Journal of Personalized Medicine

Editorial

6 pages, 234 KiB

Open AccessEditorial

Translating Biospectroscopy Techniques to Clinical Settings: A New Paradigm in Point-of-Care Screening and/or Diagnostics

by Francis L. Martin

J. Pers. Med. 2023, 13(10), 1511; https://doi.org/10.3390/jpm13101511 - 19 Oct 2023

Cited by 2 | Viewed by 1440

Abstract

As healthcare tools increasingly move towards a more digital and computational format, there is an increasing need for sensor-based technologies that allow for rapid screening and/or diagnostics [...] Full article

(This article belongs to the Special Issue Clinical Applications of Biospectroscopy and Imaging)

Research

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10 pages, 4041 KiB

Open AccessArticle

Perfusion in Pedicled Skin Flaps: Initial Insights from Smartphone-Based Thermal Imaging Protocol

by Lukas S. Fiedler, Burkard M. Lippert, Lukas Adrian and Tobias Meyer

J. Pers. Med. 2024, 14(7), 730; https://doi.org/10.3390/jpm14070730 - 5 Jul 2024

Cited by 2 | Viewed by 668

Abstract

Objective: Successful outcomes in head and neck surgery rely on maintaining perfusion in pedicled skin flaps. Thermal imaging offers a noninvasive means to assess tissue perfusion, potentially aiding in predicting flap viability. This pilot study explores the utility of SBTI (smartphone-based thermal imaging) [...] Read more.

Objective: Successful outcomes in head and neck surgery rely on maintaining perfusion in pedicled skin flaps. Thermal imaging offers a noninvasive means to assess tissue perfusion, potentially aiding in predicting flap viability. This pilot study explores the utility of SBTI (smartphone-based thermal imaging) for predicting flap vitality and monitoring during surgery. Methods: Thermal imaging was employed using the FLIR One System. An imaging protocol was established, defining points of interest (T1-T4) on pedicled skin flaps. Conducted over four months, the study integrated SBTI into reconstructive surgery for the face, head and neck defects post-tumor resections. SBTI’s effectiveness was assessed with n = 11 pedicled flaps, capturing images at key stages and correlating them with clinical flap assessment. Thermal images were retrospectively graded by two surgeons, evaluating flap perfusion on a scale from 1 to 5, based on temperature differences (1 = ΔT < 2 °C, 2 = ΔT ≥ 2 °C, 3 = ΔT ≥ 4 °C, 4 = ΔT ≥ 6 °C, and 5 = ΔT ≥ 8 °C), with assessments averaged for consensus and compared with the clinical assessment control group. Results: The study encountered challenges during implementation, leading to the exclusion of six patients. Patient data included 11 cases with n = 44 SBTI images. Intraoperative assessments consistently showed good perfusion. One postoperative dehiscence was noted, which retrospectively coincided with intraoperative SBTI grading, but not with clinical assessment. Statistical analysis indicated consistent outcomes following clinical and SBTI assessments. Thermal imaging accurately predicted flap viability, although it had limitations with small flaps. Conclusion: SBTI proved effective, inexpensive, and noninvasive for assessing tissue perfusion, showing promise for predicting flap viability and intraoperative monitoring in head and neck surgery. Full article

(This article belongs to the Special Issue Clinical Applications of Biospectroscopy and Imaging)

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12 pages, 3173 KiB

Open AccessFeature PaperArticle

Rapid and Label-Free Histopathology of Oral Lesions Using Deep Learning Applied to Optical and Infrared Spectroscopic Imaging Data

by Matthew P. Confer, Kianoush Falahkheirkhah, Subin Surendran, Sumsum P. Sunny, Kevin Yeh, Yen-Ting Liu, Ishaan Sharma, Andres C. Orr, Isabella Lebovic, William J. Magner, Sandra Lynn Sigurdson, Alfredo Aguirre, Michael R. Markiewicz, Amritha Suresh, Wesley L. Hicks, Jr., Praveen Birur, Moni Abraham Kuriakose and Rohit Bhargava

J. Pers. Med. 2024, 14(3), 304; https://doi.org/10.3390/jpm14030304 - 13 Mar 2024

Cited by 1 | Viewed by 2189

Abstract

Oral potentially malignant disorders (OPMDs) are precursors to over 80% of oral cancers. Hematoxylin and eosin (H&E) staining, followed by pathologist interpretation of tissue and cellular morphology, is the current gold standard for diagnosis. However, this method is qualitative, can result in errors [...] Read more.

Oral potentially malignant disorders (OPMDs) are precursors to over 80% of oral cancers. Hematoxylin and eosin (H&E) staining, followed by pathologist interpretation of tissue and cellular morphology, is the current gold standard for diagnosis. However, this method is qualitative, can result in errors during the multi-step diagnostic process, and results may have significant inter-observer variability. Chemical imaging (CI) offers a promising alternative, wherein label-free imaging is used to record both the morphology and the composition of tissue and artificial intelligence (AI) is used to objectively assign histologic information. Here, we employ quantum cascade laser (QCL)-based discrete frequency infrared (DFIR) chemical imaging to record data from oral tissues. In this proof-of-concept study, we focused on achieving tissue segmentation into three classes (connective tissue, dysplastic epithelium, and normal epithelium) using a convolutional neural network (CNN) applied to three bands of label-free DFIR data with paired darkfield visible imaging. Using pathologist-annotated H&E images as the ground truth, we demonstrate results that are 94.5% accurate with the ground truth using combined information from IR and darkfield microscopy in a deep learning framework. This chemical-imaging-based workflow for OPMD classification has the potential to enhance the efficiency and accuracy of clinical oral precancer diagnosis. Full article

(This article belongs to the Special Issue Clinical Applications of Biospectroscopy and Imaging)

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13 pages, 2083 KiB

Open AccessArticle

Near-Infrared Spectroscopy with Supervised Machine Learning as a Screening Tool for Neutropenia

by José Joaquim Raposo-Neto, Eduardo Kowalski-Neto, Wilson Barros Luiz, Estherlita Almeida Fonseca, Anna Karla Costa Logrado Cedro, Maneesh N. Singh, Francis L. Martin, Paula Frizera Vassallo, Luciene Cristina Gastalho Campos and Valerio Garrone Barauna

J. Pers. Med. 2024, 14(1), 9; https://doi.org/10.3390/jpm14010009 - 21 Dec 2023

Cited by 2 | Viewed by 1496

Abstract

The use of non-invasive tools in conjunction with artificial intelligence (AI) to detect diseases has the potential to revolutionize healthcare. Near-infrared spectroscopy (NIR) is a technology that can be used to analyze biological samples in a non-invasive manner. This study evaluated the use [...] Read more.

The use of non-invasive tools in conjunction with artificial intelligence (AI) to detect diseases has the potential to revolutionize healthcare. Near-infrared spectroscopy (NIR) is a technology that can be used to analyze biological samples in a non-invasive manner. This study evaluated the use of NIR spectroscopy in the fingertip to detect neutropenia in solid-tumor oncologic patients. A total of 75 patients were enrolled in the study. Fingertip NIR spectra and complete blood counts were collected from each patient. The NIR spectra were pre-processed using Savitzky–Golay smoothing and outlier detection. The pre-processed data were split into training/validation and test sets using the Kennard–Stone method. A toolbox of supervised machine learning classification algorithms was applied to the training/validation set using a stratified 5-fold cross-validation regimen. The algorithms included linear discriminant analysis (LDA), logistic regression (LR), random forest (RF), multilayer perceptron (MLP), and support vector machines (SVMs). The SVM model performed best in the validation step, with 85% sensitivity, 89% negative predictive value (NPV), and 64% accuracy. The SVM model showed 67% sensitivity, 82% NPV, and 57% accuracy on the test set. These results suggest that NIR spectroscopy in the fingertip, combined with machine learning methods, can be used to detect neutropenia in solid-tumor oncology patients in a non-invasive and timely manner. This approach could help reduce exposure to invasive tests and prevent neutropenic patients from inadvertently undergoing chemotherapy. Full article

(This article belongs to the Special Issue Clinical Applications of Biospectroscopy and Imaging)

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12 pages, 2256 KiB

Open AccessArticle

A Linear Predictor Based on FTIR Spectral Biomarkers Improves Disease Diagnosis Classification: An Application to Multiple Sclerosis

by Francesca Condino, Maria Caterina Crocco, Domenico Pirritano, Alfredo Petrone, Francesco Del Giudice and Rita Guzzi

J. Pers. Med. 2023, 13(11), 1596; https://doi.org/10.3390/jpm13111596 - 11 Nov 2023

Viewed by 1271

Abstract

Multiple sclerosis (MS) is a neurodegenerative disease of the central nervous system that can lead to long-term disability. The diagnosis of MS is not simple and requires many instrumental and clinical tests. Sampling easily collected biofluids using spectroscopic approaches is becoming of increasing [...] Read more.

Multiple sclerosis (MS) is a neurodegenerative disease of the central nervous system that can lead to long-term disability. The diagnosis of MS is not simple and requires many instrumental and clinical tests. Sampling easily collected biofluids using spectroscopic approaches is becoming of increasing interest in the medical field to integrate and improve diagnostic procedures. Here we present a statistical approach where we combine a number of spectral biomarkers derived from the ATR-FTIR spectra of blood plasma samples of healthy control subjects and MS patients, to obtain a linear predictor useful for discriminating between the two groups of individuals. This predictor provides a simple tool in which the contribution of different molecular components is summarized and, as a result, the sensitivity (80%) and specificity (93%) of the identification are significantly improved compared to those obtained with typical classification algorithms. The strategy proposed can be very helpful when applied to the diagnosis of diseases whose presence is reflected in a minimal way in the analyzed biofluids (blood and its derivatives), as it is for MS as well as for other neurological disorders. Full article

(This article belongs to the Special Issue Clinical Applications of Biospectroscopy and Imaging)

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12 pages, 991 KiB

Open AccessArticle

Attenuated Total Reflection Fourier-Transform Infrared Spectral Discrimination in Human Tissue of Oesophageal Transformation to Adenocarcinoma

by Ishaan Maitra, Camilo L. M. Morais, Kássio M. G. Lima, Katherine M. Ashton, Danielle Bury, Ravindra S. Date and Francis L. Martin

J. Pers. Med. 2023, 13(8), 1277; https://doi.org/10.3390/jpm13081277 - 20 Aug 2023

Cited by 1 | Viewed by 1542

Abstract

This study presents ATR-FTIR (attenuated total reflectance Fourier-transform infrared) spectral analysis of ex vivo oesophageal tissue including all classifications to oesophageal adenocarcinoma (OAC). The article adds further validation to previous human tissue studies identifying the potential for ATR-FTIR spectroscopy in differentiating among all [...] Read more.

This study presents ATR-FTIR (attenuated total reflectance Fourier-transform infrared) spectral analysis of ex vivo oesophageal tissue including all classifications to oesophageal adenocarcinoma (OAC). The article adds further validation to previous human tissue studies identifying the potential for ATR-FTIR spectroscopy in differentiating among all classes of oesophageal transformation to OAC. Tissue spectral analysis used principal component analysis quadratic discriminant analysis (PCA-QDA), successive projection algorithm quadratic discriminant analysis (SPA-QDA), and genetic algorithm quadratic discriminant analysis (GA-QDA) algorithms for variable selection and classification. The variables selected by SPA-QDA and GA-QDA discriminated tissue samples from Barrett’s oesophagus (BO) to OAC with 100% accuracy on the basis of unique spectral “fingerprints” of their biochemical composition. Accuracy test results including sensitivity and specificity were determined. The best results were obtained with PCA-QDA, where tissues ranging from normal to OAC were correctly classified with 90.9% overall accuracy (71.4–100% sensitivity and 89.5–100% specificity), including the discrimination between normal and inflammatory tissue, which failed in SPA-QDA and GA-QDA. All the models revealed excellent results for distinguishing among BO, low-grade dysplasia (LGD), high-grade dysplasia (HGD), and OAC tissues (100% sensitivities and specificities). This study highlights the need for further work identifying potential biochemical markers using ATR-FTIR in tissue that could be utilised as an adjunct to histopathological diagnosis for early detection of neoplastic changes in susceptible epithelium. Full article

(This article belongs to the Special Issue Clinical Applications of Biospectroscopy and Imaging)

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16 pages, 3510 KiB

Open AccessEditor’s ChoiceArticle

ATR-FTIR Spectroscopy with Chemometrics for Analysis of Saliva Samples Obtained in a Lung-Cancer-Screening Programme: Application of Swabs as a Paradigm for High Throughput in a Clinical Setting

by Francis L. Martin, Andrew W. Dickinson, Tarek Saba, Thomas Bongers, Maneesh N. Singh and Danielle Bury

J. Pers. Med. 2023, 13(7), 1039; https://doi.org/10.3390/jpm13071039 - 25 Jun 2023

Cited by 8 | Viewed by 2027

Abstract

There is an increasing need for inexpensive and rapid screening tests in point-of-care clinical oncology settings. Herein, we develop a swab “dip” test in saliva obtained from consenting patients participating in a lung-cancer-screening programme being undertaken in North West England. In a pilot [...] Read more.

There is an increasing need for inexpensive and rapid screening tests in point-of-care clinical oncology settings. Herein, we develop a swab “dip” test in saliva obtained from consenting patients participating in a lung-cancer-screening programme being undertaken in North West England. In a pilot study, a total of 211 saliva samples (n = 170 benign, 41 designated cancer-positive) were randomly taken during the course of this prospective lung-cancer-screening programme. The samples (sterile Copan blue rayon swabs dipped in saliva) were analysed using attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy. An exploratory analysis using principal component analysis (PCA,) with or without linear discriminant analysis (LDA), was then undertaken. Three pairwise comparisons were undertaken including: (1) benign vs. cancer following swab analysis; (2) benign vs. cancer following swab analysis with the subtraction of dry swab spectra; and (3) benign vs. cancer following swab analysis with the subtraction of wet swab spectra. Consistent and remarkably similar patterns of clustering for the benign control vs. cancer categories, irrespective of whether the swab plus saliva sample was analysed or whether there was a subtraction of wet or dry swab spectra, was observed. In each case, MANOVA demonstrated that this segregation of categories is highly significant. A k-NN (using three nearest neighbours) machine-learning algorithm also showed that the specificity (90%) and sensitivity (75%) are consistent for each pairwise comparison. In detailed analyses, the swab as a substrate did not alter the level of spectral discrimination between benign control vs. cancer saliva samples. These results demonstrate a novel swab “dip” test using saliva as a biofluid that is highly applicable to be rolled out into a larger lung-cancer-screening programme. Full article

(This article belongs to the Special Issue Clinical Applications of Biospectroscopy and Imaging)

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12 pages, 2020 KiB

Open AccessFeature PaperArticle

Characterisation of Cartilage Damage via Fusing Mid-Infrared, Near-Infrared, and Raman Spectroscopic Data

by Rubina Shaikh, Valeria Tafintseva, Ervin Nippolainen, Vesa Virtanen, Johanne Solheim, Boris Zimmermann, Simo Saarakkala, Juha Töyräs, Achim Kohler and Isaac O. Afara

J. Pers. Med. 2023, 13(7), 1036; https://doi.org/10.3390/jpm13071036 - 24 Jun 2023

Cited by 1 | Viewed by 1499

Abstract

Mid-infrared spectroscopy (MIR), near-infrared spectroscopy (NIR), and Raman spectroscopy are all well-established analytical techniques in biomedical applications. Since they provide complementary chemical information, we aimed to determine whether combining them amplifies their strengths and mitigates their weaknesses. This study investigates the feasibility of [...] Read more.

Mid-infrared spectroscopy (MIR), near-infrared spectroscopy (NIR), and Raman spectroscopy are all well-established analytical techniques in biomedical applications. Since they provide complementary chemical information, we aimed to determine whether combining them amplifies their strengths and mitigates their weaknesses. This study investigates the feasibility of the fusion of MIR, NIR, and Raman spectroscopic data for characterising articular cartilage integrity. Osteochondral specimens from bovine patellae were subjected to mechanical and enzymatic damage, and then MIR, NIR, and Raman data were acquired from the damaged and control specimens. We assessed the capacity of individual spectroscopic methods to classify the samples into damage or control groups using Partial Least Squares Discriminant Analysis (PLS-DA). Multi-block PLS-DA was carried out to assess the potential of data fusion by combining the dataset by applying two-block (MIR and NIR, MIR and Raman, NIR and Raman) and three-block approaches (MIR, NIR, and Raman). The results of the one-block models show a higher classification accuracy for NIR (93%) and MIR (92%) than for Raman (76%) spectroscopy. In contrast, we observed the highest classification efficiency of 94% and 93% for the two-block (MIR and NIR) and three-block models, respectively. The detailed correlative analysis of the spectral features contributing to the discrimination in the three-block models adds considerably more insight into the molecular origin of cartilage damage. Full article

(This article belongs to the Special Issue Clinical Applications of Biospectroscopy and Imaging)

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