Deep convolutional neural networks for tongue squamous cell carcinoma classification using Raman spectroscopy

Assessment of Surgical Margin of Tongue Squamous Cell Carcinoma via Raman Mapping

OBJECTIVES

This study introduces a novel classification approach that combines convolutional neural network (CNN) and Raman mapping to differentiate between tongue squamous cell carcinoma (TSCC) and non-tumorous tissue, as well as to identify different histological grades of TSCC.

MATERIALS AND METHODS

In this study, 240 Raman mappings data from 30 tissue samples were collected from 15 patients who had undergone surgical resection for TSCC. A total of 18,000 sub-mappings extracted from Raman mappings were then used to train and test a CNN model, which extracted feature representations that were subsequently processed through a fully connected network to perform classification tasks based on the Raman mapping data.

RESULTS

The experimental results indicated that the proposed method achieved competitive classification accuracy above 83%. To further validate the effectiveness of the Raman mapping, its performance was compared with Raman spectroscopy, demonstrating a competitive accuracy rate.

CONCLUSIONS

The promising outcomes from this application of CNN in Raman mapping suggest that this technique could be a reliable method for intraoperative assessment of surgical margins, potentially leading to shorter detection times.

Advances in Raman spectroscopy for characterising oral cancer and oral potentially malignant disorders

Oral cancer survival rates have seen little improvement over the past few decades. This is mainly due to late detection and a lack of reliable markers to predict disease progression in oral potentially malignant disorders (OPMDs). There is a need for highly specific and sensitive screening tools to enable early detection of malignant transformation. Biochemical alterations to tissues occur as an early response to pathological processes; manifesting as modifications to molecular structure, concentration or conformation. Raman spectroscopy is a powerful analytical technique that can probe these biochemical changes and can be exploited for the generation of novel disease-specific biomarkers. Therefore, Raman spectroscopy has the potential as an adjunct tool that can assist in the early diagnosis of oral cancer and the detection of disease progression in OPMDs. This review describes the use of Raman spectroscopy for the diagnosis of oral cancer and OPMDs based on ex vivo and liquid biopsies as well as in vivo applications that show the potential of this powerful tool to progress from benchtop to chairside.

Accuracy of machine learning in the diagnosis of odontogenic cysts and tumors: a systematic review and meta-analysis

BACKGROUND

The recent impact of artificial intelligence in diagnostic services has been enormous. Machine learning tools offer an innovative alternative to diagnose cysts and tumors radiographically that pose certain challenges due to the near similar presentation, anatomical variations, and superimposition. It is crucial that the performance of these models is evaluated for their clinical applicability in diagnosing cysts and tumors.

METHODS

A comprehensive literature search was carried out on eminent databases for published studies between January 2015 and December 2022. Studies utilizing machine learning models in the diagnosis of odontogenic cysts or tumors using Orthopantomograms (OPG) or Cone Beam Computed Tomographic images (CBCT) were included. QUADAS-2 tool was used for the assessment of the risk of bias and applicability concerns. Meta-analysis was performed for studies reporting sufficient performance metrics, separately for OPG and CBCT.

RESULTS

16 studies were included for qualitative synthesis including a total of 10,872 odontogenic cysts and tumors. The sensitivity and specificity of machine learning in diagnosing cysts and tumors through OPG were 0.83 (95% CI 0.81-0.85) and 0.82 (95% CI 0.81-0.83) respectively. Studies utilizing CBCT noted a sensitivity of 0.88 (95% CI 0.87-0.88) and specificity of 0.88 (95% CI 0.87-0.89). Highest classification accuracy was 100%, noted for Support Vector Machine classifier.

CONCLUSION

The results from the present review favoured machine learning models to be used as a clinical adjunct in the radiographic diagnosis of odontogenic cysts and tumors, provided they undergo robust training with a huge dataset. However, the arduous process, investment, and certain ethical concerns associated with the total dependence on technology must be taken into account. Standardized reporting of outcomes for diagnostic studies utilizing machine learning methods is recommended to ensure homogeneity in assessment criteria, facilitate comparison between different studies, and promote transparency in research findings.

Application of deep learning radiomics in oral squamous cell carcinoma-Extracting more information from medical images using advanced feature analysis

OBJECTIVE

To conduct a systematic review with meta-analyses to assess the recent scientific literature addressing the application of deep learning radiomics in oral squamous cell carcinoma (OSCC).

MATERIALS AND METHODS

Electronic and manual literature retrieval was performed using PubMed, Web of Science, EMbase, Ovid-MEDLINE, and IEEE databases from 2012 to 2023. The ROBINS-I tool was used for quality evaluation; random-effects model was used; and results were reported according to the PRISMA statement.

RESULTS

A total of 26 studies involving 64,731 medical images were included in quantitative synthesis. The meta-analysis showed that, the pooled sensitivity and specificity were 0.88 (95 %CI: 0.87∼0.88) and 0.80 (95 %CI: 0.80∼0.81), respectively. Deeks' asymmetry test revealed there existed slight publication bias (P = 0.03).

CONCLUSIONS

The advances in the application of radiomics combined with learning algorithm in OSCC were reviewed, including diagnosis and differential diagnosis of OSCC, efficacy assessment and prognosis prediction. The demerits of deep learning radiomics at the current stage and its future development direction aimed at medical imaging diagnosis were also summarized and analyzed at the end of the article.

Deep neural network: As the novel pipelines in multiple preprocessing for Raman spectroscopy

Raman spectroscopy is a kind of vibrational method that can rapidly and non-invasively gives chemical structural information with the Raman spectrometer. Despite its technical advantages, in practical application scenarios, Raman spectroscopy often suffers from interference, such as noises and baseline drifts, resulting in the inability to acquire high-quality Raman spectroscopy signals, which brings challenges to subsequent spectral analysis. The commonly applied spectral preprocessing methods, such as Savitzky-Golay smooth and wavelet transform, can only perform corresponding single-item processing and require manual intervention to carry out a series of tedious trial parameters. Especially, each scheme can only be used for a specific data set. In recent years, the development of deep neural networks has provided new solutions for intelligent preprocessing of spectral data. In this paper, we first creatively started from the basic mechanism of spectral signal generation and constructed a mathematical model of the Raman spectral signal. By counting the noise parameters of the real system, we generated a simulation dataset close to the output of the real system, which alleviated the dependence on data during deep learning training. Due to the powerful nonlinear fitting ability of the neural network, fully connected network model is constructed to complete the baseline estimation task simply and quickly. Then building the Unet model can effectively achieve spectral denoising, and combining it with baseline estimation can realize intelligent joint processing. Through the simulation dataset experiment, it is proved that compared with the classic method, the method proposed in this paper has obvious advantages, which can effectively improve the signal quality and further ensure the accuracy of the peak intensity. At the same time, when the proposed method is applied to the actual system, it also achieves excellent performance compared with the common method, which indirectly indicates the effectiveness of the Raman signal simulation model. The research presented in this paper offers a variety of efficient pipelines for the intelligent processing of Raman spectroscopy, which can adapt to the requirements of different tasks while providing a new idea for enhancing the quality of Raman spectroscopy signals.

Fast Raman imaging through the combination of context-aware matrix completion and low spectral resolution

Raman hyperspectral imaging is an effective method for label-free imaging with chemical specificity, yet the weak signals and correspondingly long integration times have hindered its wide adoption as a routine analytical method. Recently, low resolution Raman imaging has been proposed to improve the spectral signal-to-noise ratio, which significantly improves the speed of Raman imaging. In this paper, low resolution Raman spectroscopy is combined with "context-aware" matrix completion, where regions of the sample that are not of interest are skipped, and the regions that are measured are under-sampled, then reconstructed with a low-rank constraint. Both simulations and experiment show that low-resolution Raman boosts the speed and image quality of the computationally-reconstructed Raman images, allowing deeper sub-sampling, reduced exposure time, and an overall >10-fold improvement in imaging speed, without sacrificing chemical specificity or spatial image quality. As the method utilizes traditional point-scan imaging, it retains full confocality and is "backwards-compatible" with pre-existing traditional Raman instruments, broadening the potential scope of Raman imaging applications.

Early Predicting Tribocorrosion Rate of Dental Implant Titanium Materials Using Random Forest Machine Learning Models

Early detection and prediction of bio-tribocorrosion can avert unexpected damage that may lead to secondary revision surgery and associated risks of implantable devices. Therefore, this study sought to develop a state-of-the-art prediction technique leveraging machine learning(ML) models to classify and predict the possibility of mechanical degradation in dental implant materials. Key features considered in the study involving pure titanium and titanium-zirconium (zirconium = 5, 10, and 15 in wt%) alloys include corrosion potential, acoustic emission(AE) absolute energy, hardness, and weight-loss estimates. ML prototype models deployed confirms its suitability in tribocorrosion prediction with an accuracy above 90%. Proposed system can evolve as a continuous structural-health monitoring as well as a reliable predictive modeling technique for dental implant monitoring.

Dense Convolutional Neural Network for Identification of Raman Spectra

The rapid development of cloud computing and deep learning makes the intelligent modes of applications widespread in various fields. The identification of Raman spectra can be realized in the cloud, due to its powerful computing, abundant spectral databases and advanced algorithms. Thus, it can reduce the dependence on the performance of the terminal instruments. However, the complexity of the detection environment can cause great interferences, which might significantly decrease the identification accuracies of algorithms. In this paper, a deep learning algorithm based on the Dense network has been proposed to satisfy the realization of this vision. The proposed Dense convolutional neural network has a very deep structure of over 40 layers and plenty of parameters to adjust the weight of different wavebands. In the kernel Dense blocks part of the network, it has a feed-forward fashion of connection for each layer to every other layer. It can alleviate the gradient vanishing or explosion problems, strengthen feature propagations, encourage feature reuses and enhance training efficiency. The network's special architecture mitigates noise interferences and ensures precise identification. The Dense network shows more accuracy and robustness compared to other CNN-based algorithms. We set up a database of 1600 Raman spectra consisting of 32 different types of liquid chemicals. They are detected using different postures as examples of interfered Raman spectra. In the 50 repeated training and testing sets, the Dense network can achieve a weighted accuracy of 99.99%. We have also tested the RRUFF database and the Dense network has a good performance. The proposed approach advances cloud-enabled Raman spectra identification, offering improved accuracy and adaptability for diverse identification tasks.

Artificial Intelligence-Aided Optical Imaging for Cancer Theranostics

The use of artificial intelligence (AI) to assist biomedical imaging have demonstrated its high accuracy and high efficiency in medical decision-making for individualized cancer medicine. In particular, optical imaging methods are able to visualize both the structural and functional information of tumors tissues with high contrast, low cost, and noninvasive property. However, no systematic work has been performed to inspect the recent advances on AI-aided optical imaging for cancer theranostics. In this review, we demonstrated how AI can guide optical imaging methods to improve the accuracy on tumor detection, automated analysis and prediction of its histopathological section, its monitoring during treatment, and its prognosis by using computer vision, deep learning and natural language processing. By contrast, the optical imaging techniques involved mainly consisted of various tomography and microscopy imaging methods such as optical endoscopy imaging, optical coherence tomography, photoacoustic imaging, diffuse optical tomography, optical microscopy imaging, Raman imaging, and fluorescent imaging. Meanwhile, existing problems, possible challenges and future prospects for AI-aided optical imaging protocol for cancer theranostics were also discussed. It is expected that the present work can open a new avenue for precision oncology by using AI and optical imaging tools.

Determination of esophageal squamous cell carcinoma and gastric adenocarcinoma on raw tissue using Raman spectroscopy

BACKGROUND

Cancer detection is a global research focus, and novel, rapid, and label-free techniques are being developed for routine clinical practice. This has led to the development of new tools and techniques from the bench side to routine clinical practice. In this study, we present a method that uses Raman spectroscopy (RS) to detect cancer in unstained formalin-fixed, resected specimens of the esophagus and stomach. Our method can record a clear Raman-scattered light spectrum in these specimens, confirming that the Raman-scattered light spectrum changes because of the histological differences in the mucosal tissue.

AIM

To evaluate the use of Raman-scattered light spectrum for detecting endoscop-ically resected specimens of esophageal squamous cell carcinoma (SCC) and gastric adenocarcinoma (AC).

METHODS

We created a Raman device that is suitable for observing living tissues, and attempted to acquire Raman-scattered light spectra in endoscopically resected specimens of six esophageal tissues and 12 gastric tissues. We evaluated formalin-fixed tissues using this technique and captured shifts at multiple locations based on feasibility, ranging from six to 19 locations 200 microns apart in the vertical and horizontal directions. Furthermore, a correlation between the obtained Raman scattered light spectra and histopathological diagnosis was performed.

RESULTS

We successfully obtained Raman scattered light spectra from all six esophageal and 12 gastric specimens. After data capture, the tissue specimens were sent for histopathological analysis for further processing because RS is a label-free methodology that does not cause tissue destruction or alterations. Based on data analysis of molecular-level substrates, we established cut-off values for the diagnosis of esophageal SCC and gastric AC. By analyzing specific Raman shifts, we developed an algorithm to identify the range of esophageal SCC and gastric AC with an accuracy close to that of histopathological diagnoses.

CONCLUSION

Our technique provides qualitative information for real-time morphological diagnosis. However, further in vivo evaluations require an excitation light source with low human toxicity and large amounts of data for validation.

A double sliding-window method for baseline correction and noise estimation for Raman spectra of microplastics

When measuring microplastics of environmental samples, additives and attachment of biological materials may result in strong fluorescence in Raman spectra, which increases difficulty for imaging, identification, and quantification. Although there are several baseline correction methods available, user intervention is usually needed, which is not feasible for automated processes. In current study, a double sliding-window (DSW) method was proposed to estimate the baseline and standard deviation of noise. Simulated spectra and experimental spectra were used to evaluate the performance in comparison with two popular and widely used methods. Validation with simulated spectra and spectra of environmental samples showed that DSW method can accurately estimate the standard deviation of spectral noise. DSW method also showed better performance than compared methods when handling spectra of low signal-to-noise ratio (SNR) and elevated baselines. Therefore, DSW method is a useful approach for preprocessing Raman spectra of environmental samples and automated processes.

A Current Review of Machine Learning and Deep Learning Models in Oral Cancer Diagnosis: Recent Technologies, Open Challenges, and Future Research Directions

Cancer is a problematic global health issue with an extremely high fatality rate throughout the world. The application of various machine learning techniques that have appeared in the field of cancer diagnosis in recent years has provided meaningful insights into efficient and precise treatment decision-making. Due to rapid advancements in sequencing technologies, the detection of cancer based on gene expression data has improved over the years. Different types of cancer affect different parts of the body in different ways. Cancer that affects the mouth, lip, and upper throat is known as oral cancer, which is the sixth most prevalent form of cancer worldwide. India, Bangladesh, China, the United States, and Pakistan are the top five countries with the highest rates of oral cavity disease and lip cancer. The major causes of oral cancer are excessive use of tobacco and cigarette smoking. Many people's lives can be saved if oral cancer (OC) can be detected early. Early identification and diagnosis could assist doctors in providing better patient care and effective treatment. OC screening may advance with the implementation of artificial intelligence (AI) techniques. AI can provide assistance to the oncology sector by accurately analyzing a large dataset from several imaging modalities. This review deals with the implementation of AI during the early stages of cancer for the proper detection and treatment of OC. Furthermore, performance evaluations of several DL and ML models have been carried out to show that the DL model can overcome the difficult challenges associated with early cancerous lesions in the mouth. For this review, we have followed the rules recommended for the extension of scoping reviews and meta-analyses (PRISMA-ScR). Examining the reference lists for the chosen articles helped us gather more details on the subject. Additionally, we discussed AI's drawbacks and its potential use in research on oral cancer. There are methods for reducing risk factors, such as reducing the use of tobacco and alcohol, as well as immunization against HPV infection to avoid oral cancer, or to lessen the burden of the disease. Additionally, officious methods for preventing oral diseases include training programs for doctors and patients as well as facilitating early diagnosis via screening high-risk populations for the disease.

Raman spectroscopy in chronic heart failure diagnosis based on human skin analysis

This work aims at studying Raman spectroscopy in combination with chemometrics as an alternative fast noninvasive method to detect chronic heart failure (CHF) cases. Optical analysis is focused on the changes in the spectral features associated with the biochemical composition changes of skin tissues. A portable spectroscopy setup with the 785 nm excitation wavelength was used to record skin Raman features. In this in vivo study, 127 patients and 57 healthy volunteers were involved in measuring skin spectral features by Raman spectroscopy. The spectral data were analyzed with a projection on the latent structures and discriminant analysis. 202 skin spectra of patients with CHF and 90 skin spectra of healthy volunteers were classified with 0.888 ROC AUC for the 10-fold cross validated algorithm. To identify CHF cases, the performance of the proposed classifier was verified by means of a new test set that is equal to 0.917 ROC AUC.

Comparing a Fully Automated Cephalometric Tracing Method to a Manual Tracing Method for Orthodontic Diagnosis

Background: This study aims to compare an automated cephalometric analysis based on the latest deep learning method of automatically identifying cephalometric landmarks with a manual tracing method using broadly accepted cephalometric software. Methods: A total of 100 cephalometric X-rays taken using a CS8100SC cephalostat were collected from a private practice. The X-rays were taken in maximum image size (18 × 24 cm lateral image). All cephalometric X-rays were first manually traced using the Dolphin 3D Imaging program version 11.0 and then automatically, using the Artificial Intelligence CS imaging V8 software. The American Board of Orthodontics analysis and the European Board of Orthodontics analysis were used for the cephalometric measurements. This resulted in the identification of 16 cephalometric landmarks, used for 16 angular and 2 linear measurements. Results: All measurements showed great reproducibility with high intra-class reliability (>0.97). The two methods showed great agreement, with an ICC range of 0.70−0.92. Mean values of SNA, SNB, ANB, SN-MP, U1-SN, L1-NB, SNPg, ANPg, SN/ANS-PNS, SN/GoGn, U1/ANS-PNS, L1-APg, U1-NA, and L1-GoGn landmarks had no significant differences between the two methods (p > 0.0027), while the mean values of FMA, L1-MP, ANS-PNS/GoGn, and U1-L1 were statistically significantly different (p < 0.0027). Conclusions: The automatic cephalometric tracing method using CS imaging V8 software is reliable and accurate for all cephalometric measurements.

Deep Learning for Chondrogenic Tumor Classification through Wavelet Transform of Raman Spectra

The grading of cancer tissues is still one of the main challenges for pathologists. The development of enhanced analysis strategies hence becomes crucial to accurately identify and further deal with each individual case. Raman spectroscopy (RS) is a promising tool for the classification of tumor tissues as it allows us to obtain the biochemical maps of the tissues under analysis and to observe their evolution in terms of biomolecules, proteins, lipid structures, DNA, vitamins, and so on. However, its potential could be further improved by providing a classification system which would be able to recognize the sample tumor category by taking as input the raw Raman spectroscopy signal; this could provide more reliable responses in shorter time scales and could reduce or eliminate false-positive or -negative diagnoses. Deep Learning techniques have become ubiquitous in recent years, with models able to perform classification with high accuracy in most diverse fields of research, e.g., natural language processing, computer vision, medical imaging. However, deep models often rely on huge labeled datasets to produce reasonable accuracy, otherwise occurring in overfitting issues when the training data is insufficient. In this paper, we propose a chondrogenic tumor CLAssification through wavelet transform of RAman spectra (CLARA), which is able to classify with high accuracy Raman spectra obtained from bone tissues. CLARA recognizes and grades the tumors in the evaluated dataset with 97% accuracy by exploiting a classification pipeline consisting of the division of the original task in two binary classification steps, where the first is performed on the original RS signals while the latter is accomplished through the use of a hybrid temporal-frequency 2D transform.

Early Diagnosis of Oral Squamous Cell Carcinoma Based on Histopathological Images Using Deep and Hybrid Learning Approaches

Oral squamous cell carcinoma (OSCC) is one of the most common head and neck cancer types, which is ranked the seventh most common cancer. As OSCC is a histological tumor, histopathological images are the gold diagnosis standard. However, such diagnosis takes a long time and high-efficiency human experience due to tumor heterogeneity. Thus, artificial intelligence techniques help doctors and experts to make an accurate diagnosis. This study aimed to achieve satisfactory results for the early diagnosis of OSCC by applying hybrid techniques based on fused features. The first proposed method is based on a hybrid method of CNN models (AlexNet and ResNet-18) and the support vector machine (SVM) algorithm. This method achieved superior results in diagnosing the OSCC data set. The second proposed method is based on the hybrid features extracted by CNN models (AlexNet and ResNet-18) combined with the color, texture, and shape features extracted using the fuzzy color histogram (FCH), discrete wavelet transform (DWT), local binary pattern (LBP), and gray-level co-occurrence matrix (GLCM) algorithms. Because of the high dimensionality of the data set features, the principal component analysis (PCA) algorithm was applied to reduce the dimensionality and send it to the artificial neural network (ANN) algorithm to diagnose it with promising accuracy. All the proposed systems achieved superior results in histological image diagnosis of OSCC, the ANN network based on the hybrid features using AlexNet, DWT, LBP, FCH, and GLCM achieved an accuracy of 99.1%, specificity of 99.61%, sensitivity of 99.5%, precision of 99.71%, and AUC of 99.52%.

Blended fabric with integrated neural network based on attention mechanism qualitative identification method of near infrared spectroscopy

Near Infrared spectroscopy (NIRS) qualitative analysis technology has shown excellent development potential in the field of blend fabrics. However, the qualitative detection method based on the convolutional neural network (CNN) is difficult to accurately extract the feature of the spectral data, which will lead to missing detection or false detection; when using deep learning to build a qualitative detection model, due to interference of the external environment and other factors, the spectral data collected may have outliers, this means that the knowledge generalization on anomalous testing data, which may have a different distribution of that of the training set, is not trivial, which will also lead to missing detection or false detection. To solve the above problems, this paper proposes a novel qualitative detection neural network by analyzing the near infrared spectral data of blend fabrics. Firstly, we remove the convolutional layer and pooling layer of the CNN, making full use of the feature to enhance the feature representation ability of the model. Secondly, adding the L1 norm of the feature coefficients as a penalty term to the loss function to force those features with high redundancy to become weaker. Thirdly, in order to improve the recognition accuracy of the anomalous spectral data and minimize the model uncertainty, an ensemble machine learning approach utilizing 5 neural networks in parallel is used. To show the superiority of our proposed method, the existing methods are used as competitive methods to compare with our method. Our homemade dataset contains 3482 samples of blend fabrics with 9 different compositions. The results show that the Micro-F1-score, Micro-Specificity, Weight-F1-score, and Weight-Specificity of this method respectively 99.71%, 99.96%, 99.73%, and 99.99%, the results further confirm the method has higher analysis accuracy and stability. In addition, the method proposed in this paper can greatly improve the recognition accuracy of the anomalous spectral data. It has important practical value in the qualitative detection of blend fabrics.

Accuracy of artificial intelligence-assisted detection of Oral Squamous Cell Carcinoma: A systematic review and meta-analysis

Oral Squamous Cell Carcinoma (OSCC) is an aggressive tumor with a poor prognosis. Accurate and timely diagnosis is therefore essential for reducing the burden of advanced disease and improving outcomes. In this meta-analysis, we evaluated the accuracy of artificial intelligence (AI)-assisted technologies in detecting OSCC. We included studies that validated any diagnostic modality that used AI to detect OSCC. A search was performed in six databases: PubMed, Embase, Scopus, Cochrane Library, ProQuest, and Web of Science up to 15 Mar 2022. The Quality Assessment Tool for Diagnostic Accuracy Studies was used to evaluate the included studies' quality, while the Split Component Synthesis method was utilized to quantitatively synthesize the pooled diagnostic efficacy estimates. We considered 16 out of the 566 yielded studies, which included twelve different AI models with a total of 6606 samples. The summary sensitivity, summary specificity, positive and negative likelihood ratios as well as the pooled diagnostic odds ratio were 92.0 % (95 % confidence interval [CI] 86.7-95.4 %), 91.9 % (95 % CI 86.5-95.3 %), 11.4 (95 % CI 6.74-19.2), 0.087 (95 % CI 0.051-0.146) and 132 (95 % CI 62.6-277), respectively. Our findings support the capability of AI-assisted systems to detect OSCC with high accuracy, potentially aiding the histopathological examination in early diagnosis, yet more prospective studies are needed to justify their use in the real population.

Emerging Applications of Deep Learning in Bone Tumors: Current Advances and Challenges

Deep learning is a subfield of state-of-the-art artificial intelligence (AI) technology, and multiple deep learning-based AI models have been applied to musculoskeletal diseases. Deep learning has shown the capability to assist clinical diagnosis and prognosis prediction in a spectrum of musculoskeletal disorders, including fracture detection, cartilage and spinal lesions identification, and osteoarthritis severity assessment. Meanwhile, deep learning has also been extensively explored in diverse tumors such as prostate, breast, and lung cancers. Recently, the application of deep learning emerges in bone tumors. A growing number of deep learning models have demonstrated good performance in detection, segmentation, classification, volume calculation, grading, and assessment of tumor necrosis rate in primary and metastatic bone tumors based on both radiological (such as X-ray, CT, MRI, SPECT) and pathological images, implicating a potential for diagnosis assistance and prognosis prediction of deep learning in bone tumors. In this review, we first summarized the workflows of deep learning methods in medical images and the current applications of deep learning-based AI for diagnosis and prognosis prediction in bone tumors. Moreover, the current challenges in the implementation of the deep learning method and future perspectives in this field were extensively discussed.

Machine Learning of Raman Spectroscopy Data for Classifying Cancers: A Review of the Recent Literature

Raman Spectroscopy has long been anticipated to augment clinical decision making, such as classifying oncological samples. Unfortunately, the complexity of Raman data has thus far inhibited their routine use in clinical settings. Traditional machine learning models have been used to help exploit this information, but recent advances in deep learning have the potential to improve the field. However, there are a number of potential pitfalls with both traditional and deep learning models. We conduct a literature review to ascertain the recent machine learning methods used to classify cancers using Raman spectral data. We find that while deep learning models are popular, and ostensibly outperform traditional learning models, there are many methodological considerations which may be leading to an over-estimation of performance; primarily, small sample sizes which compound sub-optimal choices regarding sampling and validation strategies. Amongst several recommendations is a call to collate large benchmark Raman datasets, similar to those that have helped transform digital pathology, which researchers can use to develop and refine deep learning models.

Machine-Learning Applications in Oral Cancer: A Systematic Review

Over the years, several machine-learning applications have been suggested to assist in various clinical scenarios relevant to oral cancer. We offer a systematic review to identify, assess, and summarize the evidence for reported uses in the areas of oral cancer detection and prevention, prognosis, pre-cancer, treatment, and quality of life. The main algorithms applied in the context of oral cancer applications corresponded to SVM, ANN, and LR, comprising 87.71% of the total published articles in the field. Genomic, histopathological, image, medical/clinical, spectral, and speech data were used most often to predict the four areas of application found in this review. In conclusion, our study has shown that machine-learning applications are useful for prognosis, diagnosis, and prevention of potentially malignant oral lesions (pre-cancer) and therapy. Nevertheless, we strongly recommended the application of these methods in daily clinical practice.

Classification of skin cancer using convolutional neural networks analysis of Raman spectra

BACKGROUND AND OBJECTIVE

Skin cancer is the most common malignancy in whites accounting for about one third of all cancers diagnosed per year. Portable Raman spectroscopy setups for skin cancer "optical biopsy" are utilized to detect tumors based on their spectral features caused by the comparative presence of different chemical components. However, low signal-to-noise ratio in such systems may prevent accurate tumors classification. Thus, there is a challenge to develop methods for efficient skin tumors classification.

METHODS

We compare the performance of convolutional neural networks and the projection on latent structures with discriminant analysis for discriminating skin cancer using the analysis of Raman spectra with a high autofluorescence background stimulated by a 785 nm laser. We have registered the spectra of 617 cases of skin neoplasms (615 patients, 70 melanomas, 122 basal cell carcinomas, 12 squamous cell carcinomas and 413 benign tumors) in vivo with a portable Raman setup and created classification models both for convolutional neural networks and projection on latent structures approaches. To check the classification models stability, a 10-fold cross-validation was performed for all created models. To avoid models overfitting, the data was divided into a training set (80% of spectral dataset) and a test set (20% of spectral dataset).

RESULTS

The results for different classification tasks demonstrate that the convolutional neural networks significantly (p<0.01) outperforms the projection on latent structures. For the convolutional neural networks implementation we obtained ROC AUCs of 0.96 (0.94 - 0.97; 95% CI), 0.90 (0.85-0.94; 95% CI), and 0.92 (0.87 - 0.97; 95% CI) for classifying a) malignant vs benign tumors, b) melanomas vs pigmented tumors and c) melanomas vs seborrheic keratosis respectively.

CONCLUSIONS

The performance of the convolutional neural networks classification of skin tumors based on Raman spectra analysis is higher or comparable to the accuracy provided by trained dermatologists. The increased accuracy with the convolutional neural networks implementation is due to a more precise accounting of low intensity Raman bands in the intense autofluorescence background. The achieved high performance of skin tumors classifications with convolutional neural networks analysis opens a possibility for wide implementation of Raman setups in clinical setting.

Raman Spectroscopy: A Personalized Decision-Making Tool on Clinicians' Hands for In Situ Cancer Diagnosis and Surgery Guidance

Accurate in situ diagnosis and optimal surgical removal of a malignancy constitute key elements in reducing cancer-related morbidity and mortality. In surgical oncology, the accurate discrimination between healthy and cancerous tissues is critical for the postoperative care of the patient. Conventional imaging techniques have attempted to serve as adjuvant tools for in situ biopsy and surgery guidance. However, no single imaging modality has been proven sufficient in terms of specificity, sensitivity, multiplexing capacity, spatial and temporal resolution. Moreover, most techniques are unable to provide information regarding the molecular tissue composition. In this review, we highlight the potential of Raman spectroscopy as a spectroscopic technique with high detection sensitivity and spatial resolution for distinguishing healthy from malignant margins in microscopic scale and in real time. A Raman spectrum constitutes an intrinsic "molecular finger-print" of the tissue and any biochemical alteration related to inflammatory or cancerous tissue state is reflected on its Raman spectral fingerprint. Nowadays, advanced Raman systems coupled with modern instrumentation devices and machine learning methods are entering the clinical arena as adjunct tools towards personalized and optimized efficacy in surgical oncology.

Utilizing Deep Machine Learning for Prognostication of Oral Squamous Cell Carcinoma-A Systematic Review

The application of deep machine learning, a subfield of artificial intelligence, has become a growing area of interest in predictive medicine in recent years. The deep machine learning approach has been used to analyze imaging and radiomics and to develop models that have the potential to assist the clinicians to make an informed and guided decision that can assist to improve patient outcomes. Improved prognostication of oral squamous cell carcinoma (OSCC) will greatly benefit the clinical management of oral cancer patients. This review examines the recent development in the field of deep learning for OSCC prognostication. The search was carried out using five different databases-PubMed, Scopus, OvidMedline, Web of Science, and Institute of Electrical and Electronic Engineers (IEEE). The search was carried time from inception until 15 May 2021. There were 34 studies that have used deep machine learning for the prognostication of OSCC. The majority of these studies used a convolutional neural network (CNN). This review showed that a range of novel imaging modalities such as computed tomography (or enhanced computed tomography) images and spectra data have shown significant applicability to improve OSCC outcomes. The average specificity, sensitivity, area under receiving operating characteristics curve [AUC]), and accuracy for studies that used spectra data were 0.97, 0.99, 0.96, and 96.6%, respectively. Conversely, the corresponding average values for these parameters for computed tomography images were 0.84, 0.81, 0.967, and 81.8%, respectively. Ethical concerns such as privacy and confidentiality, data and model bias, peer disagreement, responsibility gap, patient-clinician relationship, and patient autonomy have limited the widespread adoption of these models in daily clinical practices. The accumulated evidence indicates that deep machine learning models have great potential in the prognostication of OSCC. This approach offers a more generic model that requires less data engineering with improved accuracy.

Deep Machine Learning for Oral Cancer: From Precise Diagnosis to Precision Medicine

Oral squamous cell carcinoma (OSCC) is one of the most prevalent cancers worldwide and its incidence is on the rise in many populations. The high incidence rate, late diagnosis, and improper treatment planning still form a significant concern. Diagnosis at an early-stage is important for better prognosis, treatment, and survival. Despite the recent improvement in the understanding of the molecular mechanisms, late diagnosis and approach toward precision medicine for OSCC patients remain a challenge. To enhance precision medicine, deep machine learning technique has been touted to enhance early detection, and consequently to reduce cancer-specific mortality and morbidity. This technique has been reported to have made a significant progress in data extraction and analysis of vital information in medical imaging in recent years. Therefore, it has the potential to assist in the early-stage detection of oral squamous cell carcinoma. Furthermore, automated image analysis can assist pathologists and clinicians to make an informed decision regarding cancer patients. This article discusses the technical knowledge and algorithms of deep learning for OSCC. It examines the application of deep learning technology in cancer detection, image classification, segmentation and synthesis, and treatment planning. Finally, we discuss how this technique can assist in precision medicine and the future perspective of deep learning technology in oral squamous cell carcinoma.

Deeply-recursive convolutional neural network for Raman spectra identification

Raman spectroscopy has been widely used in various fields due to its unique and superior properties. For achieving high spectral identification speeds and high accuracy, machine learning methods have found many applications in this area, with convolutional neural network-based methods showing great advantages. In this study, we propose a Raman spectral identification method using a deeply-recursive convolutional neural network (DRCNN). It has a very deep network structure (up to 16 layers) for improving performance without introducing more parameters for recursive layers, which eases the difficulty of training. We also propose a recursive-supervision extension to ease the difficulty of training. By testing several different open-source spectral databases, DRCNN has achieved higher prediction accuracies and better performance in transfer learning compared with other CNN-based methods. Superior identification performance is demonstrated, especially by identification, for characteristically similar and indistinguishable spectra.

Raman spectroscopy has been widely used in various fields due to its unique and superior properties.

Intraoperative discrimination of native meningioma and dura mater by Raman spectroscopy

Meningiomas are among the most frequent tumors of the central nervous system. For a total resection, shown to decrease recurrences, it is paramount to reliably discriminate tumor tissue from normal dura mater intraoperatively. Raman spectroscopy (RS) is a non-destructive, label-free method for vibrational analysis of biochemical molecules. On the microscopic level, RS was already used to differentiate meningioma from dura mater. In this study we test its suitability for intraoperative macroscopic meningioma diagnostics. RS is applied to surgical specimen of intracranial meningiomas. The main purpose is the differentiation of tumor from normal dura mater, in order to potentially accelerate the diagnostic workflow. The collected meningioma and dura mater samples (n = 223 tissue samples from a total of 59 patients) are analyzed under untreated conditions using a new partially robotized RS acquisition system. Spectra (n = 1273) are combined with the according histopathological analysis for each sample. Based on this, a classifier is trained via machine learning. Our trained classifier separates meningioma and dura mater with a sensitivity of 96.06 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\pm $$\end{document}± 0.03% and a specificity of 95.44 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\pm $$\end{document}± 0.02% for internal fivefold cross validation and 100% and 93.97% if validated with an external test set. RS is an efficient method to discriminate meningioma from healthy dura mater in fresh tissue samples without additional processing or histopathological imaging. It is a quick and reliable complementary diagnostic tool to the neuropathological workflow and has potential for guided surgery. RS offers a safe way to examine unfixed surgical specimens in a perioperative setting.

How molecular imaging will enable robotic precision surgery : The role of artificial intelligence, augmented reality, and navigation

Molecular imaging is one of the pillars of precision surgery. Its applications range from early diagnostics to therapy planning, execution, and the accurate assessment of outcomes. In particular, molecular imaging solutions are in high demand in minimally invasive surgical strategies, such as the substantially increasing field of robotic surgery. This review aims at connecting the molecular imaging and nuclear medicine community to the rapidly expanding armory of surgical medical devices. Such devices entail technologies ranging from artificial intelligence and computer-aided visualization technologies (software) to innovative molecular imaging modalities and surgical navigation (hardware). We discuss technologies based on their role at different steps of the surgical workflow, i.e., from surgical decision and planning, over to target localization and excision guidance, all the way to (back table) surgical verification. This provides a glimpse of how innovations from the technology fields can realize an exciting future for the molecular imaging and surgery communities.

Automatic localization of cephalometric landmarks based on convolutional neural network

INTRODUCTION

Cephalometry plays an important role in the diagnosis and treatment of orthodontics and orthognathic surgery. This study intends to develop an automatic landmark location system to make cephalometry more convenient.

METHODS

In this study, 512 lateral cephalograms were collected, and 37 landmarks were included. The coordinates of all landmarks in the 512 films were obtained to establish a labeled dataset: 312 were used as a training set, 100 as a validation set, and 100 as a testing set. An automatic landmark location system based on the convolutional neural network was developed. This system consisted of a global detection module and a locally modified module. The lateral cephalogram was first fed into the global module to obtain an initial estimate of the landmark's position, which was then adjusted with the locally modified module to improve accuracy. Mean radial error (MRE) and success detection rate (SDR) within the range of 1-4 mm were used to evaluate the method.

RESULTS

The MRE of our validation set was 1.127 ± 1.028 mm, and SDR of 1.0, 1.5, 2.0, 2.5, 3.0, and 4.0 mm were respectively 45.95%, 89.19%, 97.30%, 97.30%, and 97.30%. The MRE of our testing set was 1.038 ± 0.893 mm, and SDR of 1.0, 1.5, 2.0, 2.5, 3.0, and 4.0 mm were respectively 54.05%, 91.89%, 97.30%, 100%, 100%, and 100%.

CONCLUSIONS

In this study, we proposed a new automatic landmark location system on the basis of the convolutional neural network. The system could detect 37 landmarks with high accuracy. All landmarks are commonly used in clinical practice and could meet the requirements of different cephalometric analysis methods.

Robust feature collection and classification of network culture

The network provides a convenient mechanism for publishing and obtaining documents, and has now become a gathering place for all kinds of information. In the network, the amount of information increases exponentially, and how to dig useful patterns or knowledge from the massive network culture has become a hot topic for scholars. In data mining, in order to enable readers to quickly obtain the content of interest, research text classification, and automatically classify text data according to a certain classification model. Internet cultural text data has the characteristics of unstructured, subjective, high-dimensional, etc., which makes it difficult for text mining algorithms to extract effective and easy-to-understand classification rules, and the computational complexity is too high. This paper proposes a feature selection method based on robust features, using sample deviation and variance as the criteria for feature attributes to rank the importance of feature attributes, and select the best feature attribute subset. The experimental results show that the classification accuracy of the feature selection method based on sample deviation and variance proposed in this paper is higher than the traditional word frequency as the feature selection method, which proves the feasibility and superiority of the feature selection method proposed in this paper.

Biomedical applications of vibrational spectroscopy: Oral cancer diagnostics

Vibrational spectroscopy, based on either infrared absorption or Raman scattering, has attracted increasing attention for biomedical applications. Proof of concept explorations for diagnosis of oral potentially malignant disorders and cancer are reviewed, and recent advances critically appraised. Specific examples of applications of Raman microspectroscopy for analysis of histological, cytological and saliva samples are presented for illustrative purposes, and the future prospects, ultimately for routine, chairside in vivo screening are discussed.

Assessment of the deep resection margin during oral cancer surgery: A systematic review

The main challenge for radical resection in oral cancer surgery is to obtain adequate resection margins. Especially the deep margin, which can only be estimated based on palpation during surgery, is often reported inadequate. To increase the percentage of radical resections, there is a need for a quick, easy, minimal invasive method, which assesses the deep resection margin without interrupting or prolonging surgery. This systematic review provides an overview of technologies that are currently being studied with the aim of fulfilling this demand. A literature search was conducted through the databases Medline, Embase and the Cochrane Library. A total of 62 studies were included. The results were categorized according to the type of technique: 'Frozen Section Analysis', 'Fluorescence', 'Optical Imaging', 'Conventional imaging techniques', and 'Cytological assessment'. This systematic review gives for each technique an overview of the reported performance (accuracy, sensitivity, specificity, positive predictive value, negative predictive value, or a different outcome measure), acquisition time, and sampling depth. At the moment, the most prevailing technique remains frozen section analysis. In the search for other assessment methods to evaluate the deep resection margin, some technologies are very promising for future use when effectiveness has been shown in larger trials, e.g., fluorescence (real-time, sampling depth up to 6 mm) or optical techniques such as hyperspectral imaging (real-time, sampling depth few mm) for microscopic margin assessment and ultrasound (less than 10 min, sampling depth several cm) for assessment on a macroscopic scale.

Machine learning in oral squamous cell carcinoma: Current status, clinical concerns and prospects for future-A systematic review

BACKGROUND

Oral cancer can show heterogenous patterns of behavior. For proper and effective management of oral cancer, early diagnosis and accurate prediction of prognosis are important. To achieve this, artificial intelligence (AI) or its subfield, machine learning, has been touted for its potential to revolutionize cancer management through improved diagnostic precision and prediction of outcomes. Yet, to date, it has made only few contributions to actual medical practice or patient care.

OBJECTIVES

This study provides a systematic review of diagnostic and prognostic application of machine learning in oral squamous cell carcinoma (OSCC) and also highlights some of the limitations and concerns of clinicians towards the implementation of machine learning-based models for daily clinical practice.

DATA SOURCES

We searched OvidMedline, PubMed, Scopus, Web of Science, and Institute of Electrical and Electronics Engineers (IEEE) databases from inception until February 2020 for articles that used machine learning for diagnostic or prognostic purposes of OSCC.

ELIGIBILITY CRITERIA

Only original studies that examined the application of machine learning models for prognostic and/or diagnostic purposes were considered.

DATA EXTRACTION

Independent extraction of articles was done by two researchers (A.R. & O.Y) using predefine study selection criteria. We used the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) in the searching and screening processes. We also used Prediction model Risk of Bias Assessment Tool (PROBAST) for assessing the risk of bias (ROB) and quality of included studies.

RESULTS

A total of 41 studies were published to have used machine learning to aid in the diagnosis/or prognosis of OSCC. The majority of these studies used the support vector machine (SVM) and artificial neural network (ANN) algorithms as machine learning techniques. Their specificity ranged from 0.57 to 1.00, sensitivity from 0.70 to 1.00, and accuracy from 63.4 % to 100.0 % in these studies. The main limitations and concerns can be grouped as either the challenges inherent to the science of machine learning or relating to the clinical implementations.

CONCLUSION

Machine learning models have been reported to show promising performances for diagnostic and prognostic analyses in studies of oral cancer. These models should be developed to further enhance explainability, interpretability, and externally validated for generalizability in order to be safely integrated into daily clinical practices. Also, regulatory frameworks for the adoption of these models in clinical practices are necessary.

Deep Learning in Head and Neck Tumor Multiomics Diagnosis and Analysis: Review of the Literature

Head and neck tumors are the sixth most common neoplasms. Multiomics integrates multiple dimensions of clinical, pathologic, radiological, and biological data and has the potential for tumor diagnosis and analysis. Deep learning (DL), a type of artificial intelligence (AI), is applied in medical image analysis. Among the DL techniques, the convolution neural network (CNN) is used for image segmentation, detection, and classification and in computer-aided diagnosis. Here, we reviewed multiomics image analysis of head and neck tumors using CNN and other DL neural networks. We also evaluated its application in early tumor detection, classification, prognosis/metastasis prediction, and the signing out of the reports. Finally, we highlighted the challenges and potential of these techniques.

The Potential of Raman Spectroscopy in the Diagnosis of Dysplastic and Malignant Oral Lesions

Simple Summary

Raman spectroscopy, a light scattering technique that provides the biochemical fingerprint of a sample, was used on samples taken from patients with cancer and precancerous lesions. This information was then used to build a classifier to identify cancer and the precancerous phases. The ability to distinguish cancerous tissue from normal and precancerous tissue is diagnostically crucial as it can alter the patients’ prognosis and management. Moreover, as cellular changes are often present at the tumour margin, the ability to distinguish these changes from cancer can help in preserving more of the tissue and maintaining aesthetics and functionality for the patient.

Abstract

Early diagnosis, treatment and/or surveillance of oral premalignant lesions are important in preventing progression to oral squamous cell carcinoma (OSCC). The current gold standard is through histopathological diagnosis, which is limited by inter- and intra-observer errors and sampling errors. The objective of this work was to use Raman spectroscopy to discriminate between benign, mild, moderate and severe dysplasia and OSCC in formalin fixed paraffin preserved (FFPP) tissues. The study included 72 different pathologies from which 17 were benign lesions, 20 mildly dysplastic, 20 moderately dysplastic, 10 severely dysplastic and 5 invasive OSCC. The glass substrate and paraffin wax background were digitally removed and PLSDA with LOPO cross-validation was used to differentiate the pathologies. OSCC could be differentiated from the other pathologies with an accuracy of 70%, while the accuracy of the classifier for benign, moderate and severe dysplasia was ~60%. The accuracy of the classifier was lowest for mild dysplasia (~46%). The main discriminating features were increased nucleic acid contributions and decreased protein and lipid contributions in the epithelium and decreased collagen contributions in the connective tissue. Smoking and the presence of inflammation were found to significantly influence the Raman classification with respective accuracies of 76% and 94%.

Applications of deep learning in dentistry

Over the last few years, translational applications of so-called artificial intelligence in the field of medicine have garnered a significant amount of interest. The present article aims to review existing dental literature that has examined deep learning, a subset of machine learning that has demonstrated the highest performance when applied to image processing and that has been tested as a formidable diagnostic support tool through its automated analysis of radiographic/photographic images. Furthermore, the article will critically evaluate the literature to describe potential methodological weaknesses of the studies and the need for further development. This review includes 28 studies that have described the applications of deep learning in various fields of dentistry. Research into the applications of deep learning in dentistry contains claims of its high accuracy. Nonetheless, many of these studies have substantial limitations and methodological issues (e.g., examiner reliability, the number of images used for training/testing, the methods used for validation) that have significantly limited the external validity of their results. Therefore, future studies that acknowledge the methodological limitations of existing literature will help to establish a better understanding of the usefulness of applying deep learning in dentistry.

Deep learning a boon for biophotonics?

This review covers original articles using deep learning in the biophotonic field published in the last years. In these years deep learning, which is a subset of machine learning mostly based on artificial neural network geometries, was applied to a number of biophotonic tasks and has achieved state-of-the-art performances. Therefore, deep learning in the biophotonic field is rapidly growing and it will be utilized in the next years to obtain real-time biophotonic decision-making systems and to analyze biophotonic data in general. In this contribution, we discuss the possibilities of deep learning in the biophotonic field including image classification, segmentation, registration, pseudostaining and resolution enhancement. Additionally, we discuss the potential use of deep learning for spectroscopic data including spectral data preprocessing and spectral classification. We conclude this review by addressing the potential applications and challenges of using deep learning for biophotonic data.

Towards development of a novel universal medical diagnostic method: Raman spectroscopy and machine learning

This review summarizes recent progress made using Raman spectroscopy and machine learning for potential universal medical diagnostic applications.