A diagnostic strategy for pulmonary fat embolism based on routine H&E staining using computational pathology

Sakashita M, Sakashita S, Sakata A et al (2017) An autopsy case of non-traumatic fat embolism syndrome. Pathol Int 67(9):477–482

Article  PubMed  Google Scholar 

Castiglioni C, Carminati A, Fracasso T (2023) Fat embolism after intraosseous catheters in pediatric forensic autopsies. Int J Legal Med 137(3):787–791

Article  CAS  PubMed  Google Scholar 

Meng Y, Zhang M, Ling H et al (2020) Nontraumatic multiple-organ fat embolism: an autopsy case and review of literature. Am J Forensic Med Pathol 41(2):131–134

Article  PubMed  Google Scholar 

Bailey K, Wesley J, Adeyinka A et al (2019) Integrating fat embolism syndrome scoring indices in sickle cell disease: a practice management review. J Intensive Care Med 34(10):797–804

Article  PubMed  Google Scholar 

Celik SU, Kocaay AF, Sevim Y et al (2015) Renal angiomyolipoma with caval extension and pulmonary fat embolism: a case report. Medicine 94(31):e1078

Rosen JM, Braman SS, Hasan FM et al (1986) Nontraumatic fat embolization: a rare cause of new pulmonary infiltrates in an immunocompromised patient. Am Rev Respir Dis 134(4):805–808

CAS  PubMed  Google Scholar 

Schulz F, Trübner K, Hilderbrand E (1996) Fatal fat embolism in acute hepatic necrosis with associated fatty liver. Am J Forensic Med Pathol 17(3):264–268

Article  CAS  PubMed  Google Scholar 

Neri M, Riezzo I, Dambrosio M et al (2010) CD61 and fibrinogen immunohistochemical study to improve the post-mortem diagnosis in a fat embolism syndrome clinically demonstrated by transesophageal echocardiography. Forensic Sci Int 202(1-3):e13–e17

Article  CAS  PubMed  Google Scholar 

Milroy CM, Parai JL (2019) Fat embolism, fat embolism syndrome and the autopsy. Acad Forensic Pathol 9(3-4):136–154

Article  PubMed  Google Scholar 

Falzi G, Henn R, Spann W (1964) Über pulmonale Fettembolien nach Traumen mit verschieden langer Überlebenszeit. Munch Med Wochenschr 106:978–981

CAS  PubMed  Google Scholar 

Mason K (1962) Aviation accident pathology: a study of fatalities. Butterworth, p 358

Mudd KL, Hunt A, Matherly RC et al (2000) Analysis of pulmonary fat embolism in blunt force fatalities. J Trauma Acute Care Surg 48(4):711–715

Article  CAS  Google Scholar 

Sevitt S (1977) The significance and pathology of fat embolism. Ann Clin Res 9:173–180

CAS  PubMed  Google Scholar 

Turillazzi E, Riezzo I, Neri M et al (2008) The diagnosis of fatal pulmonary fat embolism using quantitative morphometry and confocal laser scanning microscopy. Pathol Res Pract 204(4):259–266

Article  PubMed  Google Scholar 

Arregui M, Fernández A, Paz-Sánchez Y et al (2020) Comparison of three histological techniques for fat emboli detection in lung cetacean’s tissue. Sci Rep 10(1):8251

Article  CAS  PubMed  PubMed Central  Google Scholar 

Moore NP, Boogaard PJ, Bremer S et al (2013) Guidance on classification for reproductive toxicity under the globally harmonized system of classification and labelling of chemicals (GHS). Crit Rev Toxicol 43(10):850–891

Article  CAS  PubMed  Google Scholar 

Hosseini MS, Bejnordi BE, Trinh VQH et al (2023) Computational pathology: a survey review and the way forward. arXiv:2304.05482[eess.IV]

Cui M, Zhang DY (2021) Artificial intelligence and computational pathology. Lab Investig 101(4):412–422

Article  PubMed  Google Scholar 

Campanella G, Hanna MG, Geneslaw L et al (2019) Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nat Med 25(8):1301–1309

Article  CAS  PubMed  PubMed Central  Google Scholar 

Chen D, Fu M, Chi L et al (2022) Prognostic and predictive value of a pathomics signature in gastric cancer. Nat Commun 13(1):6903

Article  CAS  PubMed  PubMed Central  Google Scholar 

Kather JN, Pearson AT, Halama N et al (2019) Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer. Nat Med 25(7):1054–1056

Article  CAS  PubMed  PubMed Central  Google Scholar 

Cao R, Yang F, Ma SC et al (2020) Development and interpretation of a pathomics-based model for the prediction of microsatellite instability in colorectal cancer. Theranostics 10(24):11080

Article  CAS  PubMed  PubMed Central  Google Scholar 

Chen S, Jiang L, Zheng X et al (2021) Clinical use of machine learning-based pathomics signature for diagnosis and survival prediction of bladder cancer. Cancer Sci 112(7):2905–2914

Article  CAS  PubMed  PubMed Central  Google Scholar 

Jarkman S, Karlberg M, Pocevičiūtė M et al (2022) Generalization of deep learning in digital pathology: experience in breast cancer metastasis detection. Cancers 14(21):5424

Article  PubMed  PubMed Central  Google Scholar 

Wang X, Chen H, Gan C et al (2019) Weakly supervised deep learning for whole slide lung cancer image analysis. IEEE Trans Cybern 50(9):3950–3962

Article  PubMed  Google Scholar 

Choi HR, Siadari TS, Kim JE et al (2022) Automatic detection of teeth and dental treatment patterns on dental panoramic radiographs using deep neural networks. Forensic Sci Res 7(3):456–466

Article  PubMed  PubMed Central  Google Scholar 

Cao Y, Ma Y, Yang X et al (2022) Use of deep learning in forensic sex estimation of virtual pelvic models from the Han population. Forensic Sci Res 7(3):540–549

Article  PubMed  PubMed Central  Google Scholar 

Li Y, Huang Z, Dong X et al (2019) Forensic age estimation for pelvic X-ray images using deep learning. Eur Radiol 29:2322–2329

Article  PubMed  Google Scholar 

Peng LQ, Guo Y, Wan L et al (2022) Forensic bone age estimation of adolescent pelvis X-rays based on two-stage convolutional neural network. Int J Legal Med 136(3):797–810

Article  PubMed  Google Scholar 

Bewes J, Low A, Morphett A et al (2019) Artificial intelligence for sex determination of skeletal remains: application of a deep learning artificial neural network to human skulls. J Forensic Legal Med 62:40–43

Article  Google Scholar 

Zhang J, Zhou Y, Vieira DN et al (2021) An efficient method for building a database of diatom populations for drowning site inference using a deep learning algorithm. Int J Legal Med 135:817–827

Article  PubMed  Google Scholar 

Zhou Y, Zhang J, Huang J et al (2019) Digital whole-slide image analysis for automated diatom test in forensic cases of drowning using a convolutional neural network algorithm. Forensic Sci Int 302:109922

Article  PubMed  Google Scholar 

Zhang J, Vieira DN, Cheng Q et al (2023) DiatomNet v1. 0: A novel approach for automatic diatom testing for drowning diagnosis in forensically biomedical application. Comput Methods Prog Biomed 232:107434

Article  Google Scholar 

Brinkmann B, Borgner M, von Bülow M (1976) Fat embolism of the lungs as the cause of death. Etiology, pathogenesis and reasoning. Z Rechtsmed 78:255–272

Article  CAS  PubMed  Google Scholar 

Bunai Y, Yoshimi N, Komoriya H et al (1988) An application of a quantitative analytical system for the grading of pulmonary fat embolisms. Forensic Sci Int 39(3):263–269

Article  CAS  PubMed  Google Scholar 

Busuttil A, Hanley JJ (1994) A semi-automated micro-method for the histological assessment of fat embolism. Int J Legal Med 107:90–95

Article  CAS  PubMed  Google Scholar 

Chatzaraki V, Heimer J, Thali MJ et al (2019) Approaching pulmonary fat embolism on postmortem computed tomography. Int J Legal Med 133:1879–1887

Article  PubMed  Google Scholar 

Makino Y, Kojima M, Yoshida M et al (2020) Postmortem CT and MRI findings of massive fat embolism. Int J Legal Med 134:669–678

Article  PubMed  Google Scholar 

Cheng Q, Zhu Y, Deng K et al (2022) Label-free diagnosis of pulmonary fat embolism using fourier transform infrared (FT-IR) spectroscopic imaging. Appl Spectrosc 76(3):352–360

Article  CAS  PubMed  Google Scholar 

Voisard MX, Schweitzer W, Jackowski C (2013) Pulmonary fat embolism—a prospective study within the forensic autopsy collective of the Republic of Iceland. J Forensic Sci 58:S105–S111

Article  PubMed  Google Scholar 

Janssen W (1984) Forensic histopathology. Springer-Verlag, Berlin, p 402

Book  Google Scholar 

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