform Med. 2024 Dec 12. doi: 10.1007/s10278-024-01348-8. Online ahead of print.

ABSTRACT

Circulating genetically abnormal cells (CACs) serve as crucial biomarkers for lung cancer diagnosis. Detecting CACs holds great value for early diagnosis and screening of lung cancer. To aid the identification of CACs, we have incorporated deep learning algorithms into our CACs detection system, specifically developing algorithms for cell segmentation and signal point detection. However, it is noteworthy that deep learning algorithms require extensive data labeling. Consequently, this study introduces a semi-supervised learning algorithm for CACs detection. For the cell segmentation task, a combination of self-training and Mean Teacher method was adopted in the semi-supervised training cell segmentation task. Furthermore, an Adaptive Mean Teacher approach was developed based on the Mean Teacher to enhance the effectiveness of semi-supervised cell segmentation. Regarding the signal point detection task, an end-to-end semi-supervised signal point detection algorithm was developed using the Adaptive Mean Teacher as the paradigm, and a Domain-Knowledge Pseudo Label was developed to improve the quality of pseudo-labeling and further enhance signal point detection. By incorporating semi-supervised training in both sub-tasks, the reliance on labeled data is reduced, thereby improving the performance of CACs detection. Our proposed semi-supervised method has achieved good results in cell segmentation tasks, signal point detection tasks, and the final CACs detection task. In the final CACs detection task, with 2%, 5%, and 10% of labeled data, our proposed semi-supervised method achieved 27.225%, 23.818%, and 4.513%, respectively. Experimental results demonstrated that the proposed method is effective.

PMID:39668308 | DOI:10.1007/s10278-024-01348-8

Jpn J Radiol. 2024 Dec 13. doi: 10.1007/s11604-024-01718-w. Online ahead of print.

ABSTRACT

PURPOSE: This study aimed to develop a large multimodality model (LMM) that can detect breast and esophageal carcinomas on chest contrast-enhanced CT.

MATERIALS AND METHODS: In this retrospective study, CT images of 401 (age, 62.9 ± 12.9 years; 169 males), 51 (age, 65.5 ± 11.6 years; 23 males), and 120 (age, 64.6 ± 14.2 years; 60 males) patients were used in the training, validation, and test phases. The numbers of CT images with breast carcinoma, esophageal carcinoma, and no lesion were 927, 2180, and 2087; 80, 233, and 270; and 184, 246, and 6919 for the training, validation, and test datasets, respectively. The LMM was fine-tuned using CT images as input and text data ("suspicious of breast carcinoma"/ "suspicious of esophageal carcinoma"/ "no lesion") as reference data on a desktop computer equipped with a single graphic processing unit. Because of the random nature of the training process, supervised learning was performed 10 times. The performance of the best performing model on the validation dataset was further tested using the time-independent test dataset. The detection performance was evaluated by calculating the area under the receiver operating characteristic curve (AUC).

RESULTS: The sensitivities of the fine-tuned LMM for detecting breast and esophageal carcinomas in the test dataset were 0.929 and 0.951, respectively. The diagnostic performance of the fine-tuned LMM for detecting breast and esophageal carcinomas was high, with AUCs of 0.890 (95%CI 0.871-0.909) and 0.880 (95%CI 0.865-0.894), respectively.

CONCLUSIONS: The fine-tuned LMM could detect both breast and esophageal carcinomas on chest contrast-enhanced CT with high diagnostic performance. Usefulness of large multimodality models in chest cancer imaging has not been assessed so far. The fine-tuned large multimodality model could detect breast and esophageal carcinomas with high diagnostic performance (area under the receiver operating characteristic curve of 0.890 and 0.880, respectively).

PMID:39668277 | DOI:10.1007/s11604-024-01718-w

Commun Biol. 2024 Dec 12;7(1):1636. doi: 10.1038/s42003-024-07322-y.

ABSTRACT

Eye tracking has emerged as a key method for understanding how animals process visual information, identifying crucial elements of perception and attention. Traditional fish eye tracking often alters animal behavior due to invasive techniques, while non-invasive methods are limited to either 2D tracking or restricting animals after training. Our study introduces a non-invasive technique for tracking and reconstructing the retinal view of free-swimming fish in a large 3D arena without behavioral training. Using 3D fish bodymeshes reconstructed by DeepShapeKit, our method integrates multiple camera angles, deep learning for 3D fish posture reconstruction, perspective transformation, and eye tracking. We evaluated our approach using data from two fish swimming in a flow tank, captured from two perpendicular viewpoints, and validated its accuracy using human-labeled and synthesized ground truth data. Our analysis of eye movements and retinal view reconstruction within leader-follower schooling behavior reveals that fish exhibit negatively synchronised eye movements and focus on neighbors centered in the retinal view. These findings are consistent with previous studies on schooling fish, providing a further, indirect, validation of our method. Our approach offers new insights into animal attention in naturalistic settings and potentially has broader implications for studying collective behavior and advancing swarm robotics.

PMID:39668195 | DOI:10.1038/s42003-024-07322-y

Cancer Sci. 2024 Dec 12. doi: 10.1111/cas.16334. Online ahead of print.

ABSTRACT

Personalized radiotherapy based on the intrinsic sensitivity of individual tumors is anticipated, however, it has yet to be realized. To explore cancer radiosensitivity, analysis in combination with omics data is important. The Cancer Cell Line Encyclopedia (CCLE) provides multi-layer omics data for hundreds of cancer cell lines. However, the radiosensitivity counterpart is lacking. To address this issue, we aimed to establish a database of radiosensitivity, as assessed by the gold standard clonogenic assays, for the CCLE cell lines by collecting data from the literature. A deep learning-based screen of 33,284 papers identified 926 relevant studies, from which SF2 (survival fraction after 2 Gy irradiation) data were extracted. The median SF2 (mSF2) was calculated for each cell line, generating an mSF2 database comprising 285 cell lines from 28 cancer types. The mSF2 showed a normal distribution among higher and lower cancer-type hierarchies, demonstrating a large variation across and within cancer types. In selected cell lines, mSF2 correlated significantly with the single-institution SF2 obtained using standardized experimental protocols or with integral survival, a radiosensitivity index that correlates with clonogenic survival. Notably, the mSF2 for blood cancer cell lines was significantly lower than that for solid cancer cell lines, which is in line with the empirical knowledge that blood cancers are radiosensitive. Furthermore, the CCLE-derived protein levels of NFE2L2 and SQSTM1, which are involved in antioxidant damage responses that confer radioresistance, correlated significantly with mSF2. These results suggest the robustness and potential utility of the mSF2 database, linked to multi-layer omics data, for exploring cancer radiosensitivity.

PMID:39668120 | DOI:10.1111/cas.16334

Brachytherapy. 2024 Dec 11:S1538-4721(24)00457-4. doi: 10.1016/j.brachy.2024.11.007. Online ahead of print.

ABSTRACT

BACKGROUND: Poor needle placement in prostate high-dose-rate brachytherapy (HDR-BT) results in sub-optimal dosimetry and mentally predicting these effects during HDR-BT is difficult, creating a barrier to widespread availability of high-quality prostate HDR-BT.

PURPOSE: To provide earlier feedback on needle implantation quality, we trained machine learning models to predict 2D dosimetry for prostate HDR-BT on axial TRUS images.

METHODS AND MATERIALS: Clinical treatment plans from 248 prostate HDR-BT patients were retrospectively collected and randomly split 80/20 for training/testing. Fifteen U-Net models were implemented to predict the 90%, 100%, 120%, 150%, and 200% isodose levels in the prostate base, midgland, and apex. Predicted isodose lines were compared to delivered dose using Dice similarity coefficient (DSC), precision, recall, average symmetric surface distance, area percent difference, and 95th percentile Hausdorff distance. To benchmark performance, 10 cases were retrospectively replanned and compared against the clinical plans using the same metrics.

RESULTS: Models predicting 90% and 100% isodose lines at midgland performed best, with median DSC of 0.97 and 0.96, respectively. Performance declined as isodose level increased, with median DSC of 0.90, 0.79, and 0.65 in the 120%, 150%, and 200% models. In the base, median DSC was 0.94 for 90% and decreased to 0.64 for 200%. In the apex, median DSC was 0.93 for 90% and decreased to 0.63 for 200%. Median prediction time was 25 ms.

CONCLUSION: U-Net models accurately predicted HDR-BT isodose lines on 2D TRUS images sufficiently quickly for real-time use. Incorporating auto-segmentation algorithms will allow intra-operative feedback on needle implantation quality.

PMID:39668102 | DOI:10.1016/j.brachy.2024.11.007

Diagn Interv Imaging. 2024 Dec 11:S2211-5684(24)00256-0. doi: 10.1016/j.diii.2024.11.001. Online ahead of print.

ABSTRACT

Magnetic resonance imaging (MRI) techniques that enhance the visualization of mineralized tissues (hereafter referred to as MT-MRI) are increasingly being incorporated into clinical practice, particularly in musculoskeletal imaging. These techniques aim to mimic the contrast provided by computed tomography (CT), while taking advantage of MRI's superior soft tissue contrast and lack of ionizing radiation. However, the variety of MT-MRI techniques, including three-dimensional gradient-echo, ultra-short and zero-echo time, susceptibility-weighted imaging, and artificial intelligence-generated synthetic CT, each offer different technical characteristics, advantages, and limitations. Understanding these differences is critical to optimizing clinical application. This review provides a comprehensive overview of the most commonly used MT-MRI techniques, categorizing them based on their technical principles and clinical utility. The advantages and disadvantages of each approach, including their performance in bone morphology assessment, fracture detection, arthropathy-related findings, and soft tissue calcification evaluation are discussed. Additionally, technical limitations and artifacts that may affect image quality and diagnostic accuracy, such as susceptibility effects, signal-to-noise ratio issues, and motion artifacts are addressed. Despite promising developments, MT-MRI remains inferior to conventional CT for evaluating subtle bone abnormalities and soft tissue calcification due to spatial resolution limitations. However, advances in deep learning and hardware innovations, such as artificial intelligence-generated synthetic CT and ultrahigh-field MRI, may bridge this gap in the future.

PMID:39667997 | DOI:10.1016/j.diii.2024.11.001

Magn Reson Imaging. 2024 Dec 10:110308. doi: 10.1016/j.mri.2024.110308. Online ahead of print.

ABSTRACT

PURPOSES: This study aimed to assess the effectiveness of Super-Resolution Deep Learning Reconstruction (SR-DLR) -a deep learning-based technique that enhances image resolution and quality during MRI reconstruction- in improving the image quality of thin-slice 3D T2-weighted imaging (T2WI) and Prostate Imaging-Reporting and Data System (PI-RADS) assessment in prostate Magnetic Resonance Imaging (MRI).

METHODS: This retrospective study included 33 patients who underwent prostate MRI with SR-DLR between November 2022 and April 2023. Thin-slice 3D-T2WI of the prostate was obtained and reconstructed with and without SR-DLR (matrix: 720 × 720 and 240 × 240, respectively). We calculated the contrast and contrast-to-noise ratio (CNR) between the internal and external glands of the prostate, as well as the slope of pelvic bone and adipose tissue. Two radiologists evaluated qualitative image quality and assessed PI-RADS scores of each reconstruction.

RESULTS: The final analysis included 28 male patients (age range: 47-88 years; mean age: 70.8 years). The CNR with SR-DLR was significantly higher than without SR-DLR (1.93 [IQR: 0.79, 3.83] vs. 1.88 [IQR: 0.63, 3.82], p = 0.002). No significant difference in contrast was observed between images with and without SR-DLR (p = 0.864). The slope with SR-DLR was significantly higher than without SR-DLR (0.21 [IQR: 0.15, 0.25] vs. 0.15 [IQR: 0.12, 0.19], p < 0.01). Qualitative scores for contrast, sharpness, artifacts, and overall image quality were significantly higher with SR-DLR than without SR-DLR (p < 0.05 for all). The kappa values for 2D-T2WI and 3D-T2WI increased from 0.694 and 0.640 to 0.870 and 0.827 with SR-DLR for both readers.

CONCLUSIONS: SR-DLR has the potential to improve image quality and the ability to assess PI-RADS scores in thin-slice 3D-T2WI of the prostate without extending MRI acquisition time.

SUMMARY: Super-Resolution Deep Learning Reconstruction (SR-DLR) significantly improved image quality of thin-slice 3D T2-weighted imaging (T2WI) without extending the acquisition time. Additionally, the PI-RADS scores from 3D-T2WI with SR-DLR demonstrated higher agreement with those from 2D-T2WI.

PMID:39667642 | DOI:10.1016/j.mri.2024.110308

Int J Radiat Oncol Biol Phys. 2024 Dec 10:S0360-3016(24)03711-8. doi: 10.1016/j.ijrobp.2024.11.104. Online ahead of print.

ABSTRACT

PURPOSE: This study aimed to design and evaluate a prior-knowledge-guided U-Net (PK-UNet) for automatic clinical target volume (CTV) segmentation in postmastectomy radiotherapy for breast cancer.

METHODS AND MATERIALS: A total of 102 computed tomography (CT) scans from breast cancer patients who underwent postmastectomy were retrospectively collected. Of these, 80 scans were used for training with 5-fold cross-validation, and 22 scans for independent testing. The CTV included the chest wall, supraclavicular region, and axillary group III. The proposed PK-UNet method employs a two-stage auto-segmentation process. Initially, the localization network categorizes CT slices based on the anatomical information of the CTV and generates prior knowledge labels. These outputs, along with the CT images, were fed into the final segmentation network. Quantitative evaluation was conducted using the mean Dice similarity coefficient (DSC), 95% Hausdorff distance (95HD), average surface distance (ASD), surface Dice similarity coefficient (sDSC). A four-level objective scale evaluation was performed by two experienced radiation oncologists in a randomized, double-blind manner.

RESULTS: Quantitative evaluations revealed that PK-UNet significantly outperformed state-of-the-art (SOTA) segmentation methods (P < 0.01), with a mean DSC of 0.90 ± 0.02 and a 95HD of 2.82 ± 1.29 mm. The mean ASD of PK-UNet was 0.91 ± 0.22 mm and the sDSC was 0.84 ± 0.07, significantly surpassing the performance of AdwU-Net (P < 0.01) and showing comparable results to other models. Clinical evaluation confirmed the efficacy of PK-UNet, with 81.8% of the predicted contours being acceptable for clinical application. The advantages of the auto-segmentation capability of PK-UNet were most evident in the superior and inferior slices and slices with discontinuities at the junctions of different subregions. The average manual correction time was reduced to 1.02 min, compared to 18.20 min for manual contouring leading to a 94.4% reduction in working time.

CONCLUSION: This study introduced the pioneering integration of prior medical knowledge into a deep learning framework for postmastectomy radiotherapy. This strategy addresses the challenges of CTV segmentation in postmastectomy radiotherapy and improves clinical workflow efficiency.

PMID:39667584 | DOI:10.1016/j.ijrobp.2024.11.104

J Dent. 2024 Dec 10:105526. doi: 10.1016/j.jdent.2024.105526. Online ahead of print.

ABSTRACT

OBJECTIVES: Periapical lesions are not always evident on radiographic scans. Sometimes, asymptomatic or initial periapical lesions on cone-beam computed tomography (CBCT) could be missed by inexperienced dentists, especially when the scan has a large field of view and is not for endodontic treatment purposes. Previously, numerous algorithms have been introduced to assist radiographic assessment and diagnosis in the field of endodontics. This study aims to investigate the efficacy of CBCT-SAM, a new artificial intelligence (AI) model, in identifying periapical lesions on CBCT.

METHODS: Model training and validation in this study was performed using 185 CBCT scans with confirmed periapical lesions. Manual segmentation labels were prepared by a trained operator and validated by a maxillofacial radiologist. The diagnostic and segmentation performances of four AI models were evaluated and compared: CBCT-SAM, CBCT-SAM without progressive prediction refinement module(PPR), and two previously developed models: Modified U-Net and PAL-Net. Accuracy was used to evaluated the diagnostic performance of the models, and accuracy, sensitivity, specificity, precision and Dice Similarity Coefficient (DSC) were used to evaluate the models' segmentation performance.

RESULTS: CBCT-SAM achieved an average diagnostic accuracy of 98.92% ± 010.37% and an average segmentation accuracy of 99.65% ± 0.66%. The average sensitivity, specificity, precision and DSC were 72.36 ± 21.61%, 99.87% ± 0.11%, 0.73 ± 0.21 and 0.70 ± 0.19. CBCT-SAM and PAL-Net performed significantly better than Modified U-Net in segmentation accuracy (p= 0.023, p = 0.041), sensitivity (p = 0.000, p = 0.002), and DSC (p=0.001, p=0.004). There is no significant difference between CBCT-SAM, CBCT-SAM without PPR and PAL-Net. However, with PPR incorporated into the model, CBCT-SAM slightly surpassed PAL-Net in the diagnostic and segmentation tasks.

CONCLUSIONS: CBCT-SAM is capable of providing expert-level assistance in the identification of periapical lesions on CBCT.

CLINICAL SIGNIFICANCE: The application of artificial intelligence could increase dentists' chairside diagnostic accuracy and efficiency. By assisting radiographic assessment, such as periapical lesions on CBCT, it help reduce the chance of missed diagnosis by human errors and facilitates early detection and treatment of dental pathologies at the early stage.

PMID:39667487 | DOI:10.1016/j.jdent.2024.105526

J Acoust Soc Am. 2024 Dec 1;156(6):3958-3969. doi: 10.1121/10.0034599.

ABSTRACT

Hearing impairment is often characterized by poor speech-in-noise recognition. State-of-the-art laboratory-based noise-reduction technology can eliminate background sounds from a corrupted speech signal and improve intelligibility, but it can also hinder environmental sound recognition (ESR), which is essential for personal independence and safety. This paper presents a time-frequency mask, the ideal compressed mask (ICM), that aims to provide listeners with improved speech intelligibility without substantially reducing ESR. This is accomplished by limiting the maximum attenuation that the mask performs. Speech intelligibility and ESR for hearing-impaired and normal-hearing listeners were measured using stimuli that had been processed by ICMs with various levels of maximum attenuation. This processing resulted in significantly improved intelligibility while retaining high ESR performance for both types of listeners. It was also found that the same level of maximum attenuation provided the optimal balance of intelligibility and ESR for both listener types. It is argued that future deep-learning-based noise reduction algorithms may provide better outcomes by balancing the levels of the target speech and the background environmental sounds, rather than eliminating all signals except for the target speech. The ICM provides one such simple solution for frequency-domain models.

PMID:39666959 | DOI:10.1121/10.0034599

JCO Glob Oncol. 2024 Dec;10:e2400146. doi: 10.1200/GO.24.00146. Epub 2024 Dec 12.

ABSTRACT

PURPOSE: The burden of cervical cancer in India is enormous, with more than 60,000 deaths being reported in 2020. The key intervention in the WHO's global strategy for the elimination of cervical cancer is to aim for the treatment and care of 90% of women diagnosed with cervical lesions. The current screen-and-treat approach as an option for resource-limited health care systems where screening of the cervix with visual inspection with acetic acid application (VIA) is followed by immediate ablative treatment by nurses in the case of a positive test. This approach often results in overtreatment, owing to the subjective nature of the test. Unnecessary treatments can be diminished with the use of emerging computer-assisted visual evaluation technology, using artificial intelligence (AI) tool to triage VIA-positive women. The aim of this study was (1) to develop a VIA-AI tool using cervical images to identify and categorize the VIA-screen-positive areas for eligibility and suitability for ablative treatment, and (2) to understand the efficacy of the VIA-AI tool in guiding the nurses to decide on treatment eligibility in the screen-and-treat cervical screening program.

METHODS: This was an exploratory, interventional study. The VIA-AI tool was developed using deep-learning AI from the image bank collected in our previously conducted screening programs. This VIA-AI tool was then pilot-tested in an ongoing nurse-led VIA screening program.

RESULTS: A comparative assessment of the cervical features performed in all women using the VIA-AI tool showed clinical accuracy of 76%. The perceived challenge rate for false positives was 20%.

CONCLUSION: This novel cervical image-based VIA-AI algorithm showed promising results in real-life settings, and could help minimize overtreatment in single-visit VIA screening and treatment programs in resource-constrained situations.

PMID:39666915 | DOI:10.1200/GO.24.00146

Plant J. 2024 Dec 12. doi: 10.1111/tpj.17190. Online ahead of print.

ABSTRACT

Due to its excellent performance in processing large amounts of data and capturing complex non-linear relationships, deep learning has been widely applied in many fields of plant biology. Here we first review the application of deep learning in analyzing genome sequences to predict gene expression, chromatin interactions, and epigenetic features (open chromatin, transcription factor binding sites, and methylation sites) in plants. Then, current motif mining and functional component design and synthesis based on generative adversarial networks, large models, and attention mechanisms are elaborated in detail. The progress of protein structure and function prediction, genomic prediction, and large model applications based on deep learning is also discussed. Finally, this work provides prospects for the future development of deep learning in plants with regard to multiple omics data, algorithm optimization, large language models, sequence design, and intelligent breeding.

PMID:39666835 | DOI:10.1111/tpj.17190

PLoS One. 2024 Dec 12;19(12):e0315646. doi: 10.1371/journal.pone.0315646. eCollection 2024.

ABSTRACT

A general-purpose method of emphasizing abnormal lesions in chest radiographs, named EGGPALE (Extrapolative, Generative and General-Purpose Abnormal Lesion Emphasizer), is presented. The proposed EGGPALE method is composed of a flow-based generative model and L-infinity-distance-based extrapolation in a latent space. The flow-based model is trained using only normal chest radiographs, and an invertible mapping function from the image space to the latent space is determined. In the latent space, a given unseen image is extrapolated so that the image point moves away from the normal chest X-ray hyperplane. Finally, the moved point is mapped back to the image space and the corresponding emphasized image is created. The proposed method was evaluated by an image interpretation experiment with nine radiologists and 1,000 chest radiographs, of which positive suspected lung cancer cases and negative cases were validated by computed tomography examinations. The sensitivity of EGGPALE-processed images showed +0.0559 average improvement compared with that of the original images, with -0.0192 deterioration of average specificity. The area under the receiver operating characteristic curve of the ensemble of nine radiologists showed a statistically significant improvement. From these results, the feasibility of EGGPALE for enhancing abnormal lesions was validated. Our code is available at https://github.com/utrad-ical/Eggpale.

PMID:39666722 | DOI:10.1371/journal.pone.0315646

PLoS One. 2024 Dec 12;19(12):e0313871. doi: 10.1371/journal.pone.0313871. eCollection 2024.

ABSTRACT

Accurate medium- to long-term runoff forecasting is of great significance for flood control, drought mitigation, comprehensive water resource management, and ecological restoration. However, runoff formation is a complex process influenced by various natural and anthropogenic factors, resulting in nonlinearity, nonstationarity, and long prediction periods, which complicate forecasting efforts. Traditional statistical models, which primarily focus on individual runoff sequences, struggle to integrate multi-source data, limiting their predictive accuracy. This narrow approach overlooks the multifaceted variables influencing runoff, resulting in incomplete and less reliable predictions. To address these challenges, we selected and integrated Random Forest (RF), Support Vector Regression (SVR), and Multilayer Perceptron Regression (MLPR) to develop two coupled intelligent prediction models-RF-SVR and RF-MLPR-due to their complementary strengths. RF effectively removes collinear and redundant information from high-dimensional data, while SVR and MLPR handle nonlinearity and nonstationarity, offering enhanced generalization capabilities. Specifically, MLPR, with its deep learning structure, can extract more complex latent information from data, making it particularly suitable for long-term forecasting. The proposed models were tested in the Yalong River Basin (YLRB), where accurate medium- to long-term runoff forecasts are essential for ecological management, flood control, and optimal water resource allocation. The results demonstrate the following: (1) The impact of atmospheric circulation indices on YLRB runoff exhibits a one-month lag, providing crucial insights for water resource scheduling and flood prevention. (2) The coupled models effectively eliminate collinearity and redundant variables, improving prediction accuracy across all forecast periods. (3) Compared to single baseline models, the coupled models demonstrated significant performance improvements across six evaluation metrics. For instance, the RF-MLPR model achieved a 3.7%-6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R2 value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. For example, in terms of the R2 metric, the RF-MLPR model's performance at the Jinping hydrological station improved by 6.5% compared to the RF-SVR model. Similarly, at the Lianghekou station, for a one-month lead prediction period, the RF-MLPR model's R2 value was 7.9% higher than that of the RF-SVR model. The significance of this research lies not only in its contribution to improving hydrological prediction accuracy but also in its broader applicability. The proposed coupled prediction models provide practical tools for water resource management, flood control planning, and drought mitigation in regions with similar hydrological characteristics. Furthermore, the framework's flexibility in parameterization and its ability to integrate multi-source data offer valuable insights for interdisciplinary applications across environmental sciences, meteorology, and climate prediction, making it a globally relevant contribution to addressing water management challenges under changing climatic conditions.

PMID:39666703 | DOI:10.1371/journal.pone.0313871