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Xiong L, Zhang J, Li D, Yu H, Tian T, Deng K, Qin Z, Zhang J, Huang J, Huang P. FTIR microspectroscopy of renal tubules for the identification of diabetic ketoacidosis death. Microchem J 2023. [DOI: 10.1016/j.microc.2023.108532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/17/2023]
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Galante N, Cotroneo R, Furci D, Lodetti G, Casali MB. Applications of artificial intelligence in forensic sciences: Current potential benefits, limitations and perspectives. Int J Legal Med 2023; 137:445-458. [PMID: 36507961 DOI: 10.1007/s00414-022-02928-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 12/04/2022] [Indexed: 12/14/2022]
Abstract
In recent years, new studies based on artificial intelligence (AI) have been conducted in the forensic field, posing new challenges and demonstrating the advantages and disadvantages of using AI methodologies to solve forensic well-known problems. Specifically, AI technology has tried to overcome the human subjective bias limitations of the traditional approach of the forensic sciences, which include sex prediction and age estimation from morphometric measurements in forensic anthropology or evaluating the third molar stage of development in forensic odontology. Likewise, AI has been studied as an assisting tool in forensic pathology for a quick and easy identification of the taxonomy of diatoms. The present systematic review follows the PRISMA 2020 statements and aims to explore an emerging topic that has been poorly analyzed in the forensic literature. Benefits, limitations, and forensic implications concerning AI are therefore highlighted, by providing an extensive critical review of its current applications on forensic sciences as well as its future directions. Results are divided into 5 subsections which included forensic anthropology, forensic odontology, forensic pathology, forensic genetics, and other forensic branches. The discussion offers a useful instrument to investigate the potential benefits of AI in the forensic fields as well as to point out the existing open questions and issues concerning its application on real-life scenarios. Procedural notes and technical aspects are also provided to the readers.
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Affiliation(s)
- Nicola Galante
- Healthcare Accountability Lab, Institute of Legal Medicine of Milan, University of Milan, Via Luigi Mangiagalli 37, 20133, Milan, Italy.
- Department of Biomedical Sciences for Health, Institute of Legal Medicine of Milan, University of Milan, Via Luigi Mangiagalli 37, 20133, Milan, Italy.
| | - Rosy Cotroneo
- Healthcare Accountability Lab, Institute of Legal Medicine of Milan, University of Milan, Via Luigi Mangiagalli 37, 20133, Milan, Italy
- Department of Biomedical Sciences for Health, Institute of Legal Medicine of Milan, University of Milan, Via Luigi Mangiagalli 37, 20133, Milan, Italy
| | - Domenico Furci
- Healthcare Accountability Lab, Institute of Legal Medicine of Milan, University of Milan, Via Luigi Mangiagalli 37, 20133, Milan, Italy
- Department of Biomedical Sciences for Health, Institute of Legal Medicine of Milan, University of Milan, Via Luigi Mangiagalli 37, 20133, Milan, Italy
| | - Giorgia Lodetti
- Healthcare Accountability Lab, Institute of Legal Medicine of Milan, University of Milan, Via Luigi Mangiagalli 37, 20133, Milan, Italy
- Department of Biomedical Sciences for Health, Institute of Legal Medicine of Milan, University of Milan, Via Luigi Mangiagalli 37, 20133, Milan, Italy
| | - Michelangelo Bruno Casali
- Healthcare Accountability Lab, Institute of Legal Medicine of Milan, University of Milan, Via Luigi Mangiagalli 37, 20133, Milan, Italy
- Department of Oncology and Hemato-Oncology (DIPO), University of Milan, Via Luigi Mangiagalli 37, 20133, Milan, Italy
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Wu H, Li Z, Liang X, Chen R, Yu K, Wei X, Wang G, Cai W, Li H, Sun Q, Wang Z. Pathological and ATR-FTIR spectral changes of delayed splenic rupture and medical significance. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 278:121286. [PMID: 35526439 DOI: 10.1016/j.saa.2022.121286] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 04/13/2022] [Accepted: 04/16/2022] [Indexed: 06/14/2023]
Abstract
Traumatic delayed splenic rupture often follows by a "latent period" without typical symptoms after injury. During this period, though there are no obvious symptoms, the injury is still present and changing. In this study, we constructed an SD rat model of delayed splenic rupture; evaluated the model by HE staining, Perl's staining, Masson trichrome staining and immunohistochemical staining; observed the pathological changes of spleen tissue in delayed splenic rupture at different times after splenic injury; we found that pathological change of injured tissues were different from non-injured, and has phases-change patterns, it can be roughly divided into three phases: 2-7 d, 10-14 d, and 18-28.We then investigated the relationship between the pathological changes and FTIR spectroscopy by chemometric methods. The main distinction of injured and non-injured tissue was the protein secondary structure of amide I, and the main distinctions of different phases of delayed splenic rupture were protein secondary structures and content of amide I and amide II.A classification model developed by SVM-DA was used to infer three phases (2-7 days, 10-12 days and 14-28 days). According to the most probable class, the accuracy of external validation is 96.7%. The results indicate that FTIR spectroscopy combined with various types of pathological staining has a potential for forensic identification and can provide theoretical support and diagnostic reference on clinical persistent injury.
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Affiliation(s)
- Hao Wu
- Department of Forensic Pathology, College of Forensic Medicine, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China
| | - Zefeng Li
- Department of Forensic Pathology, College of Forensic Medicine, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China
| | - Xinggong Liang
- Department of Forensic Pathology, College of Forensic Medicine, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China
| | - Run Chen
- Department of Forensic Pathology, College of Forensic Medicine, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China
| | - Kai Yu
- Department of Forensic Pathology, College of Forensic Medicine, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China
| | - Xin Wei
- Department of Forensic Pathology, College of Forensic Medicine, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China
| | - Gongji Wang
- Department of Forensic Pathology, College of Forensic Medicine, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China
| | - Wumin Cai
- Department of Forensic Pathology, College of Forensic Medicine, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China
| | - Huiyu Li
- Department of Forensic Pathology, College of Forensic Medicine, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China
| | - Qinru Sun
- Department of Forensic Pathology, College of Forensic Medicine, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China.
| | - Zhenyuan Wang
- Department of Forensic Pathology, College of Forensic Medicine, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China.
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Koul A, Bawa RK, Kumar Y. Artificial Intelligence Techniques to Predict the Airway Disorders Illness: A Systematic Review. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2022; 30:831-864. [PMID: 36189431 PMCID: PMC9516534 DOI: 10.1007/s11831-022-09818-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 09/04/2022] [Indexed: 06/16/2023]
Abstract
Airway disease is a major healthcare issue that causes at least 3 million fatalities every year. It is also considered one of the foremost causes of death all around the globe by 2030. Numerous studies have been undertaken to demonstrate the latest advances in artificial intelligence algorithms to assist in identifying and classifying these diseases. This comprehensive review aims to summarise the state-of-the-art machine and deep learning-based systems for detecting airway disorders, envisage the trends of the recent work in this domain, and analyze the difficulties and potential future paths. This systematic literature review includes the study of one hundred fifty-five articles on airway diseases such as cystic fibrosis, emphysema, lung cancer, Mesothelioma, covid-19, pneumoconiosis, asthma, pulmonary edema, tuberculosis, pulmonary embolism as well as highlights the automated learning techniques to predict them. The study concludes with a discussion and challenges about expanding the efficiency and machine and deep learning-assisted airway disease detection applications.
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Affiliation(s)
- Apeksha Koul
- Department of Computer Science and Engineering, Punjabi University, Patiala, Punjab India
| | - Rajesh K. Bawa
- Department of Computer Science, Punjabi University, Patiala, Punjab India
| | - Yogesh Kumar
- Department of Computer Science and Engineering, School of Technology, Pandit Deendayal Energy University, Gandhinagar, Gujarat India
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Peng W, Chen S, Kong D, Zhou X, Lu X, Chang C. Grade classification of human glioma using a convolutional neural network based on mid-infrared spectroscopy mapping. JOURNAL OF BIOPHOTONICS 2022; 15:e202100313. [PMID: 34931464 DOI: 10.1002/jbio.202100313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Revised: 11/15/2021] [Accepted: 12/17/2021] [Indexed: 06/14/2023]
Abstract
This study proposes a convolutional neural network (CNN)-based computer-aided diagnosis (CAD) system for the grade classification of human glioma by using mid-infrared (MIR) spectroscopic mappings. Through data augmentation of pixels recombination, the mappings in the training set increased almost 161 times relative to the original mappings. The pixels of the recombined mappings in the training set came from all of the one-dimensional (1D) vibrational spectroscopy of 62 (almost 80% of all 77 patients) patients at specific bands. Compared with the performance of the CNN-CAD system based on the 1D vibrational spectroscopy, we found that the mean diagnostic accuracy of the recombined MIR spectroscopic mappings at peaks of 2917 cm-1 , 1539 cm-1 and 1234 cm-1 on the test set performed higher and the model also had more stable patterns. This research demonstrates that two-dimensional MIR mapping at a single frequency can be used by the CNN-CAD system for diagnosis and the research also gives a prompt that the mapping collection process can be replaced by a single-frequency IR imaging system, which is cheaper and more portable than a Fourier transform infrared microscopy and thus may be widely utilized in hospitals to provide meaningful assistance for pathologists in clinics.
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Affiliation(s)
- Wenyu Peng
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science, Xi'an Jiaotong University, Xi'an, China
- Innovation Laboratory of Terahertz Biophysics, National Innovation Institute of Defense Technology, Beijing, China
| | - Shuo Chen
- Innovation Laboratory of Terahertz Biophysics, National Innovation Institute of Defense Technology, Beijing, China
| | - Dongsheng Kong
- Department of Neurosurgery, Chinese People's Liberation Army (PLA) General Hospital, Beijing, China
| | - Xiaojie Zhou
- National Facility for Protein Science in Shanghai, Shanghai Advanced Research Institute, Chinese Academy of Science, Shanghai, China
| | - Xiaoyun Lu
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science, Xi'an Jiaotong University, Xi'an, China
| | - Chao Chang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science, Xi'an Jiaotong University, Xi'an, China
- Innovation Laboratory of Terahertz Biophysics, National Innovation Institute of Defense Technology, Beijing, China
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AI in Forensic Medicine for the Practicing Doctor. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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7
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Wu D, Luo YW, Zhang J, Luo B, Zhang K, Yu K, Liu RN, Lin HC, Wei X, Wang ZY, Huang P. Fourier-transform infrared microspectroscopy of pulmonary edema fluid for postmortem diagnosis of diabetic ketoacidosis. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 258:119882. [PMID: 33964633 DOI: 10.1016/j.saa.2021.119882] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Revised: 04/14/2021] [Accepted: 04/23/2021] [Indexed: 06/12/2023]
Abstract
Determination of the cause of death for diabetic ketoacidosis (DKA), a common and fatal acute complication of diabetes mellitus, is a challenging forensic task owing to the lack of characteristic morphological findings at autopsy. In this study, Fourier-transform infrared (FTIR) microspectroscopy coupled with chemometrics was employed to characterize biochemical differences in pulmonary edema fluid from different causes of death to supplement conventional methods and provide an efficient postmortem diagnosis of DKA. With this aim, FTIR spectra in three different situations (DKA-caused death, other causes of death with diabetes history, and other causes of death without diabetes history) were measured. The results of principal component analysis indicated different spectral profiles between these three groups, which mainly exhibited variations in proteins. Subsequently, two binary classification models were established using an algorithm of partial least squares discriminant analysis (PLS-DA) to determine whether decedents had diabetes and whether the diabetic patients died from DKA. Satisfactory prediction results of PLS-DA models demonstrated good differentiation among these three groups. Therefore, it is feasible to make a postmortem diagnosis of DKA and detect diabetes history via FTIR microspectroscopic analysis of the pulmonary edema fluid.
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Affiliation(s)
- Di Wu
- Department of Forensic Pathology, College of Forensic Medicine, Xi'an Jiaotong University, No. 76 West Yanta Rd., Xi'an, Shaanxi 710061, China; Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Ministry of Justice, No. 1347 West Guangfu Rd., Shanghai 200063, China
| | - Yi-Wen Luo
- Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Ministry of Justice, No. 1347 West Guangfu Rd., Shanghai 200063, China
| | - Ji Zhang
- Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Ministry of Justice, No. 1347 West Guangfu Rd., Shanghai 200063, China
| | - Bin Luo
- Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-Sen University, No. 76 Zhongshan 2nd Rd., Guangzhou 510080, China
| | - Kai Zhang
- Department of Forensic Pathology, College of Forensic Medicine, Xi'an Jiaotong University, No. 76 West Yanta Rd., Xi'an, Shaanxi 710061, China
| | - Kai Yu
- Department of Forensic Pathology, College of Forensic Medicine, Xi'an Jiaotong University, No. 76 West Yanta Rd., Xi'an, Shaanxi 710061, China
| | - Rui-Na Liu
- Department of Forensic Pathology, College of Forensic Medicine, Xi'an Jiaotong University, No. 76 West Yanta Rd., Xi'an, Shaanxi 710061, China
| | - Han-Cheng Lin
- Department of Forensic Pathology, College of Forensic Medicine, Xi'an Jiaotong University, No. 76 West Yanta Rd., Xi'an, Shaanxi 710061, China
| | - Xin Wei
- Department of Forensic Pathology, College of Forensic Medicine, Xi'an Jiaotong University, No. 76 West Yanta Rd., Xi'an, Shaanxi 710061, China
| | - Zhen-Yuan Wang
- Department of Forensic Pathology, College of Forensic Medicine, Xi'an Jiaotong University, No. 76 West Yanta Rd., Xi'an, Shaanxi 710061, China.
| | - Ping Huang
- Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Ministry of Justice, No. 1347 West Guangfu Rd., Shanghai 200063, China.
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AI in Forensic Medicine for the Practicing Doctor. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_221-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Korb E, Bağcıoğlu M, Garner-Spitzer E, Wiedermann U, Ehling-Schulz M, Schabussova I. Machine Learning-Empowered FTIR Spectroscopy Serum Analysis Stratifies Healthy, Allergic, and SIT-Treated Mice and Humans. Biomolecules 2020; 10:biom10071058. [PMID: 32708591 PMCID: PMC7408032 DOI: 10.3390/biom10071058] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Revised: 07/13/2020] [Accepted: 07/14/2020] [Indexed: 12/17/2022] Open
Abstract
The unabated global increase of allergic patients leads to an unmet need for rapid and inexpensive tools for the diagnosis of allergies and for monitoring the outcome of allergen-specific immunotherapy (SIT). In this proof-of-concept study, we investigated the potential of Fourier-Transform Infrared (FTIR) spectroscopy, a high-resolution and cost-efficient biophotonic method with high throughput capacities, to detect characteristic alterations in serum samples of healthy, allergic, and SIT-treated mice and humans. To this end, we used experimental models of ovalbumin (OVA)-induced allergic airway inflammation and allergen-specific tolerance induction in BALB/c mice. Serum collected before and at the end of the experiment was subjected to FTIR spectroscopy. As shown by our study, FTIR spectroscopy, combined with deep learning, can discriminate serum from healthy, allergic, and tolerized mice, which correlated with immunological data. Furthermore, to test the suitability of this biophotonic method for clinical diagnostics, serum samples from human patients were analyzed by FTIR spectroscopy. In line with the results from the mouse models, machine learning-assisted FTIR spectroscopy allowed to discriminate sera obtained from healthy, allergic, and SIT-treated humans, thereby demonstrating its potential for rapid diagnosis of allergy and clinical therapeutic monitoring of allergic patients.
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Affiliation(s)
- Elke Korb
- Institute of Specific Prophylaxis and Tropical Medicine, Medical University of Vienna, 1090 Vienna, Austria; (E.K.); (E.G.-S.); (U.W.)
| | - Murat Bağcıoğlu
- Institute of Microbiology, Department of Pathobiology, University of Veterinary Medicine, 1210 Vienna, Austria;
| | - Erika Garner-Spitzer
- Institute of Specific Prophylaxis and Tropical Medicine, Medical University of Vienna, 1090 Vienna, Austria; (E.K.); (E.G.-S.); (U.W.)
| | - Ursula Wiedermann
- Institute of Specific Prophylaxis and Tropical Medicine, Medical University of Vienna, 1090 Vienna, Austria; (E.K.); (E.G.-S.); (U.W.)
| | - Monika Ehling-Schulz
- Institute of Microbiology, Department of Pathobiology, University of Veterinary Medicine, 1210 Vienna, Austria;
- Correspondence: (M.E.-S.); (I.S.); Tel.: +43-1-25077-2460 (M.E.-S.); +43-1-40160-38250 (I.S.)
| | - Irma Schabussova
- Institute of Specific Prophylaxis and Tropical Medicine, Medical University of Vienna, 1090 Vienna, Austria; (E.K.); (E.G.-S.); (U.W.)
- Correspondence: (M.E.-S.); (I.S.); Tel.: +43-1-25077-2460 (M.E.-S.); +43-1-40160-38250 (I.S.)
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